MODELING AND ANALYSIS OF POWER AND ENERGY

METRICS IN NAVAL SHIP SYSTEMS

Introduction

Naval ship design is a complex system of activities, integrating the operational needs,

physical constraints, and logical connectivity of various systems into a single unified platform.

For surface combatant ships, missions as diverse as ballistic missile defense to anti-submarine

warfare determine the needs of combat systems, including sensors, processing, communications,

payload, and operational strategies. For this mission system to work, the ship must have a stable,

seaworthy hull, as well as an adequate power and energy (P&E) system.

The U.S. Navy's surface fleet is currently in a period of transition, facing challenges in the

form of recapitalization of aging ships, accelerating technological innovation, uncertainty

regarding future combat systems, and large investments in designing and building new classes

of ships. Most of today's fleet was designed in the post-Cold War era, reflecting geo-political

needs since the 1980s, such as the adoption of the Aegis combat system and the SPY-1D radar.

However, after more than 40 years of operation, these ships are nearing the end of their service

life, and the Navy needs new ships capable of supporting operations for the next 50 years.

In addition, the acceleration of technological change adds uncertainty regarding the future

needs for key combat system elements. The value of a system is determined by its ability to

remain missionally relevant at an affordable cost in an ever-evolving operational context.

Mission system technologies under development are increasing the need for new electrical

power, driving attention to additional properties such as flexibility and scalability beyond

traditional functional requirements. This is important considering the lifespan of the platform

which is designed for 30 to 40 years.

Affordability requirements also encourage the study of cost versus capability trades from

the beginning of the design process. System metrics are needed to measure performance,

provide insight into systems-of-systems (SoS) architectures, and support design decisions. With

R&D and Acquisition budget constraints and a new P&E system development schedule, a

comprehensive evaluation of the design space is required to determine the optimal solution.

Metrics based on the "ility" property provide an important tool for designers to differentiate

design options within a broad solution space.

This thesis presents findings from a literature review related to the requirements and

property relationships of "ility" in system systems, as well as methods to distinguish optimal

solutions in the design space. The study developed a hierarchy of "ility" relationships for naval

power and energy systems, resulting in a framework for outlining high-level requirements into

metrics that can help identify dominant architectures in the early stages of design. The

framework considers the physical, logical, and operational aspects of the architecture,

identifying factors that can affect a system's ability to maintain value throughout its lifecycle.

The deepening of flexibility, one of the significant "ility" properties, is explained through

five case studies with proposed metrics for power and energy systems. The study aims to

provide a repeatable process for developing metrics that are integrated into early-stage design

tools, such as the Smart Ship System Design (S3D) design environment being developed by the

Electric Ship Research and Development Consortium (ESRDC).

Naval Power and Energy Systems

The power and energy system is responsible for providing propulsion and the ship

electrical power required to perform the mission requirements of the platform. The current

surface fleet consists primarily of vessels with a P&E system architecture that separates the

propulsion and power generation functions through the implementation of special propulsion

turbines connected directly to the propeller shafts and separate ship service generators installed

to provide electrical power on distributed vessels. This type of mechanical-electric configuration

has become a profitable and cost-effective design over the past century, as the demand for

propulsion power has significantly exceeded the demand for combat system power. The DDG-

51 class, for example, has about 78 MW of dedicated propulsion power on the shaft, compared

to 9 MW of separate ship service power.

The Navy's newest class of destroyers, the DDG-1000 Zumwalt class, introduced an

alternative power and energy system architecture, the Integrated Power System (IPS), in which

all power generated on board is shared between the propulsion load demand and the distributed

electrical power demand, including the mission system load. The ship's ability to share 78 MW

of power across all platform functions is made possible by the inclusion of electric propulsion

motors, improved power distribution and power control. Power and energy systems can be

further broken down into seven basic module types, as described in the Navy's Next Generation

Integrated Power System Roadmap (Doerry, 2008):

Power Plant Module (PGM)

Engine Propulsion Module (PMM)

Power Load Module (PLM)

Power Distribution Module (PDM)

Power Conversion Module (PCM)

Energy Storage Module (ESM)

Power Control Module (PCON)

The performance characteristics of power and energy systems can be traced to the physical,

logical, and operational characteristics of the sub-module configuration. It is important to

outline the desired functional and non-functional requirements to the lowest level of

measurability, as they can often be met by a variety of architectural configurations. For example,

IPS architecture provides an increase in flexible power capacity over traditional mechanical

architectures based on the total installed power residing within the power generation module,

and is divided between the power generation and propulsion modules as in mechanical

architecture. However, alternative measures of flexibility, such as the ability to serve high-scale,

short-duration pulse load types, may be overall architecture agnostic and more dependent on the

configuration of specific sub-modules, such as energy storage modules. When comparing power

and energy system architecture alternatives, designers need to consider the capabilities of the

fully integrated system and the dependencies between the applicable modules.

1.1 Design Requirements

The U.S. Navy's ship design program is most commonly classified as a Major Defense

Acquisition Program (MDAP) within the Defense Acquisition System and is subject to the Joint

Capability Development and Integration System (JCIDS) process for Acquisitions,

Requirements, and Funding. JCIDS supports the Joint Requirements Supervisory Board's

(JROC) responsibility to validate combat capability requirements. Figure 1 illustrates the JCIDS

and Defense Acquisition process as per the 2021 JCIDS Manual, with core elements of

capability requirements development and validation, as described by the Defense Acquisition

University (DAU, 2023). High-level operational requirements, including capability gaps and

mission needs, are identified during the Capability Base Assessment (CBA), and captured in the

Initial Capability Document (ICD) for outcomes recommending material solution approval.

Alternative Analysis (AoA) compares potential material solutions based on mission-level

requirements for "operational effectiveness, conformity, and lifecycle cost" (DAU, 2023). The

results of the AoA inform the development of a draft Capability Development Document

(CDD), which consists of threshold values and objective performance for Key Performance

Parameters (KPPs) and Key System Attributes (KSAs). The draft CDD was matured during the

Technology Maturation and Risk Mitigation Phase, which corresponds to the Initial Design

Phase for the design of U.S. Navy ships.

Figure 1: JCIDS and the Defense Acquisition Process (DAU, 2023)

In Navy ship design, CDD requirements are outlined, assigned, and allocated to individual

systems within the ship's SoS, using Systems Engineering principles, to guide the design. In

addition to the CDD capability requirements, the ship's design will be subject to other design

criteria, including the Department of Defense (DoD) and Navy-specific Military Specifications

(MIL-SPEC) and Military Standards (MIL-STD). The Ship Systems Specification Document is

developed in conjunction with system and subsystem design activities to capture the total set of

requirements subject to the platform, applied down to the subsystem level. This specification

identifies the design criteria and standards, constraints, and system interfaces required to meet

the platform's performance requirements. The Vessel Specification Document is required to

support the review of the Milestone B program and is a key component of the Technical Data

Package (TDP) which represents the basic design of the vessel to be included in the Design

Detail & Construction (DD&C) Request for Proposal (RFP).

Design decisions are made at the system and subsystem level during the vessel design

process to meet end-to-end performance and cost requirements. Permutation of architectural

options within each subsystem domain creates a space for potential solutions of high magnitude

that are difficult to evaluate. Beyond the ability to meet predefined requirements and

specifications, additional performance metrics for non-functional requirements are required to

evaluate and rank design options within the trading space.

1.2 Early Stage Design

The early-stage design includes a range of engineering activities undertaken prior to the

Acquisition of Milestone B. It includes the study and analysis of trades conducted during the

Capability-Based Assessment and Alternative Analysis in the requirements development

process, as well as

as an initial system architecture effort in the period of Material Solution Analysis and

Technology Maturation and Risk Mitigation. In the language of ship design, the initial stage

design is carried out in the Concept Formulation (CF) and Initial Design (PD) Phases. Figure 2

illustrates the design phase for the notional Navy ship acquisition program. In this construction,

requirements development activities, including a cost versus capability trade study, are

conducted to define the Top-Level Requirements during the Concept Formulation phase that is

incorporated into the Draft CDD. Initial Design begins after a stable set of system requirements

are established, and the design program can demonstrate the ability to achieve them in a viable

basic ship concept. The early-stage components of PD focus on the selection of the main system

and the identification of sub-systems.

Design

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Figure 2: U.S. Navy Ship Acquisition Phases, Activities, and Products Notional

The description of the notional program in Figure 2 illustrates the use of the Set Based

Design (SBD) approach for Concept Formulation and Initial Design. (JE Page, 2022) describes

the implementation and organization of the Set-Based Design process currently being carried out

by the U.S. Navy's next-generation large surface warfare program, DDG(X). SBD relies on the

principles of concurrent engineering, decision deferral, and increased exploration of the design

space to make design decisions through the elimination process. This is achieved through the

decomposition of the SoS with the design of the domain or competence and the establishment of

a set of alternative solutions. (JE Page, 2022) explains the execution of SBD through the

following three adjacent design activities:

1. Articulate a series of every conceivable solution to the problem that has been

presented.

2. Remove from this pool a subset of all unviable solutions.

3. Remove from the remaining set all existing solutions the better

(dominating) solution.

The design of modern Navy ships relies on a mixture of computer-aided tools capable of

initially characterizing the concept of a synthesized ship and the definition of a particular

system with high precision Architecture. For the Navy's power and energy systems, the ESRDC

Intelligent Ship System Design is a tool developed by the U.S. Navy to define, analyze, and

understand the performance of power and energy flows in distributed systems. This allows

designers to quickly characterize the physical implications of the notional power and energy

system architecture in terms of weight, volume, and location of associated components during

early-stage design activities. The logical and physical connectivity between system components

is defined across a variety of disciplines, including electrical, mechanical, and plumbing

subsystems. Today, S3D is used to analyze energy flows across all subsystems and components

to verify power supply, demand, and distribution requirements are being met within a larger

system. The ability to incorporate additional performance metrics and non-functional

requirements in such a device will provide greater insight to system designers in making design

decisions within a trading space set out based on feasibility and dominance.

2Literature Review

Literature reviews are conducted to survey existing knowledge bodies related to

"ilities" in complex system design. The design community is found to use the term "ility" with a

variety of similar definitions, as summarized in Section 2.1. This research was conducted at the

beginning of the thesis process to provide context on the state of the published work related to

the utilization of ilities and design metrics in various SoS engineering processes and to identify

priority design focus areas within the specific discipline of naval power and energy (P&E)

systems. Two initial hypotheses were formulated for the structuring of the research, with their

intended application in the framework of a new early-stage P&E design. The first is that a

relationship exists between individual diseases in such a way that the optimization of one

person may have a positive or negative impact that coincides with another. The second is that,

in complex system-of-system designs, the lowest level of the system definition is the selection

of design variables that are combined to form the metrics used to measure illiteracy.

The following review documents the state of practice and implementation as published

through various professional and academic forums. Several consistent themes were found related

to the interconnectivity of individual diseases and the general way in which they were prioritized

to improve the value function of the system. Various methods for analyzing performance and

cost value when comparing alternative architecture decisions are captured below in three

categories: Caus-Effect Mapping, Differential Analysis, and Scenario-Based Evaluation. A

separate line of research is also discussed, which outlines systems based on spatial, functional,

and

temporal characteristics. Finally, an additional focus is placed on the "flexibility" of emerging and

how it relates to the characteristics of the system that are prioritized more frequently.

2.1 Disease

Starting with a broad exploration of ilities for complex system systems, some common

themes and definitions are found throughout the published material reviewed. The primary

objective of defining a disease centers on the maintenance of system values over time. These

needs arise from the identified differences between the functional requirements used to define

The goals and weaknesses of the current system are used to measure the system's ability to

respond to change. Temporal aspects of change are prevalent throughout the literature,

including lifecycle performance and value discussions. However, there seems to be conflicting

terminology used to articulate this goal. One common conflict is the overlap between the

definitions of ilities and metrics.

(Ricci, Fitzgerald, Ross, & Rhodes, 2014) defines system-of-system ility with a lifecycle

value property that allows the system to "maintain value delivery over time by responding to

exogenous changes in the operational environment." They suggest the temporal aspect of ility,

where the given value is not realized until after the system is operational. This aspect differs

from the traditional functional requirements, which are established to determine the initial

primary value of the system. The authors outline the System of System Architecting with Ilities

(SAI) method, discussed in Section 2.3.2, which provides examples of a set of evaluation

metrics to compare design alternatives that include "operability" in addition to quantitative

criteria such as cost and multiple uses. They went on to explain the need to evaluate SoS

architecture alternatives against a variety of metrics, including "value metrics", such as attributes

and costs, and "ility metrics", which are determined by evaluating the impact of shifts in the

context of a system or requirement from one moment to another.

(Chin, Yau, Kok Wah, & Khiang, 2013) describes ilities as "attributes that characterize a

system's ability to respond to change, both predictable and unpredictable." They are

presented as a non-functional requirement necessary to ensure the delivery of value throughout

the life cycle of the system system. The authors make a point to acknowledge the cost of

implementing ilities and the potential conflicts between certain ilities that will require decision-

making tradeoffs in architecture. These considerations emphasize the need for a balanced design

approach that takes into account the broader context and requirements of the system.

(Doerry & Amy, 2019) discusses the key requirements for surface combat power and

propulsion system design. The authors present a mixed discussion of the three prioritized

metrics (size, weight, cost) and disease (flexibility and survivability) that greatly influence the

metrics.

They identified the drivers of requirements implementation as a mixture of metrics and ilities:

the projected future mission system load, which is a metric, and the system survival criteria,

including CONOPS, which are ility.

(Guariniello & DeLaurentis, 2014) calls for the important role that metrics play in the

definition of ilities as the impact of functional and developmental dependencies "on metrics that

characterize the global nature of a system over its lifetime." They suggest that metrics represent

capabilities at the individual system level but do not directly translate to system-level systems.

Higher level metrics at the SoS level are called ilities.

2.1.1 Hierarki Ility

Various hierarchies and decompositions of ility are found throughout the literature to better

define ility as a measurable attribute of the system. Certain diseases, such as

Survivability, identified as having strong roots in the requirements of traditional systems, while

others, such as evolutionary ability, are less easily defined. This decomposition suggests that

there is a clear relationship between the individual's ility and the different ways to approach the

attributes of the desired system. Many of the diseases discussed extensively in literature reviews

are desirable attributes of the naval P&E system.

Research by (deWeck, Ross, & Rhodes, 2012) to unravel the relationship between the life

cycle properties of systems resulted in a proposal of "hierarchical means-end relationships

among ilities." The authors first acknowledge that certain diseases, such as safety and reliability,

have been historically prevalent in system design systems, although they are considered

secondary requirements to requirements that can be quantitatively tested by traditional processes.

Through the authors' own experience and subject matter expertise, they developed a list of

twenty principles.

They conducted a research survey to collect data on the frequency and occurrence of concurrent

citations, to develop a model of the potential relationship between ilities, shown below in

Figure 3.

Figure 3: The emergence of inequalities in literature reviews with implicit dependence (deWeck,

Ross, & Rhodes, 2012)

Based on the prevalence and concurrent occurrence of a particular ility, the authors

propose a hierarchical structure of ility means, in which one ility can serve as a means to

achieve another or goal. The authors recommend considering weaknesses in terms of system

nature versus capabilities in one particular area. The authors identified a hierarchy of potential

means by conducting a preliminary exercise with a group of researchers with experience

working with ilities and were given a general set of definitions for twenty ilities. Of interest to

this thesis literature review, further exploration into the initial set of twenty ilities identifies

survivability, change, and robustness as the "ends" at the top of the hierarchy, flexibility in the

middle of hierarchical relationships, and modularity and interoperability as samples of

"means" at the bottom of the hierarchy. Figure 7 in Section 2.1.1 will be further

define these interesting halas and show their relationship in the context of the Navy's power and

energy systems.

(Chin, Yau, Kok Wah, & Khiang, 2013) presents a framework for managing system

systems through the identification of ility hierarchies. The authors propose that two specific

diseases, robustness and evolution, are essential for maintaining the ability of SoS to meet

basic operational requirements and future unforeseen requirements later in the system's lifetime.

Because SoS architectural requirements are capability-driven, they are typically evaluated

against predefined missions and scenarios. This condition ensures that the system can meet

performance requirements once "operational contingencies" are introduced to value-driven

scenarios. In their system hierarchy, the authors outline robustness to include survival and

sustainability. Their framework continues to identify flexibility and interoperability as the keys

to making it possible.

(Richards, Ross, Hastings, & Rhodes, 2009), in his discussion of various perspectives for

defining resilience, introduces flexibility and robustness ilities as "the properties of temporal

systems that determine the extent to which a system can maintain or even improve function in

the presence of change." The authors emphasize that ility is dynamic, based on changes in the

needs of the system, the system itself, or the context of the system, as depicted in Figure 4.

Survivability is defined as "the ability of a system to minimize the impact of a limited duration

disruption on value delivery, which is achieved through (I) reduction in the likelihood or

magnitude of the impairment, (II) satisfaction of a minimally acceptable level of value delivery

during and after the disruption, and/or (III) timely recovery." The authors distinguish resilience

from resilience, although both are "measures of a system's ability to reduce the sensitivity of

their output to environmental changes." In this way, resilience is considered a case of resilience,

in which the system must mitigate limited changes in the context or impulse events. An eight-

phase multi-attribute commerce space exploration for the survival process is presented, with

measurable metrics of the end of the time-weighted average utility loss and threshold

availability.

Figure 4: Context-density configuration requirements space (Richards, Ross, Hastings, &

Rhodes, 2009)

(Doerry & Moniri, 2013) cites the need to improve the survivability and reliability of naval

and energy power systems as systems evolve from traditional low-voltage systems to meet the

demands of new high-power combat systems.

2.2 Design Metrics

To evaluate alternative power and energy system architectures, (Smart, et al., 2017)

identified the need for metrics to differentiate between design alternatives. The study explores

the impact of new technologies and alternative topologies. Several metrics are available in the

designated design tool, S3D, including weight, volume, number of components, and calculation

of fuel load range. The authors propose several future areas for development in early-stage

design tools, including a variety of performance metrics.

(Toshon, et al., 2017) presents a method for executing Set-Based Design in on-board power

systems using the metrics available in the early-stage design tools. The authors discussed the

topology of the 5 MW Modular Multilevel Converter (MMC) and identified related metrics

related to thermal facility selection, power density, and cabinet size as selection criteria for the

preferred architecture.

(McNabb, et al., 2019) presents a case study to measure the architectural value of a

particular electric vessel within a broader trading space using a methodical approach to apply

architectural variations in the base model in a robust design simulation environment. The

example presents the basic performance metrics measured, displacement, speed, and range

variation.

(Chalfant, Hanthorn, & Chryssostomidis, 2012) discusses some of the metrics commonly

used in the design analysis of early-stage P&E systems of alternatives, such as weight, volume,

fuel efficiency, and losses (based on location, size, and loading). They present additional

survival metrics, which depend on input data from the load, defined services, connectors, and

their associated locations. These metrics and the underlying variables are identified in existing

design tools, as they are necessary to define the physical architecture and functional

capabilities of the system.

2.2.1 Measures of Effectiveness

At the beginning of this literature review, the initial theory was that there was a relationship

between the method used to determine the system's utility, the overall effectiveness measure

(OMOE), and illiteracy. A common relationship found between different ways of setting design

priorities is to use scenario-based evaluation to derive alternative value of the system.

(Berrow, Parsons, Shane, Kara, & Brown, 2022) presents a method for performing mission

capability modeling, which requires an interface between logical-operational-physical

architectures. This analysis relies on the determination of Measures of Effectiveness (MOE),

Measures of Performance (MOP), and Design Reference Missions (DRM). The authors define

MOE as the metric assigned to each mission that is evaluated to quantitatively or qualitatively

measure how well a task is completed and MOP as a metric to characterize how well a task is

performed by the capabilities that make it possible. DRM is a specific set of scenarios and

operational requirements that are used to determine the overall measure of effectiveness when

comparing early-stage design concept architectures.

(Mierzwicki & Brown, 2004) identified a two-phase approach to conducting a risk

assessment for comparison with cost and performance in the evaluation of multi-attribute

designs. The Overall Risk Measure (OMOR), depending on the design variables, is proposed for

the exploration of the concept at an early stage. Variable risk is based on expert opinion and is

related to performance, cost, and schedule. The accompanying quantitative Overall Effectiveness

measure is determined based on the design variables and the specified mission thresholds and

objectives. The probability of success is referred to as robustness. The second phase is presented

for the later stages of concept development when a higher fidelity evaluation is required,

utilizing the risk probability distribution function.

(Bottero & Gualeni, 2022) discusses the application of systems thinking in the design

process of traditional naval architecture. They propose a capability-based approach, in which the

ship's functions (representative focus on the required systems) are outlined from Key

Performance Parameters (ship functions), High-Level MOPs (ship-level), Low-Level MOPS

(system-level), and TPM (parameters).

(Goodfriend & Brown, 2018) proposes an Overall Vulnerability Size (OMOV) that is

incorporated into an existing design space evaluation tool called the Multi-Objective Genetic

Optimization (MOGO) framework. OMOV relies on the identification and prioritization of vital

components (VCs), the location of VCs within the geometry of the gastric compartment, and

probabilistic vulnerability analysis. Statistical component factors in the type of threat, the

location of the potential hit, the probability of killing the VC in the event of a hit, and the

probability of killing the system given the aggregate assessment of equipment deactivation after

damage.

2.3 Space Exploration Design Methods

A repetitive set of terminology is found throughout the literature review on systemic

diseases. To build on the same colloquial language, the various approaches to implementing

ilities to maintain system value generally refer to "options in design", "distractions", and

"Preparation." To design unity and to maintain system values, the term disruption is used to

characterize the influence on a system that requires change. Design options are the ability

inherent in design to accommodate future changes. They give the system owner the option or

right to implement changes later in the system's lifetime once a need has been identified (the

right to take action). Preparation refers to specific architectural features or capabilities that are

planned into the design to enable the system to respond positively to disruptions (maintaining

value, cost value, effectiveness).

(Ricci, Fitzgerald, Ross, & Rhodes, 2014) defines disruption as "an undesirable (i.e.,

imposed) change in circumstances in the design, context, or needs of system stakeholders that

can

jeopardize value delivery;" and options as "the ability to execute design decisions or features

at any point in the lifecycle that would change or prevent changes to the SoS, in response to

variations in the operational context and stakeholder preferences." The author further breaks

down the options into change options, which allow changes in the design in response to

disturbances, and resistance options, which increase the system's ability to withstand the effects

of changes from disturbances.

(Mekdeci, Ross, Rhodes, & Hastings, 2012) decomposes disorders into disorders and

disorders in their "Taxonomy of Disorders of Disorders". Disorders and disorders are defined as

a type of disorder, with the difference that the disorder occurs over a period of time, but the

disorder is almost instantaneous.

The following sections discuss some of the current methods used to characterize

architectural value or alternative system decisions in early-stage design. The end-to-end design

process relies on a large number of architectural decisions that require data and an

understanding of the trade-offs between design alternatives. There are several effective methods

for conducting these alternative comparisons, including exploration of design spaces and set-

based designs. In addition to identifying system value in the face of changing contexts and

requirements, the various methods discussed below rely on the designer's ability to define

system boundaries.

2.3.1 Causal Mapping

(Hein, 2022) presents a framework for identifying and characterizing flexibility in design

using Causal Mapping (CEM) or cause-and-effect chains. This approach uses key distractions,

preparations, and option elements to provide designers with insight into the value of flexibility.

Hein suggests proper characterization of the disorder such as risk assessment, which uses the

elements of likelihood and severity to describe the potential event. Within the framework,

preparations are tied directly back to the distractions they want to reduce and

characterized by cost and complexity. The authors define preparation as "something that can be

done in the present that mitigates, eliminates, or allows the option to reduce or eliminate the

negative effects of future events." This paper identifies six essential principles for identifying

appropriate disturbances, including the definition of system ownership (affected systems),

organization, and the concept of immediate external disturbances (IEPs). IEPs are useful in

determining the scope of direct impact on the system of interest. They can be categorized as

changes in the mission, changes in the projected operational environment, changes in

capabilities, or non-technical decisions. No metrics are presented to prioritize or determine the

value of each preparation, including any relationship back to possibility.

(Mekdeci, Ross, Rhodes, & Hastings, 2012) outlines the process for performing cause-and-

effect mapping to obtain unknown-unknown disturbances effectively. In its application, cause-

and-effect mapping considers the context of the system, the Concept of Operation (CONOPS),

and the chain reaction of the disorder so that the system architect can categorize and address the

identified effects. The resulting taxonomy defines the categories of value loss associated with the

type of disorder: loss of capability, degradation of capability, change in mode of operation,

increased cost, or change in stakeholder expectations. The discussion of value robustness is

presented in the context of system survival, broken down into aspects of prevention, mitigation,

and recovery.

2.3.2 Differential Analysis

(Ricci, Fitzgerald, Ross, & Rhodes, 2014) Present the System-of-System Architecting with

Ilities method to measure the tradeoffs between design options required to target specific ilities,

resulting in customized system requirements. The eight-step process is laid out in detail, starting

from the initial definition of system requirements and value determination, the identification of

potential faults to obtain the desired system faults, and the creation of alternative architectures

with ilities in mind. Identify the disruptions organized into fixed time periods, called epochs, to

categorize the context of the system and how its changes will affect the SoS value.

In SAI, desired ilities are identified after surveying potential SoS disruptions and

stakeholder needs. Evaluation of different options describes the desired problem, providing a

contingent value if a disturbance materializes. The logical flow of research is a method for

uncovering diseases and metrics.

(Guariniello & DeLaurentis, 2014) shows that some sets of ilities have competing interests

and effects on the design, architecture, and evolution of system systems. They propose a

framework to conduct a trade-off analysis that combines elements of functional dependency

network analysis (FDNA) and development dependency network analysis (DDNA) to assess the

impact of both types of dependency on ilitas. The disease is measured in terms of operation over

time, as assessed against various developmental and disorder scenarios.

An important utility of this analysis is the ability to model and account for partial capabilities in

the SoS development process. Robustness is assessed in mission scenarios when some loss of

capability has occurred, robustness is considered when disruption occurs, and partial capability

can be restored due to system interoperability. Flexibility is evaluated in the context of the

development cycle, which requires mission coverage from other connected systems within the

SoS.

(McNabb, et al., 2019) presented the Technology Identification, Evaluation, and Selection

(TIES) methodology to identify system sacrifices and assess designs against "Merit Numbers."

In this methodology, technology evaluation relies on the simulation environment to determine

the impact of probabilistic design parameters. Through a discussion of case studies to measure

the architectural value of a particular electric vessel within a broader trading space, the authors

outline the Technique for Preferred Order (TOPSIS) as a means to conduct Multi-Attribute

Decision Making (MADM), which is a weighted means of identifying the best or worst designs

in a given trading space.

2.3.3 Scenario-Based Evaluation

(Chalfant, Hanthorn, & Chryssostomidis, 2012) presents a method to analyze the topology

of the electric ship distribution system based on two ways of assessing the survival of the

system. This approach involves determining the priority rating of serviceable loads and specified

services, connectors, and locations. The first score determines the overall ability to provide and

distribute power after a breakdown based on the weighted priority amount of the remaining load

and the amount of power or capacity of other resources, such as cooling capacity, delivered to

that load. This approach requires establishing a damage case scenario or a series of damage

cases based on the assumption of the explosion profile. These cases can be explicitly regulated

or stochastic-defined. The second metric is used to characterize the severity of the damage by

identifying the highest priority load that cannot be filled, following the same analysis method for

the first metric.

(Chalfant & Chryssostomidis, 2011) presents a relatively simple application of the

operational profile for a ship's powertrain and energy system based on the percentage of time per

year the platform spends on each combination of speed and electrical load. This data, combined

with the characteristics of the system components for propulsion and power generation, makes it

possible to calculate the fuel consumption profile based on conditions.

(Chin, Yau, Kok Wah, & Khiang, 2013) presents an example of a system resilience

evaluation by defining a series of scenarios to test the ability of the SoS to meet its operational

requirements, with the analysis presented as a Measure of Effectiveness

(Cramer, Sudhoff, & Zivi, 2007) defines a method for determining the continuity of service

metrics to predict worst-case scenarios for the survival of layered systems, such as power and

energy systems. This technique involves the definition of "event" (cross-product of the external

environment, IEP configuration, and possible interference) and "operation-

metric" (continuity of service for vital loads given the requirements of time-dependent

scenarios).

(Stevens, Opilia, Cramer, & Zivi, 2015) presents a method for establishing operational

sketches to assess electric ship power and energy systems. The sketch is defined based on the

sequence of stochastic load modeling under cruising and combat conditions and operational

propulsion scenarios over a given period. Implementing this method requires a notional or basic

power system architecture, including propulsion, power generation, complete load sets

- Integrated load parameters (max/min power rating, ramp rate, pulse width, repeat), and spatial

(zone) settings. Stochastic modeling of various mission load power demands is presented for

pulse load number, unit power level, and pulse length. This method makes it possible to

evaluate the architecture and topology of different high-level systems.

(Sabah, Ojo, & Cramer, 2021) describes an operability-based performance metric as the

ability of a system to work in a single scenario, given combat damage, unique load profiles,

cyber interference, and others. The authors emphasize the unique demand profile of electric

warships for "dynamic" capability, to perform relatively short-duration missions as a reason to

support a shift in focus from load-centric operations (linear power flow) in early-stage design to

mission-centric (nonlinear relationships between resources and mission effectiveness). The

authors present an example of an evaluation of three system configurations (energy storage

differences) and expected performance across three different mission scenarios to obtain a

probability of successful performance.

In the development of a framework for assessing the flexibility of naval warships, (Doerry

& Koenig, 2017) proposed a method for defining "uncertainty vectors" to assess the

performance of flexible design trading spaces over time. The uncertainty vector is

It is configured to capture a series of scenarios at different points in the projected lifetime of the

system and contains a variety of assessment criteria related to potential changes in requirements

for combat capability and technological maturity. The state of uncertainty, or the combination

of uncertainty parameters in a vector at a given time step, can be fixed or determined

stochasticly. The proposed framework is depicted in Figure 5, showing how uncertainty space

is used to evaluate design vectors and configuration vectors.

Figure 5: Framework for assessing flexible design (Doerry & Koenig, 2017)

2.4 System View & Context

Naval power and energy systems are complex, multidimensional systems, including

architectures that perform a variety of tasks related to the generation and supply of electric

power, cooling, and mechanical utilities, among others. In the literature review, ilities are

discussed in the context of individual components of multifaceted systems and in high-level

integrated systems. The traditional design process focuses on optimizing specific system

domains depending on the requirements and priorities of the desired system. (Brefort, et al.,

2018) claim, "The growth of system complexity and interdependencies has made systems

significantly more difficult to understand and design, in part due to an increase in potential

emerging properties that only emerge after the system is completed and operational."

(Brefort, et al., 2018) presents a framework for analyzing the distributed systems of naval

ship design by outlining the characteristics of the systems into three main architectures: physical,

logical, and operational. The relationship between interconnected and interdependent systems is

discussed in terms of their spatial, functional, and temporal characteristics. The framework

intends to provide deeper insights into complex systems, such as integrated power systems, in

the early design stages. The authors present this framework with specific survivability in mind

but outlines its applicability for other desirable system characteristics. The different properties of

the three main architectures traditionally produce different types of information that require

many devices The author defines the main architecture as follows:

Physical architecture represents the spatial and physical characteristics of a system

and its environment.

Logical architecture describes the functional characteristics of the system and the

relationships between each component of the system. Logical architecture is where the

main focus is placed on the multidisciplinary nature of the system.

Operational architecture describes the temporal behavior of a system, including

human-system interaction to some extent.

Figure 6 illustrates three main architectures and their interrelated: physical solutions,

functional utilization, physical behavior, and system response. These overlapping areas

combine information from each major architecture to provide a deeper understanding of the

design space.

Figure 6: Representation of the physical-logical-operational framework for a given scenario

(Brefort, et al., 2018)

(Cramer, Sudhoff, & Zivi, 2007) introduced the Integrated Engineering Plant as a system

that provides electric power, mobility, and thermal services. The authors define a method for

determining the continuity of service metrics to predict worst-case scenarios for system survival.

A layered approach is outlined for Integrated Engineering Plants, including spatial, automation,

air conditioning, DC, seawater, and thermal layers to capture the behavior and functionality of

each component. The framework for assessing survivability involves determining "events" and

"operations" to generate metrics, including average operation and minimum system

dependencies.

(Jansen, et al., 2020), in their discussion of the early-stage ship design vulnerability

assessment approach, differentiated between two types of system models for distributed system

design: a physically oriented ship perspective and an operationally-oriented systems

perspective. This perspective provides a logical, operational, and physical description of a

distributed system. The systems perspective defines the topology of interrelated elements and

focuses on values such as capacity, flow, and thermodynamics (load balancing). The topology

is defined by vertices and edges that represent the main elements and their logical relationships.

The ship perspective focuses on the physical integration within the ship, including system

routing (balancing mass and volume). The methods for assessing vulnerability are organized

based on phase and design perspective: max-flow-between-hubs (energy flow, deterministic,

large # nodes), Markov (state/transition/capacity, probabilistic, small # nodes), and hurt-state-

percolation (damage cases/scenarios, deterministic, large # nodes).

2.5 Ility - Flexibility

Flexibility was found to be the dominant obstacle considered throughout the literature review.

As discussed in Section 2.1, flexibility is often presented along with the classic weaknesses of

survival and security as a mechanism to easily enable system changes in response to different

types of disruptions. In the community of naval power and energy systems, the desire for system

flexibility is clear; However, only one accepted approach to implementation currently exists.

Unlike survivability, where industry, government, and Navy-specific guidance has been issued

to define system requirements, flexibility is still in the early stages of definition and

implementation. This is partly due to the wide scope of system requirements and attributes that

are generally categorized as flexibility. Where the definition of survival is widely accepted as

outlined into vulnerability, vulnerability, and recovery, the literature on flexibility ranges from

intrinsic design properties to tangible options for stakeholder value.

(Chin, Yau, Kok Wah, & Khiang, 2013) defines flexibility as "the degree of ease with which

an SoS can make changes, in response to external or internal changes, in order to maintain the

effectiveness of its mission." They suggest that there are two different types of flexibility –

operational: the ability to transition between different modes of operation, and design: a design

attribute that allows the system to incorporate changes more easily. Agility, adaptability, and

scalability are considered part of flexibility.

(Hein, 2022) defines flexibility as "a measure of a ship's ability to scale up quickly and

cheaply to respond efficiently to known or unknown disturbances." His thesis develops a

framework for identifying and characterizing flexibility in design through cause-and-effect

mapping.

(Doerry, 2014) identifies eight methods for global ship flexibility and how electric power

distribution systems should be considered in each approach. This flexibility approach includes

the arrangement of physical onboard equipment to align with hull and electrical zone features,

longitudinal electrical distribution bus sizing, power cable sizing, use of interface standards for

supporting equipment, use of Integrated Power Node Centers (IPNC) to convert power for end

users, Electronic Modular Enclosures to isolate commercial equipment and provide power and

conversion conditions, and incorporation of energy storage methods. Doerry specifically

highlighted the importance of flexibility in electrical distribution systems to serve future electric

weapon systems with much higher power ratings and load-type demands and proposed several

interfaces to be developed, including the required power

type, amount of power required, ramp level, power quality, service quality requirements, and

monitoring and control conditions. The Traditional Service Life Allowance (SLA) is discussed

from the perspective of the Interface Control Document. The authors suggest that these

documents need to define the explicit intent of a specific SLA.

(Doerry & Koenig, 2017) proposes a framework to identify what type and amount of

flexibility will "enhance the ship's ability to reconfigure quickly and economically in the future."

They recognize the temporal aspect of the necessary change as temporary mission capabilities or

permanent reconfigurations. Their paper discusses modularity, adaptability, and flexibility as

pertaining to specific types of technologies that can be combined, each with an independent

impact on the overall affordability of the system. The need for flexibility over the platform's

lifetime is based on the potential for unknown changes in requirements, including high power

and new variant combat systems and missions. The overarching framework is based on the

principles of Real Options analysis, where design options are considered in relation to the cost

per value given. In early-stage design and requirements formulation, this type of analysis is

invaluable for estimating potential changes to system requirements and evaluating cost-effective

means of responding in the future, but requires upfront investment in design. The authors define

a trading space of the type and quantity of modular and adaptable technology, taking into

account the cost impact in terms of weight/space/design effort. The technology for this flexible

vessel is proposed by considering the location of the future system, power capacity, sufficient

power conversion and distribution, and cooling capacity to support the future system.

(Page J., 2012) discusses the value of flexibility options in the design of early-stage naval

warships rather than options on projects or designs. The authors argue that the analysis of Real

Options and Net Present Value (NPV) needs to be modified to evaluate capital projects (without

income) and options in design based on needs, costs, and capabilities. The authors identify power

generation and power distribution as the top design considerations for considerations that allow

for the upgrade of historical ship platforms, following general arrangements. Given the Navy's

budgeting constraints that limit investment in new capabilities through the development of new

ship classes, the framework is presented using the Overall Effectiveness Scale based on the

Preferred Model of how capabilities can be added to a single ship class over time. This example

compares an inflexible platform (the current Navy) to a notional modular platform with some

flexible preparation. The authors suggest extending this framework to the subsystem level or

SoS-level analysis. The paper also points out that flexible platforms have lower upfront

acquisition costs, contrary to many discussions about flexibility costs.

(McCauley, Hannapel, Bassler, & Koleser, 2016) introduced the concept of "SWAP Boxes"

to separate the ship's payload (combat system) from the platform. This decoupling is intended to

counteract the tendency observed in the Navy's design program to quickly lock in design

requirements to reduce design time and limit the weight of the ship to control costs. The authors

state that flexibility and modularity are two concepts: "flexibility is the ability of a ship's design

to Accommodating the growth of combat systems, and the ability to incorporate new

technologies into ships throughout the life cycle of each ship and its class. Modularity is the

ability of a platform to accept a system as a standalone unit with a standard interface." They

define flexibility as a function of four criteria: design flexibility, construction modularity,

mission modularity, and mission flexibility. Some of the key benefits of implementing the

SWAP Box approach are the ability to apply targeted system margins versus top-level margins

and the ability to perform sensitivity analysis against the intended system maturity. For impacts

on power and energy systems, the SWAP Box parameters will include mission-related loads

used to measure distributed systems; However, this method clearly does not apply to the

architectural design of the power and energy system itself.

3Problem Statement

A published review of the material has identified several key elements of the SoS ilities for

further refinement and implementation in the Navy's power and energy system design process.

The Navy design community needs a consistent method for evaluating non-functional

requirements at the initial design stage, when informed decision-making provides the greatest

opportunity to positively impact system performance and lifecycle costs. This research proposes

a framework to develop metrics that can be used to identify preferred options with design

spaces. The proposed process is undertaken to develop quantitative and measurable metrics for

the flexibility of the Navy's energy and power systems, a non-functional requirement that

appeals to the design and acquisition community but lacks a common understanding base. This

research and its implementation are framed by a problem statement:

To measure non-functional requirements for early-stage design decision-making

By developing metrics for the Navy's power and energy system Flexibility

Use the framework to characterize potential disruptions that affect system changes and

measure the value of potential design options in terms of the physical, logical, and

operational impact of the system.

4Research Summary

This study presents a hierarchy of ility relationships for naval power and energy systems

and proposes a framework to outline top-level requirements and ility-based requirements into

metrics to identify the dominant architecture in the early-stage design trade space. The

framework considers the physical, logical, and operational aspects of

to generate a series of disturbances that are likely to affect the system's ability to

maintain value throughout its life cycle. Choosing a preferred architecture requires balance

between uncertainty, performance, cost, and complexity for the "right size" of the system. A

deep dive into Flexibility, of general interest, is presented with four case studies using proposed

metrics for power and energy system flexibility. This work is intended to present a repeatable

process for developing metrics that can be integrated in early-stage design tools to generate and

evaluate naval power and energy systems.

4.1 Ility Relationship

The collection of research presented in the literature review shows a general definition of

ilities as an emerging system property that affects the system's ability to retain value over time.

Ilities are not primary functional requirements, as defined in the Initial Capability Document or

Capability Development Document that define the system's objectives, but rather attributes used

to measure the system's ability to respond to change.

Emergent refers to the functions or capabilities that are generated when multiple elements of a

decomposed system architecture are integrated together. While the design community agrees on

perceived value in analyzing ility, system architects and decision-makers need a consistent

method for prioritizing and measuring ility requirements. The U.S. Navy Guide identifies the

need to assess issues such as reliability, maintenance, sustainability, flexibility, and

vulnerability. The Vessel Specification will typically detail the expected productivity,

operation, and maintenance of the vessel. However, this propriety is usually measured in the

final stages of design, after the ability to influence the architecture of the system has passed. An

upfront understanding of dependencies and the relationship between functional needs and

requirements will allow designers to identify stronger solutions when making architectural

decisions in the early stages of design.

This study investigates the relationship between systemic diseases and underlying

characteristics that may be common to certain types or families of diseases. Figure 7 illustrates

the ilities hierarchy for the Navy's power and energy systems, based on the end-of-the-road

approach presented by de Weck, et al. (2012). Of course there are more diseases than are

depicted, but this representation is meant to focus on things that are significant to maintain the

value of the power and energy system. The final average approach is represented by an arrow

directed upwards from the lower level condition that allows the above attributes. The overall

purpose of the ility hierarchy is to enable Solidity Value, or retention of value under the

influence of change. At the base of the hierarchy are the physical, logical, and operational

attributes of the system that serve as the basis for the properties that emerge, as discussed in

Section 5.1. The next part of the study will focus specifically on Flexibility as a priority system

property due to the current rate of change in the functional demands on the P&E system.

Figure 7: "Ility" hierarchy of power &

energy systems Table 1. Definition of

ility

Perseverance The ability of a system to...

Adaptation to be changed by the system's internal change agent with intent*

Affordability to minimize acquisition and lifecycle costs to maintain value

Agility to be changed in time*

Flexibility to make changes in the system in response to interference

Note: Flexibility is further elaborated based on temporal responses, as defined in

Section 5.

Maintainability to be maintained regularly so that failure does not occur.

Modularitas consists of modules (at various levels of module composition)

Recovery to restore the system in a timely manner (with various measures of timeliness)**

Reliability to operate without problems, as measured over a period of time

Resistance to maintain a defined level and/or set of parameters in the context of changes in the

internal and external forces of the system*

Scalability to change the current level of the specified system parameters*

Survivability to minimize the impact of limited duration disruption on value delivery*

Vulnerability to reduce the likelihood or magnitude of the disruption**

Sturdines

s value

to maintain value delivery despite changing needs or contexts*

Vulnerability to maintain a minimum acceptable level of value delivery during and after the

disruption**

* verbatim from (de Weck, et al., 2012, p.7)

** framed by (Richards, 2009, p.61)

While survival is widely accepted as unraveling into vulnerability, vulnerability, and

recovery, the literature on flexibility ranges from intrinsic design properties to tangible options

for stakeholder value. Informally, in the field of Marine Engineering, the two ilities are used

interchangeably to describe the ability to maintain the performance of a system; However, the

main distinguishing differences in applications come from the origin of the interference in the

system, and the identification of possible system attributes. Disruptions that require system

resilience are posed by deliberate threats to degrade system performance, whereas flexibility

disruptions are based on the competitive performance of the system itself or the capabilities that

stakeholders want. Persistence is most closely related to the short-duration flexibility sub-type,

due to the nature of real-time limited duration disruption.

4.2 Framework for Space Exploration Design

In early-stage design, the assessment criteria for determining the preferred solution can be

challenging to elaborate. Often, designers are faced with a large number of viable architectures

that meet key functional requirements. This study presents a framework for establishing metrics

that measure the value of system diseases, as a means to identify preferred solutions in the design

space. It is applied here, in the evaluation of power and energy systems to take into account the

multi-disciplinary aspects of the system system. It is hypothesized that, in complex system-of-

system designs, the lowest level of the system definition is the selection of design variables that

are combined to form the metrics used to measure ility. The framework shows that a set of

common architectural attributes can be linked in a way that aims to develop system metrics and

characterize ilities. The output of the P&E system framework focuses on the "Right Measure" of

the system, finding a balance between uncertainty, performance, cost, and complexity. The

framework for the exploration of the design space consists of the elements in Table 2.

Table 2. Framework for Establishing Ility Metrics

Step Action

1. Define the properties of the system that appear that are interesting.

2. Characterize the attributes of a system in terms of its physical, logical, and

operational architecture. Define the system boundaries and necessary interfaces in

the logical model of the system.

3. Assign a viable solution design trading space, which is determined by the low-level

system attributes of each option.

4. Identify a comprehensive set of potential disruptions that affect emerging system

properties. Maintain the Immediate External Disruption (IEP) perspective, as

proposed by Hein (2022), tracing the chain of effects caused by the broader

influence on the system system down to the disruption that occurs directly at the

boundaries of the subsystem.

5. Start connecting potential preparations in the design to a set of distractions to verify

the sturdiness of the potential design solution's space. Break down the preparation

into its basic attributes in the physical, logical, and operational view of the system.

6. For disturbances of interest, generate design metrics to measure the value of the

system under the influence of changes caused by a given disorder. Leverage the

physical, logical, and operational attributes of the system to identify independent and

dependent variables.

These steps are shown in Sections 5 and 6 to develop metrics to measure the flexibility of the

Navy's power and energy systems and how to balance performance with system affordability.

5Flexibility for Ship Design

Flexibility is ility that often comes up in discussions about complex system attributes and

requirements but lacks a clear and consistent definition. From the literature review in Part 2,

several authors have identified common characteristics of flexible systems in the context of

Naval Architecture and ship design, but at varying degrees of specificity. (Chin, Yau, Kok Wah,

& Khiang, 2013) discusses a comprehensive maritime system system, which attributes flexibility

to the degree of ease of making changes to maintain mission effectiveness in response to

external or internal disturbances. At the platform level, (Doerry & Koenig, 2017) has expanded

the definition of "ease" to include measures of speed, timeliness, and cost, and (Hein, 2022)

identifies that disruptions can be anticipated or unknown at the time of making the necessary

design decisions that determine the capabilities of the platform. (McCauley, Hannapel, Bassler,

& Koleser, 2016) identified mission systems as a driver of platform flexibility, which (Schank,

et al., 2016) relates to the ability to change the boundaries of a physical platform by providing

excess space and flexible infrastructure.

From the perspective of the commercial energy industry, the International Energy Agency

(IEA) defines the flexibility of a power system as "the ability to respond in a timely manner to

variations in electricity supply and demand" (Gutierrez Tavarez, 2019). This definition of

industrial flexibility can be tailored to naval power and energy system applications on board

and used to develop metrics for early-stage design evaluation.

5.1 Power and Energy System Flexibility

Flexibility is the ability of a system to accommodate changes in response to disruptions in

requirements. The usefulness in the application of flexibility depends on the boundaries of the

defined system and the difference between short-term and long-term impacts. Requirements,

such as Top-Level Requirements or system specifications, refer to the measurable needs of

stakeholders. Requirements can be organized into the physical, logical, and operational context

of the system to better understand the design drivers and determine possible design

characteristics.

For naval power and energy (P&E) systems, flexibility is measured within system

boundaries, in response to disruption from new loads and changes that require power (demand)

or changes to energy flow sources (supply). The following discussion, in Sections 5.1.1 to

5.1.3, defines power and energy systems in the construction of physical-logical-operational

capabilities introduced by (Brefort, et al., 2018). Together, these systems

the view connects "the right strength, the right location, the right time, and the right conditions" (Doerry, 2014).

5.1.1 Physical

The physical view is concerned with the spatial configuration of the system and the physical

attributes of each subsystem and component. A P&E system is a distributed system that covers

the entire ship and consists of many components that are usually listed on the Machine

Equipment List (MEL). In this view, a system can be described as a series of nodes that

represent each component or closed subsystem. Each node is assigned a location using a

coordinate system to establish integration in the entire ship architecture and to determine the

location of the nodes in relation to each other. The metrics used to measure the physical

requirements and characteristics of the system include a measure of distance and the physical

attributes of each component, including space, weight, power, and cooling (SWAP-C). The

following list of attributes, in the context of physical display, can be used as parameters and

variables to develop power and energy system metrics.

Power and Energy Systems - Physical Attributes

Place

Access, required transfer routes, and reservations

Distance between nodes

Gross number and percentage of boat compartments touched

Stacking length

Number of components by type

Direct Costs

Control system computing and processing equipment

Component level

oSpatial: area and volume

oHeavy

Weight of components

Weight per meter (for distribution components)

oPower level (supply and/or demand)

Installed power versus available power (by type)

oCooling level (supply and/or demand)

oEfficiency and disadvantages

oFuel consumption

oPower density

oSpecific power

System level (number of components by type)

oSpatial: area and volume

oHeavy

oPower level (supply and/or demand)

Installed power versus available power (by type)

oCooling level (supply and/or demand)

oEfficiency and disadvantages

oFuel consumption

oPower density

oSpecific power

Flexibility in physical display is driven by system configuration. The selection of

components consisting of power and energy systems and their integration in the ship's platform

determine the potential flexibility of the system. Component capacity is measured against the

system's requirements for supply and demand. Options for implementing flexibility in system

attributes include the provision of traditional Life Allowance margins on SWAP-C, the

installation of excess capacity (e.g., installed power plants) beyond the initial platform

requirements, and specifying system interface standards for future subsystem integration.

Spatially, the architecture of the P&E system must be arranged to align with the hull features

and electrical zones. Options for implementing physical-spatial flexibility include designing

reconfigurable spaces, providing access points and equipment, or reserving excess manageable

areas within a defined hull compartment. Modularity, a design feature that allows for the

swapping or plug-and-play capabilities of various system sub-modules within a specified

location and interface standard, is defined in physical display.

5.1.2 Logis

The logical view describes the functional characteristics of the system and the relationships

between the components of the system that enable the emerging capabilities. Power and energy

systems are multidisciplinary, with components connected across mechanical, electrical, thermal,

and signal domains. Figure 8 depicts the flow of electric power, thermal aids (water and

air), and data across the electrical, thermal, and signal domains for representative Integrated

Power System architectures. In the IPS configuration, as described in Section 1.1, the propulsion

module is considered in the power and energy system, represented as an external load. In logical

view, links are identified to connect individual subsystems or component nodes created in

physical view. Each relationship needs its direction, type, and magnitude to represent a flow in a

defined domain.

Figure 8: Logical Model of Power and Energy System for Integrated Power System (IPS) for

Combatants

The following list of "ility" specific attributes, in the context of a logical view, can be used

as parameters and variables to develop power and energy system metrics.

Power and Energy Systems - Logical Attributes

●Number of flow types

●Number of links on each node (total and per domain)

oSupply links

oRequest link

●Standard interfaces (types and quantities)

●Energy flow capacity

●Number of alternative paths for a given energy flow

oTime and cost required to switch between paths

●Mechanical Domain

oEquipment type: motor, gas turbine, diesel

●Electrical Domain

oEquipment type: battery, gas turbine, diesel, fuel cell

oDistribution type: frequency, voltage, current (combination)

●Domain Termal

oWater system type: cold water, fresh water, seawater

oAir system type: ambient, forced air, air-conditioned

●Signal Domain

oControl system type: localized, enclave, network

oControl system interface type

oControl system direction: two-way, one-way

Flexibility in a logical view focuses on the system's ability to provide the necessary

relationship between supply and demand elements in each functional domain when the system

becomes aware of future disruptions in requirements. Power and energy systems include a

network of distributed systems to allow flow within each domain. The functional flexibility of

these systems often centers on the conversion and distribution of flows and the types of

compatible supply and demand elements. To facilitate system measurement and design

decision-making, the SWAP Box method introduced by (McCauley, Hannapel, Bassler, &

Koleser, 2016) can be used to represent unknown future elements that require a variety of

potential P&E system services. The logical view also provides insight into the ability to

reconfigure the system in response to a realized glitch.

5.1.3 Operational

The operational view defines the temporal behavior of the system that is necessary to

complete a particular mission, including the sequencing of system functions. This view connects

the physical and logical aspects of a particular architecture to the performance of the system,

often referred to as the Performance Measure. Typical design requirements, as identified in

Section 1.2, specify the desired functional capabilities in a particular operating scenario.

Scenario timescales can range from instantaneous system responses to multi-year expenditures,

such as estimated maturity and technology integration. For power and energy systems, these

requirements can target the specific capabilities of components within each specified domain

(supply side) or come from higher-level platform performance requirements (demand side), such

as those related to the energy consumption of the platform. The following list of attributes, in the

context of an operational view, can be used as parameters and variables to develop power and

energy system metrics.

Power and Energy Systems – Operational Attributes

Operating duration

Opportunity cost

oUpfront fees

oReconfiguration fees

oAlternative investment costs

Classification of supply sources and demand loads

oVital vs non-vital

oMission important

Number of required operating (loading) modes

Scale each required mode of operation (loading)

Loading conditions:

oNumber and magnitude of Serviced load (power)

oAmount and magnitude of Loads served (thermal)

oShip Speed

oFlow rate

Component Life

Battery charge and discharge rate

Response time (e.g., breakers, signals, generator starts, backups, and parts)

Pulse loading

Mechanical Speed, rotations per minute (RPM)

Specific fuel consumption at loading speed and conditions

Dynamic simulation output

Operational flexibility is differentiated between requirements for instant response to real-

time changes in running conditions outside of design requirements, and system reconfiguration

in response to requirements changes that arise over large time scales (order of magnitude in

years). The various combinations of demand loads (combat systems, ship service, and

propulsion loads) that require service and energy flows in each domain determine operational

scenarios for power and energy systems. Examples of operational flexibility include the ability

to discharge power from one load category to serve another, the use of energy storage in

response to real-time operational changes or service disruptions, and the ability to combine

elements of future combat systems with unique load profiles, such as pulse loads.

5.2 Disruption to Flexibility

Disruptions in requirements beyond the initial system design criteria drive the need for

flexibility. For power and energy systems, the disturbances are traced from the broader system

context to the direct impact on the system boundary in a logical view, Figure

8. They are easily identified based on impacts associated with the source of energy flow

generation (Supply) and the required load (Demand). The method for localizing influence on

this system is based on the concept of Immediate External Interference (IEP), proposed by

(Hein,

2022). The process of identifying IEPs is essential for establishing the metrics necessary to

determine the value of a system in the context of any ility requirements.

Table 3 and Table 4 identify disruptions in P&E systems that require flexibility to maintain

the capabilities and value of the system. They are distinguished by the response time required to

change the system. Short-term disruptions are realized while the system is operating; they

needed a flexibility solution for the P&E system to maintain acceptable performance with

existing system components and configurations, including software and controls. The

identification of operational disruptions should be balanced with the range of required operating

conditions that the system will be designed to achieve, so that disruptions represent new

requirements or unforeseen criteria for maintaining desired operation. Long-term disruptions are

realized over a long period of time, often projected years before realization, and can be met with

planned system upgrades in the future.

The following set of impairments is considered against potential preparations in the

design that require flexibility in the appearance of physical, logical, or operational systems.

Further disruptions may apply to the evaluation of the P&E system in the context of other

illities but will require adjustment of the impact of the related system. The basis of operational

flexibility in power and energy systems is strongly correlated with disruptions for survival and

reliability, i.e. disruptions stemming from equipment failures and maintenance measures.

(Doerry & Amy, 2011) defines a design metric for related ility, quality of service (QoS), to

address unexpected service interruptions, which can lead to disruptions in operating

requirements and conditions. Each of the disorders below is not limited to the immediate

impacts and applicable design preparations listed but has the potential to produce cascading

effects that trigger additional flexibility-related impairments and other necessary ailments.

Table 3: Short-term (operational) disruptions beyond initial design requirements that require

Flexibility.

Gangguan Subtype System Design Preparation Example

Change of

propulsion load

from design to

new conditions

The demand for

propulsion loads varies

from design conditions

e.g. propellers,

shafts, gears, motors,

drive efficiency

Changes in how

ships plan to operate

in service (e.g. twin

shafts vs trails)

Physical: Built-in capacity

(power & cooling); Selection of

component types

operational: change the mode

of operation; discharge of

energy flows from other

requests

IPS Architecture:

Installation of power

plants and supply &

demand of propulsion to

maximize efficiency

across the desired modes

of operation

Load variation of

expected design

conditions:

Combat/mission

system elements

Physical: Built-in capacity

(power & cooling); Location of

distribution elements (including

zones)

Logic: the amount and type of

energy flow required

IPS architecture:

reduce propulsion

demand to increase

mission load

Primary/secondary/

combat system power

distribution architecture

Cable HTS

Capacity and

Change of

mission/combat/se

rvice load of the

ship from design

to new conditions

Additional elements

Elements of ship

service

operational: change the mode

of operation; discharge of

energy flows from other

requests

control of power

electronics, load

centers, SWBD

Offboard Vehicle

Hosting & Service

(Energy and Control)

Variation of power

quality anomalies and

differences from

expected design

conditions:

Frequency

voltage

current

physical: the size and location of

distribution and conversion

elements; Use of special equipment

Logical: number and type of

distribution and conversion

elements

Power Electronic

Building Block

(PEBB)

Modular power

converter

The type of cooling of

the demand element

differs from the design

conditions

Physical: cooling system

capacity, configuration, and

routing

Logic: number and type of

cooling source

Operational: Determine the

loading condition, change the

operation mode

Chilled/fresh/marine

water and HVAC

system design: capacity,

redundancy, location,

loading conditions

Physical: additional system capacity

Operating

environment

Temperature changes:

Atmosphere

Cooling water source

air internal

compartment

Logic: number and types of

assistants/alternative

recommendations

operational: change the mode

of operation; discharge of

energy flows from other

demands; Change in flow rate

Specified operating

modes for load release

Operable aids

(FW/SW/CW)

Threats/signatures

physical: special equipment

required; location of the specified

element; Number of elements

needed per energy flow

Logic: Operational System

Management Control: Change

operating mode

Design for signature

mitigation in specific

operating modes

Signature Augmentation

Fuel type change

physical: selection of power

generation equipment; Types of fuel

input systems

Operational: Efficiency

Reduced fuel availability Operational: Change the mode of

operation

Unique load types

Pulse load, variation of

design expectations

Physical: including specialized

energy conversion, distribution, and

storage equipment; location of

distribution and conversion

equipment; Size of supply and

distribution elements to maintain

system inertia

Logical: management of

control systems; Number and

types of distribution and

conversion elements

operational: sequencing and logic

of load conditions and operating

modes; discharge of energy flows

from other requests

Integrated energy

storage - types and

capacities

battery,

flywheel,

capacitor

IPS architecture -

dynamic loading

capability

High power short-

duration load, variation

of design

Hope

Ramp-rate, a variation of

design expectations

Table 4: Long-term disruptions (realized in future maintenance periods) beyond the initial design requirements

that require Flexibility.

Gangguan Subtype System Design Preparation Example

Changes in power plant

components

physical: weight and adjustable

space margin; access/deletion

routes; Distribution system size

Logical: compatibility of energy

flows (voltage, frequency,

current, auxiliary); Modified

control system logic

operational: change the mode of

operation; Efficiency changes

generator swap

(GTG/DG)

Adding additional

generators to existing

plants

Modified distribution

system

physical: weight and adjustable

space margin; Pre-planned Setup

and Routing

Logical: modified control system

logic; Number and type of electrical

support connections and additions

operational: change of mode of

operation; Efficiency changes

replace conventional

with high-temperature

superconducting cables,

or MVDC cables (cable

bundling, number)

Modular Power Node

defined interfaces

(spatial and physical)

accessible cable trays,

cable corridors, cable

P&E system

configuration

changes

breaks

Improved power

electronics and

switchboards

physical: SWAP-C margin;

pre-planned arrangements and

routing; Hazard Mitigation

logical: modified controls;

Changes in the quality of

energy flow

Operational: Operation mode

change, efficiency change

PEBB

Modular SWBD, load

center cabinet, electric

bus

New/additional

secondary distribution

loops (goal-driven)

physical: weight and adjustable

space margin; Pre-planned Setup

and Routing

Logical: modified control system

logic; Number and type of electrical

support connections and additions

operational: change of mode of

operation; Efficiency changes

Distribution of

special combat

systems

Defined interface,

specified power

quality

accessible cable trays,

cable corridors, cable

breaks

Physical: rating of the

propulsion system; adjustable

higher or more

efficient: turbine

Change the elements of

the propulsion system

weight and space margin

Logic: compatibility of the

shafting system; Number and

types of additional supporting

connections

(mechanical), electric

motor (IPS)

change the type of

motor

(AIM/PMM/HTS/Po

dded)

Switch to motor drive

operational: change the

operation profile; efficiency

changes; discharge of energy

flows from other requests

Change the topology of

the propulsion system

physical: SWAP-C margin;

Pre-planned arrangements and

routing subsystem capacity

(generation, distribution,

conversion, increment)

Logic: the amount and type of

energy flow required; Modified

Controls

Operational: Change of operation

mode

Mechanical to hybrid

conversion

physical: SWAP-C margin; Expand energy

Change the energy

storage system

pre-planned arrangements and

routing; Hazard Mitigation

logical: energy flows of number

and type; additional interfaces;

Modified Controls

operational: change of mode of

operation; Efficiency changes

capacity (additional

point-of-use system

capacity or integrated

energy storage)

Changes in technology

or combinations of

battery types, rotating

engines, etc.

Required - Fire

Fighting and Safety

System

Changes to

payload:

New load types (pulse

load, ramp rate, etc.).

physical: SWAP-C margin; the

inclusion of specialized

equipment; Pre-planned Setup

and Routing

logical: modified controls;

Number and type of energy flow

connections

Operational: Change of operation

mode

Interface Control

Document for planned

future upgrades

Integrated energy storage

Dynamic loading

capability

Physical: Generation,

IPS architecture -

reduces propulsion

mission/combat

systems, ship

service, support

Increased demand:

vital/nonvital

Load Case Conditions

Distribution, Conversion,

additional subsystem capacity

operational: discharge from

other energy streams; Change of

operation mode

demand to increase

mission load

Interface Control

Documentation for

planned future updates

New responsiveness

(agility) requirements

physical: the inclusion of

special equipment; Margin

SWAP-C

logical: the amount and type of

energy flow; Modified Controls

Operational: Change of operation

mode

Inclusion of energy

storage and power

electronics

Request location change

physical: weight and adjustable

space margin; Pre-planned

distribution and conversion

capacity, setup and routing

Interface Control

Document for planned

future systems

Flexible infrastructure

Secondary impacts

realized in the

physical: weight and adjustable

space margin; Additional system

capacity

Ability to run multiple

water-cooled systems to

a specific load

ancillary system

operational: discharge from

other energy streams; Flow Rate

Thermal Battery

Additional Air

Conditioning Factory

Modified damage

control and fire

extinguishing

requirements (response

to other configuration

changes)

physical: adjustable space; Pre-

planned Setup and Routing

Logical: modified control

system; Defined interface

Operational: Change of operation

mode

Reconfigurable Zones

Planned operation of

piping, standard piping

joints or valves

HVAC

intersection and

connection; Fan

room

arrangement

Personnel upgrades

physical: HVAC and electrical

capacity; Request location

Logic: electrical and

auxiliary connections

Changes in

command

Introduction to Artificial

Intelligence (AI)

physical: processing capacity;

inclusion of special hardware

(sensors-processors-actuators);

logical: HW/SW data

P&E system

designated: Modular

electronic enclosure

Platform/System

Network Configuration

Requirements for

autonomous operation;

Personnel reduction

and control connection; Control Logic

operational: change of mode of

operation; internal/external

communication; Maintenance of

the P&E system;

Signature/Security

distributed and

multifunctional

control stations

Off-platform

communication and

control

HM&E Control

Power Plant Control/IPS

Operating

Environmen

Arctic Operations

physical: HVAC and electrical

capacity; Request location

Logic: electrical and

auxiliary connections

Loading plugins (hot/de-

es)

Environmental

regulations

physical: selection of power

generation equipment; energy

storage size; Fuel type

operational: change of mode of

operation; Efficiency changes

6Metrics for Flexibility

Design metrics are quantitative or qualitative measures of system characterization and

measured value. In the early stages of design, metrics are formulated to assess the system's

ability to achieve design requirements and other desired capabilities, including ilities. When

evaluating a large multi-attribute trading space of a potential system architecture, alternative

design instead uses two or more sets of metrics to understand the design trade-offs and

determine the preferred or non-dominated design. A typical trading space exploration will

evaluate primary and secondary performance measures against cost requirements to uncover

system configuration trends within an open design trading space.

System attributes in a physical, logical, and operational view, as identified in Sections 5.1.1

to 5.1.3 for power and energy systems, serve as a foundational element for capability metrics. For

things like flexibility, any measure of performance can be traced to the physical attributes of the

elements that make up the system; However, the logical and operational nature of these elements in a

wider system configuration is necessary to achieve the desired emergent capability. Flexibility, due to

the ability to make changes in a system in response to interference, requires initial consideration of

how the chosen architecture will respond within each design domain.

For U.S. Navy ship design, the standard measure of flexibility is the Service Life Allowance

(SLA) requirement, which equates each ship's intended year in service with a measure of future

growth and fatigue capacity based on historical trends such as weight growth and increased

electrical load demand over time. The Navy's design authority, the Naval Marine Systems

Command (NAVSEA), breaks down SLAs into space, weight, power, and cooling-specific

design domains (SWAP-C). This allowance is used to inform the design of power and energy

systems and auxiliary systems, measure the shape of the hull, and design the hull structure. For

power and energy systems, SLAs represent flexibility based on gross capacity, but do not

address the necessary decomposition to the subsystem level such as the necessary preparation in

power distribution and energy storage modules to ensure the intended future capabilities can be

achieved. Table 5 shows the Service Life Requirements defined in the NAVSA Naval Combat

Design

Specifications' (2014) in each of the SWAP-C criteria, for vessels with an expected service life.

Table 5: Required Life Allowance for 20 and 30 years

Life Allowance 20 Years 30 Years

Space 0% 5%*

Weight & KG 10% & 0.3

million

10% & 0.4

million*

Power** 15%* 20%

Cooling 15%* 20%

*Notional value, not specified in NCDS

**Values based on IPS representative traditional separate vessel service manpower system

The following sections identify metrics for evaluating the flexibility of power and energy

systems in early-stage design space exploration activities, such as concept formulation,

preliminary design, alternative analysis, or requirements evaluation and development. The

distinguishing factor of early-stage design is the relatively low amount of design-specific

information available to determine the system architecture. Designers and decision-makers will

typically start with a list of initial machine tools of components that drive acquisition costs and

determine gross system capacity, such as prime movers, generators, power converters, and

transformers. The size and quantity of these components are balanced with an estimate of the

first-order load demand based on historical regression or ratio, the known demand from the

required mission equipment, and the initial system layout in the conceptual ship stacking setup.

The following process traces the disruptions identified in Section 5.2 to three categories of

system flexibility requirements: power capacity, distributable power, and energy storage.

Metrics to characterize capabilities in each category are proposed using physical-logical-

operational system attributes. This process can be used to develop ility metrics to incorporate in

early-stage design tools, such as the Leading Edge Architecture for Prototyping Systems

(LEAPS) suite of product model tools, including Smart Ship System Design (S3D) for energy

flow analysis.

6.1 Power Capacity

Flexible power capacity depends on the physical attributes of the power plant subsystem and

the design rating of its components. In an operational perspective, flexible power capacity

depends on the specified supply operating conditions of the power generation subsystem and the

demand from the ship's mission system and service elements. While the overall power and

energy system can be measured based on defined Life Allowance requirements, the definition of

operating conditions provides a realistic measure of the system's ability to accommodate

potential future loads. For IPS systems, power flexibility is determined by the size criteria of the

ship's power plant subsystem, including requirements-driven loading conditions. Sufficient

power generation is required to power the electric propulsion motors, provide ship service

power, and operate the ship's mission systems. Requirements-driven loading conditions

determine the combination of ship speed and electrical load of the mission system that requires

simultaneous power supply. Typically, the power plant's size requirements will determine the

propulsion load required to ensure sustained speed, as this is the order of magnitude load highest

on board. The corresponding electrical load of the mission system depends on the purpose of use

of the platform, which may require the vessel to operate the most stressful mission load at

sustained speed or a representative average of the daily load experienced during mission

operations.

Flexible Power Capacity (FPC) metric. Equation (1) defines the flexibility power

capacity (FPC) as the sum of the total available distributable power (PDST ), based on the

capacity of the generating and distribution subsystems; minus the sum of all required loads

(LREQ) in the system size criteria used for the calculation, such as the 24-hour average load or

the maximum margin electrical load; divided by the total installed power (Ptot). Distributable

power including energy

generated on board the ship available for the ship's mission and service systems, whereas

depending on the architectural topology, the total installed power includes all the energy

generated. For example, in an IPS architecture, the distributable power may be equal to the total

installed power, but the mechanical architecture will have a separate ship service power plant

and a dedicated propulsion diesel or gas turbine directly connected to the shaft line. FPC

metrics provide a measure of relative flexibility for alternative architectures that meet similar

mission requirements and should not be used to compare platforms with drastically different

initial load requirements. For these types of high-level material solution considerations, the total

size of excess capacity in megawatts is more appropriate. Section 6.1.1 outlines the differences

in applying metric (1) to different power and energy system architectures.

FPC = PDST− LREQUEST

Ptot

(1)

Metrics of Renewable Power Flexibility (DPF). The second metric for flexible power

capacity utilization in IPS architectures, where the total power generated is required to serve

propulsion as well as mission and ship service loads, is the flexibility of dischargeable power

(DPF), equation (2). Where FPC Metrics consider the physical architectural elements of a

system under specified loading conditions, Dischargeable Power Metrics consider the

operational architectural capabilities for the system topology applicable across a wide range of

operational loading conditions, determined by a combination of load requirements.

Dischargeable Power is the ability of IPS systems to prioritize the load receiving power,

effectively discharging power from one load category to serve another. Since the largest

magnitude load by category is the propulsion load at continuous velocity (Lps), the available

dischargeable power load (Lavail) is the propulsion load used to measure the propulsion

subsystem (LpREQ) minus the propulsion load required to make the minimum acceptable

mission speed (Lpmin). The DPF is then the minimum of the new load request above the initial

design requirement (Ladd) and the available dischargeable power load, divided by the new load

request. Case 2 will discuss the sensitivity of IPS power flexibility to the selected sizing,

propulsion, and mission load criteria.

The mana lavel = lprac −

lpmin.

DPF = min

(Ladd, Lavail)

Ladd

(2)

A phenomenon observed when using this metric to compare the power and energy systems

integrated in a ship concept with a wide range of hull efficiencies is that a less efficient hull

requires a greater installed power capacity to achieve the same top-end speed, thus providing a

greater dischargeable power load available when the propulsion requirement is reduced to the

minimum acceptable speed. However, these perceived benefits sometimes only lead to the

selection of a system within the trading space when balanced with other attributes, such as cost.

The right size of the power plant subsystem to align with the desired mode of operation leads to

the preferred architecture.

Propulsion Shaft Horsepower

70000

60000

50000

40000

30000

20000

10000

6.1.1 Case 1: Flexible Power Capacity Metrics

The following example shows the application of the Flexible Power Capacity metric,

Equation (1), for three different power and energy system architectures: Integrated Power

Systems, Hybrid power systems, and mechanical propulsion systems with separate ship service

power plants. In each architecture, sensitivity to specific load conditions is demonstrated by

varying the load criteria for ship service and mission elements between the case of a 24-hour

average electrical load and the propulsion load between sustained speed and economical transit

(shipping) conditions. In addition, each demand load is evaluated on the initial shipment and

end-of-life conditions to indicate an increase in demand over time.

For the basis of this analysis, the notional ship concept is utilized from the NAVSEA

Design Data Sheet (DDS 200-2) for 'Calculation of Annual Energy Use and Cost of Surface

Ships' (2012). The concept has a design life of 20 years, requiring a 15% power SLA. Table 6

shows the electrical load for each operating condition of the design, including 50% of the SLA.

Economical transit is performed at 16 knots, a surge to the theater requires 30 knots of

propulsion power, and the ongoing mission propulsion load is based on the velocity-time profile

specified in DDS 200-2. The propulsion velocity power curve for the required shaft horsepower

(SHP) per node is shown in Figure 9.

Table 6: Electrical Load Conditions at Various Temperatures and Operational Scenarios

(NAVSEA, 2012)

Temperature

(°F)

In the harbor -

Beach

Power (kW)

Ongoing - Economical

Transit (kW)

Ongoing - Surge to

Theatre (kW)

Ongoing -

Mission

(kW)

70000

60000

50000

40000

30000

20000

10000

0 5 10 15 20 25 30 35

Speed (kt)

10 1,000 3,000 3,000 4,800

59 500 1,800 1,800 3,200

100 900 2,400 2,400 4,000

Propulsion

Load

- 7,100 46,800 7,208

Figure 9: Propulsion Velocity-Power Curve (NAVSEA, 2012)

SHP (kW)

The three representative vessels were built using the same hull shape, mission system load,

and propulsion requirements, but with three different P&E system topologies: IPS, Hybrid, and

Mechanical. The DDS 200-2 representative ship concept is utilized for the Integrated Power

System, which consists of three Large Gas Turbine Generators (LTG), two Small Gas Turbine

Generators (STG), and two electric Propulsion Motor Modules (PMM). On the basis of this

comparison, alternative hybrid and mechanical architectures are created to provide comparable

power to both propulsion and mission loads, as shown in Table 7. In the IPS concept, PMM is

sized to achieve a design sustained speed of 30 knots at eighty percent of the maximum

continuous rating (MCR). The power generation subsystem, consisting of LTG and STG, is

sized to provide sufficient power for sustained speed conditions plus mission load at end of life

(EOSL), taking into account motor efficiency and power transmission losses. For the hybrid

concept, the propulsion subsystem consists of a PMM, sized to achieve an economical transit

speed of 16 knots, plus two propulsion gas turbines (PGTs) coupled directly one to each shaft in

an 'OR' configuration, so that the PMM and PGT do not combine to achieve a sustained speed,

and the required propulsion demand is supplied by one or the other. Hybrid power generation

subsystems are sized to provide full power to PMM and mission loads in EOSL. Finally, the

mechanical concept propulsion subsystem consists of four PGTs, two per shaft, and a power

generation subsystem sized to provide the mission load in the EOSL with one offline generator

for redundancy, referred to as the requirement (N-1). This requirement (N-1) does not apply to

IPS or hybrid architectures because the amount of installed distributable power capacity is

greater which allows the system to discharge the propulsion load to compensate for the generator

victim.

Table 7: List of Main Machine Tools

IP Hybrid (or) Mechanic

Number of

Units

Total kW Number of

Units

Total kW Number of

Units

Total kW

Large Turbines

Generator Set

(LTG)

3 72,000 0 - 0 -

Small Turbine

Generator Set

(STG)

2 6,000 5 15,000 3 9,000

Propulsion Motor

Module) PMM

2 60,000 2 8,000 0 -

Gas Propulsion

Turbin (PGT)

0 - 2 60,000 4 76,000

Built-in Power

Driving

Conditions

Generation

Continuous

Speed Propulsion

(30kt)+

EOSL mission

Propulsi Listrik Maks

(16kt) + misi EOSL

Misi EOSL (N-1)

Power plant

Needed

- 67,370 - 12,938 - 5,136

Total Installed

Power

5 78,000 7 75,000 7 85,000

The case of IPS architecture. In the IPS architecture, it is assumed that the full amount

of power generated can be distributed throughout the ship for the ship's propulsion or mission

load; thus, the Power Distributable (PDST ) is equal to (Ptot) at 78 MW. In reality, there may

be a limit on the amount of power that can be distributed on a single bus, limiting the power

available for non-propulsion loads based on a particular distribution architecture. The required

load (LREQ) depends on the specific combination of propulsion and mission load demands, and

the amount of service life consumed.

Table 8 specifies the Flexible Power Capacity for IPS architectures at sustained speeds

while operating in two different modes: mission in progress at 10° Fahrenheit, requiring

maximum margin electrical load, and ongoing economical at 10° Fahrenheit, requiring an

average electrical load of twenty hours. Each combination of loads will develop over the life of

the vessel as SLA is consumed and a reduction in the efficiency of propulsion of the fact of life

is realized. Required "on-delivery" loads include required propulsion shaft horsepower with a

PMM efficiency of 94% at sustained speed and declared mission loads without SLA. The "at the

end of life" load applies an additional 25% growth factor for SHP propulsion for hull fouling and

plant degradation and a 15% growth factor for mission load for SLA consumed. Table 9

provides the calculation of the Flexible Power Capacity for the same load condition at cruising

speed, where the PMM efficiency is 91%.

Table 8: IPS at sustained speed

IPS: Continuous Speed

Max Margin Load Max Margin Load 24 HR AVG di 24 AVG HR on

on Delivery (no

SLA)

on EOSL (with

SLA)

Shipping (without

SLA)

EOSL (with

SLA)

PDST (kW) 78,000 78,000 78,000 78,000

LREQ (kW) 54,253 67,370 52,578 65,444

Ptot (kW) 78,000 78,000 78,000 78,000

FPC 0.30 0.14 0.33 0.16

Table 9: IPS at Roaming Speed

IPS: Cruising Speed

Max Margin Load

on Delivery (no

SLA)

Max Margin Load

on EOSL (with

SLA)

24 HR AVG di

Shipping (without

SLA)

24 AVG HR on

EOSL (with

SLA)

PDST (kW) 78,000 78,000 78,000 78,000

LREQ (kW) 12,268 14,889 10,593 12,963

Ptot (kW) 78,000 78,000 78,000 78,000

FPC 0.84 0.81 0.86 0.83

The case of hybrid architecture. For hybrid architectures, where electrical propulsion

PMM is required to cover a smaller portion of the propulsion speed-power curve than IPS,

distributable power (PDST) is significantly less, at 15 MW. In this configuration, the propulsion

power at the upper end of the speed-power curve is provided by a special PGT on each shaft,

which is accounted for in the 75 MW Ptot. In operating conditions with high speed

requirements, PGT is online to provide a propulsion load, and LREQ only reflects the ship's

mission load. In conditions with speeds of up to 16 knots, the LREQ includes power for the

electric propulsion PMM in addition to the ship's mission load. Table 10 and Table 11 show the

difference between the loading conditions that require PGT and PMM propulsion services. In

each example, the LREQ is calculated at the maximum average load and twenty-four hours at

the time of delivery and at the end of the service life, as evaluated in the case of IPS. A PMM

efficiency factor of 94% is applied to the propulsion load in all cruising conditions (16 knots),

and a 25% hull fouling and factory degradation factor is applied to the end-of-life evaluation.

Table 10: Hybrid with Continuous Speed (PGT) Required

Hybrid: Continuous Speed (PGT)

Max Margin Load

on Delivery (no

SLA)

Max Margin Load

on EOSL (with

SLA)

24 HR AVG di

Shipping (without

SLA)

24 AVG HR on

EOSL (with

SLA)

PDST (kW) 15,000 15,000 15,000 15,000

LREQ (kW) 4,466 5,136 2,791 3,210

Ptot (kW) 75,000 75,000 75,000 75,000

FPC 0.14 0.13 0.16 0.16

Table 11: Hybrid with Cruise Speed (PMM) Required

Hybrid: Cruise Speed (PMM)

Max Margin Load

on Delivery (no

SLA)

Max Margin Load

on EOSL (with

SLA)

24 HR AVG di

Shipping (without

SLA)

24 AVG HR on

EOSL (with

SLA)

PDST (kW) 15,000 15,000 15,000 15,000

LREQ (kW) 12,019 14,577 10,344 12,651

Ptot (kW) 75,000 75,000 75,000 75,000

FPC 0.04 0.01 0.06 0.03

Mechanical architecture case. In the case of mechanical architectures, electrical power

distribution capacity (PDST ) is not required for any part of the propulsion load and, therefore, is

measured only on the basis of the ship's service and mission load. Propulsion demand, an order

of magnitude greater than the maximum margin electrical load, is served by a dedicated PGT

and is included in the total installed power (Ptot). The required load (LREQ) is calculated at the

maximum margin load and averages twenty-four hours at the time of delivery and at the end of

the service life, as evaluated in the case of IPS and hybrids. Mechanical power flexibility, Table

12, calculated based on the same loading requirements as the continuous velocity hybrid casing,

using PGT propulsion power.

Table 12: Mechanical (depending on non-propulsion)

Mechanical: Independent of Propulsion

Max Margin Load

on Delivery (no

SLA)

Max Margin Load

on EOSL (with

SLA)

24 HR AVG di

Shipping (without

SLA)

24 AVG HR on

EOSL (with

SLA)

PDST (kW) 6,000 6,000 6,000 6,000

LREQ (kW) 4,466 5,136 2,791 3,210

Ptot (kW) 85,000 85,000 85,000 85,000

FPC 0.02 0.01 0.04 0.03

Discussion. When establishing flexible power capacity requirements, the selection of

determining loading conditions should be based on the intended use of the platform and

CONOPS. The above case comparison provides the requirements owner with additional context

into the differences between the resulting architectures that will be driven by a specific set of

requirements for the designer to choose from. Figure 10 describes the flexible power capacity for

each IPS, hybrid, and mechanical architecture considered across a wide range of potential

loading requirements. Each of the eight loading conditions is plotted for IPS and hybrid

architectures, along with four mechanical load cases. Flexibility metrics are plotted against the

normalized power balance and the power available to serve needs due to significant capacity

differences for integrated versus separate power systems. This normalization indicates the

amount of power required for each individual load case versus the capacity of the physical

architecture installed.

Flexible Power Capacity

1.00

0.90

0.80 IP

0.70 Hybrid

0.60 Mechanic

0.50

0.40

0.30

0.20

0.10

FPC

Figure 10: Flexible Power Capacity (FPC) metrics for IPS, Hybrid, and Mechanical instances

versus normalized power capacity, the required load case over distributable power

0.50

0.40

0.30

0.20

0.10

0.000.100.200.300.40 0.50

LREQ/PDST

0.600.700.800.901.00

The IPS example architecture has an installed capacity beyond the minimum requirements

for the end life based on the selected combination of LTG and STG. The plant lineup identified

in DDS 200-2 (NAVSEA, 2012) targets improved energy efficiency at each operating condition,

which requires a mix of low- and high-power turbines aligned with the required load

combinations. This configuration provides flexible power capacity in every evaluation condition,

including the most stressful cases: continuous speed plus maximum margin electrical load with

full SLA consumption. IPS examples have five times the amount of power that can be distributed

as a hybrid example and thirteen times the number of mechanical examples. When evaluated for

Flexible Power Capacity, including consideration of total installed power and propulsion plus

vessel service load in each condition, the IPS example scored one and a half times greater than

the average hybrid PGT propulsion FPC value across four loading conditions, and eleven times

greater than the mechanical architecture FPC value on average.

Interestingly, Case's results determined that the flexibility of hybrid architecture FPC is

higher at high speeds, while the flexibility of IPS architecture FPC is higher at low speeds. In an

'Or' condition with online PMM (up to 8 MW), the flexibility measure of the hybrid architecture

is significantly reduced from the flexible power capacity when using PGT, as electric propulsion

consumes more than half of the power available for distribution. However, it should be noted

that there may be limitations in the minimum operating speed for scenarios that can take

advantage of the flexible power capacity of PGT-specific operating conditions based on the

minimum RPM of the propulsion gas turbine and the shaft propeller design.

The mechanical case requires the most installed power of the three architectures, as the load

required for the mission and propulsion is isolated to a dedicated power supply, resulting in the

lowest amount of distributable power. In addition, although the mechanical concept requires the

installation of redundant/backup ship service power plants to meet the requirements (N-1), the

third STG does not contribute to distributable power.

6.1.2 Case 2: IPS Dischargeable Power Flexibility Metric

This case uses the notional IPS vessel concept from DDS 200-2 (NAVSEA, 2012), as

described in Case 1, to demonstrate the metric of dischargeable power flexibility. Two variants

of the IPS architecture, with 30-knot and 27-knot sustained velocity (LpREQ) requirements, are

compared to isolate the impacts associated with specific architectural sizing criteria for the

required propulsion load. The debitable power metric for each variant is evaluated for a 1-knot

and 5-knot speed reduction in the minimum required propulsion load (Lpmin), both at the initial

and end-of-life delivery conditions. Three new sets of load demands on top of the initial design

requirements (Ladd) were then used to represent various future mission system requirements.

Table 13 shows the dismissable power flexibility (DPF) for a 30-knot IPS architecture,

given the 1-knot speed reduction for the minimum acceptable propulsion load at the time of

transmission and EOSL conditions. Table 14 calculates the DPF metrics for the same architecture

but

with a speed reduction of 5 knots for the minimum acceptable propulsion load. An additional

25% propulsion factor applied to EOSL conditions reduces the debitable power load (Lavail) by

11 MW in the case of a 1-know reduction and 7 MW in the case of a 5-knot reduction. This

results in lower DPF values when assessed against a 15 MW load for a 1-knot reduction case and

a 30 MW load for a 1-knot and 5-knot reduction case. In all acceptable minimum propulsion

conditions, the 30-knot IPS architecture easily accommodates the additional load case of 2 MW.

The 5-knot speed reduction significantly increases the availability of the dischargeable power

load, a 94% increase in delivery conditions, and a 340% increase in EOSL conditions.

Table 13: IPS 30 knot Dischargeable Power – 1 knot Reduction

Propulsion

Conditions

Kw Propulsion

Conditions

LpREQ 30kt, 100% MCR 62,234 LpREQ 30kt, 100% MCR 62,234

Lpmin 29kt, Delivery 45,014 Lpmin 29kt, EOSL 56,268

Availabili

17,220 Availabili

5,966

Ladd (kW) DPF Ladd (kW) DPF

Load 1 2,000 1.00 Load 1 2,000 1.00

Load 2 15,000 1.00 Load 2 15,000 0.40

Load 3 30,000 0.57 Load 3 30,000 0.20

Table 14: IPS 30 knot Dischargeable Power – 5 knots reduction

Propulsion

Conditions

Kw Propulsion

Conditions

LpREQ 30kt, 100% MCR 62,234 LpREQ 30kt, 100% MCR 62,234

Lpmin 25kt, Delivery 28,812 Lpmin 25kt, EOSL 36,015

Availabili

33,422 Availabili

26,219

Ladd (kW) DPF Ladd (kW) DPF

Load 1 2,000 1.00 Load 1 2,000 1.00

Load 2 15,000 1.00 Load 2 15,000 1.00

Load 3 30,000 1.00 Load 3 30,000 0.87

The 27-knot sustained speed variant of the notional IPS architecture assumes the same

velocity-power curve performance of the hull, but the reduced top-end speed requires less total

installed power. Table 15 shows the flexibility of dissipable power for a 27-knot IPS

architecture, given the 1-knot speed reduction for the minimum acceptable propulsion load at the

time of delivery and EOSL conditions. Table 16 calculates the discharge power metric for the

same architecture but with a 5-knot speed reduction for the minimum acceptable propulsion

load. Based on the lower speed requirements, which correspond to exponentially less resistance

and propulsion demand along the velocity-power curve, the concept has less dischargeable

power load available in both speed reduction conditions. Compared to the 30-knot concept, the

available load is 20-25% lower for the 27-knot concept case. Regardless of the difference in the

magnitude of the load available in all conditions, the relationship between the load available at

the time of delivery and the EOSL condition applies to the 27-knot concept, with a 98% increase

for a 1-knot reduction

and a 330% increase in the case of a 5-knot reduction. In summary, the 27-knot concept prints

lower dischargeable power flexibility in all cases and fails to provide the load threshold

available for the 15 MW2 load case in a 1-knot reduction in the transmission case, where the

30-knot IPS concept is capable of providing sufficient flexible power in the case of a 1-knot

reduction.

Table 15: IPS 27 knot Dischargeable Power – 1 knot Reduction

Propulsion

Conditions

Kw Propulsion

Conditions

LpREQ 27kt, 100% MCR 45,495 LpREQ 27kt, 100% MCR 45,495

Lpmin 26kt, Delivery 32,535 Lpmin 26kt, EOSL 40,669

Availabili

12,959 Availabili

4,826

Ladd (kW) DPF Ladd (kW) DPF

Load 1 2,000 1.00 Load 1 2,000 1.00

Load 2 15,000 0.86 Load 2 15,000 0.32

Load 3 30,000 0.43 Load 3 30,000 0.16

Table 16: IPS 27 knot Dischargeable Power – 5 knots reduction

Propulsion

Conditions

Kw Propulsion

Conditions

LpREQ 27kt, 100% MCR 45,495 LpREQ 27kt, 100% MCR 45,495

Lpmin 22kt, Delivery 19,830 Lpmin 22kt, EOSL 24,787

Availabili

25,665 Availabili

20,708

Ladd (kW) DPF Ladd (kW) DPF

Load 1 2,000 1.00 Load 1 2,000 1.00

Load 2 15,000 1.00 Load 2 15,000 1.00

Load 3 30,000 0.86 Load 3 30,000 0.69

While the flexible power capacity metric considers architecture-specific installed power

plants and electrical loading conditions, the flexibility of dischargeable power focuses only on

the demand load conditions, given the established system sizing criteria. Figure 11 graphically

shows the increase in load available when the propulsion load is discharged for the 27 and 30

knots concepts in their EOSL state. The area below each curve, constrained at the lower end by

the Lpmin velocity, is the available flexible power, as evaluated in the case in Table 13-16.

Horizontal gray lines are placed on three evaluation loads for 2, 15, and 30 MW. Where the

shaded area does not overlap with the horizontal line, the discharging power flexibility is less

than one, with the score decreasing as the distance between the two vertical arrows is drawn at

1 and 5 knot speed reductions, as evaluated above.

Figure 11: Flexible Power – Available Load with Speed

The metrics of dischargeable power flexibility for each of the eight conditions are plotted

in Figure 12 against the three additional load requirements (2, 15, and 30 MW). The figure

illustrates the point at which each case can no longer meet the additional load when the DPF

drops below one. The 30kt IPS concept outperforms the 27kt concept in every delivery/EOSL

combination and

-1/-5 knots of minimum propulsion load due to the exponential shape of the speed power curve.

The higher the sustained velocity required, the greater the load available when the minimum

propulsion load is identified along the exponential curve. In addition, as expected, we see that a

-5 knots reduction for the minimum propulsion load gives the greatest load and DPF value

available in each condition. Finally, the impact of the fact of expected life growth in the

propulsion load to achieve the minimum acceptable speed in the EOSL reduces the load and the

DPF available for an additional load of 15 and 20 MW in each case.

Power-Speed Flexibility

70,000

60,000

L-avail 27kt EOSL

L-avail 30kt EOSL

50,000

40,000

30,000

20,000

10,000

0 5 10 15 20 25 30 35

Speed (knots)

Figure 12: Flexibility of Dischargeable Power versus Available Load e.g. the case of speed delta

6.2 Distributable Power

The flexibility of the power distribution system is necessary to link the generating

capacity with the specific load demands of components throughout the vessel. Distribution

includes the transmission of energy flows throughout the ship and the conversion of energy

into the voltage and quality required by the end user, as shown in the logical view, Figure 8.

The physical configuration of the distribution system depends on the maximum distribution

capacity, the type and rating of available voltages, and the spatial considerations in which the

load is on board, which is usually limited by the assignment of electrical zones. Load

requirements will vary within each zone, depending on the interfaces required for each end

user. Therefore, the flexibility of power depends on the local conversion and distribution

capabilities of each zone.

Power Distribution System Flexibility Metric (PDSF). The power distribution system

Ladd (kW)

35,00030,00025,00020,00015,00010,0005,000-

0.00

0.20

30kt (-5kt EOSL)

27kt (peniriman -

1kt) 27kt (-1kt

EOSL)

27kt (delivery -5kt)

27kt (-5kt EOSL)

0.60

0.40

30kt (delivery -1kt)

30kt (-1kt EOSL)

30kt (-5kt delivery)

0.80

1.00

1.20

Discharge Power Flexibility

flexibility metric uses 'evaluation loading sets' to represent interface types and classifications of

potential future load requests within individual zones. The evaluation loading set is a

compilation of potential load elements in the future, beyond the initial system design

requirements for on-demand service at the time of delivery plus the required lifetime allowance.

Sets can be generated to include the various load characteristics required for service of the

power and energy system to be supplied, such as voltage type, voltage rating, and power draw.

Since the demand for propulsion loads for IPS vessels significantly exceeds the mission and

service loads of ships in any zone, they are considered separately from the distribution

evaluation loading set. Table 17 shows five evaluation loading conditions based on four potential

elements of the future mission and a representative set of their combinations. Each load element

is differentiated based on the type of voltage and power demand. The ~1000 VDC demand is

typical of high-power mission systems such as radar and laser weapons and can be taken

directly from the main power distribution bus. Other low-voltage demands, such as onboard

computing and thermal auxiliary systems, require conversion and distribution of power within

zones within a secondary power distribution system. In the exploration of the early-stage design

trading space, full permutations of single elements and combinations thereof can be used to

define simple and indicative metrics for the flexibility of distributable power. Further in the

design process, ship configuration details such as general settings and mission station locations

are established, and evaluation loading sets must be adjusted to reflect the revised open trade

space or uncertainty for specific zones.

Table 17: Example of an 'evaluation loading set' distribution system for potential future load

requests

Voltage

Type:

1000 VDC 800-650 VAC 450 VAC

Load

Condition

(N)

Element*

(Power -

kW)

Element

(Power - kW)

Element

(Power - kW)

N1 Lasers (1200) Base Load (500) Base Load (2000)

N2 Radar (1000) Base Load (500) Base Load (2000)

N3 EW (1500) Base Load (500) Base Load (2000)

N4 NA

Base Load (500)

Energy Magazine

(1000)

Base Load (2000)

Lasers (1200)

Radar (1000)

EW (1500)

Base Load (500)

Energy Magazine

(1000)

Base Load (2000)

* Electrical loads for interesting mission system elements taken from (ESRDC, Ship Concept Alternatives,

2017)

The distribution capacity within a zone depends on the size of the primary power

distribution system, which carries the intermediate voltage power from the onboard generator,

and the secondary power distribution system, which converts the intermediate voltage power

into lower voltages and currents that are directly compatible with the end-user demand. Power

distribution systems can be configured in a variety of topologies, such as radial, distributed, or

zonal buses, with each option having sacrifices in space, weight, cost, and performance.

Flexibility of ship power distribution system (PDSF), Equation (3), is the average of the

flexibility of each zone

(DSTzone ): the amount of flexibility of each zone, divided by the total number of zones

(Nzones ). Equation (4) determines the flexibility score of each zone by assessing the ability of

the distribution within the zone to meet a set of load conditions (N). If the zone has sufficient

capacity in all categories of the specified rating criteria, (Nj) will be rated as 1, otherwise, if the

distribution architecture cannot satisfy any of the categories under load conditions, the zone will

receive 0.

This approach provides a measure of platform distribution flexibility, regardless of

total number of electrical zones, as described below in Section 6.2.1.

PDSF =

DST Zone

∑zone(s)

DSTzone

Zone

= N1+N2+N3+⋯

+Nj

Note

(3)

(4)

Flexibility can be combined (and purchased) as capacity in the design on the initial delivery

of the system, or through design preparation that allows for future system upgrades when

needed. Primary and secondary power distribution configurations (ring, distributed, zonal, or

other) control the capabilities inherent in the system that affect flexibility, as measured in

equation (4). Table 18 provides three examples of power distribution system features that allow

for flexibility by increasing the total number of potential load cases either on the initial system

delivery or as a future reconfiguration. Section 6.2.1 provides case studies comparing split rings

and zone distribution system architectures at different stages of the design specification process,

and different points in the platform's lifetime. Section 6.5 will outline the use of real options to

differentiate between the value of installing capacity in advance of the design representative in

the ability to upgrade the system in the future after the disruption has been realized.

Table 18. Examples of flexible distribution system features

Flexible Electricity Distribution Impact

The electric power distribution bus is

dedicated to the expected high power

load.

Increase the number of potential load cases by

allowing new mission system elements to be installed

in any zone, with

reduce dependence on power conversion capacity

within zones.

The use of HTS cables – variable

current, depending on temperature.

It can increase the power distributed to the zone by

lowering the temperature of the cable without adding

new cables. Requires additional cooling. (Note: not

necessarily available

instantaneous, design preparation is required)

Use of programmable and/or modular

power conversion and power

electronics:

-Power Electronic Building Block

(PEBB)

-Integrated Power Node Center

(IPNC)

Reduce the total number of power conversion

elements. Provides the ability to tailor conversions

within a specific zone to the needs of future end users

using or

general distribution equipment.

6.2.1 Case 3: Power Distribution System Flexibility Metrics

This case shows how to build an evaluation loading set and use it to assess the flexibility of

a power distribution system in a P&E system architecture.

Conventional split-ring bus architecture (early-stage design): based on the

concept of a 10,000-ton IPS ESRDC vessel (Smart, et al., 2017)

Alternative ring bus (later design stage): a variant of the ESRDC concept case

is presented to demonstrate the maturation of the evaluation criteria as the design

space for potential future loads is reduced.

Zone distribution architecture (base model): based on the Integrated Fight-

Through Power (IFTP) concept described in the 'Next Generation Integrated Power

System Roadmap (NGIPS)' (Doerry, 2007)

Zone alternatives (future block upgrades): variants of the NGIPS concept are

used to show the increased flexibility associated with future upgrades to the initial

base architecture.

The evaluation loading set is built as a full permutation of each element load in Table 19,

which includes the base load required on the delivery plus any potential future mission systems

that may be required to be hosted by the platform in the future. The voltage type and power

ratings for this evaluation set are notional in nature, based on the payload list identified in

(Smart, et al., 2017), and do not represent the actual value of the Navy system. Elements listed

with multiple power ratings, separated by commas, represent different configurations that future

systems may reflect. The various options per element type can represent the uncertainty of the

ranking or quantity of the elements. The two baseload LVAC options reflect potential

differences in multiple ship zones during delivery. The inclusion of a zero kW element load

allows the evaluation set to take into account potential zone requirements that do not include a

given mission element. The full permutation of these load elements results in 1,728 evaluation

conditions, which are provided in Appendix A; each of these evaluation conditions is assessed

against each zone in a given distribution system architecture to determine the distribution score

for that zone, then the zone scores are combined for the overall PDSF metric. To simplify the

assessment of a particular electrical distribution zone, the load applicable to each set is summed

up by the voltage type category, in this case as 1000V Medium Voltage Direct Current

(MVDC), between 650-800V Alternating Current or Direct Current (MVAC/MVDC), or 450V

Low Voltage Alternating Current (LVAC). For example, the 300th permutation consists of:

[500 kW MVAC/DC Base Load, 1500 kW LVAC Base Load, 200 kW MVAC/DC Energy

Magazine, 600 kW MVDC Laser, 0 kW MVAC/DC Processing, 0 kW MVAC/DC VLS, 1700

kW mvdc Radar, 4000 kW MVDC Wipe, 450 kW MVAC/DC Sonar

which amounts to [6,300 kW MVDC, 1,150 kW MVAC/DC, 1500 kW LVAC].

Table 19: Evaluation Load Set Elements

Voltage Type: MVDC (direct pass)MVAC / MVDCLVAC

Element Power (kW) Power (kW) Power (kW)

Base Load NA 500 1500, 2000

Energy Magazine NA 0, 200, 1000,

2000

Laser 0, 600, 1200 ON ON

Processing Equipment ON 0, 200 ON

Missile Launcher ON 0, 400 ON

Radar 0, 1700, 3300 ON ON

Electronic Warfare (EW)0, 2000, 4000 ON ON

Sonar ON 0, 450 ON

Variant 1: Ring Bus (initial stage design evaluation). The conventional split-ring-bus

architecture, shown in Figure 13, is based on the 10,000-ton IPS concept (Smart, et al., 2017),

with four electrical distribution zones, a 10 kVDC primary power distribution system voltage,

and dual power lines on the port and right sides of the ship via a fully connected ring bus. The

power generation module (PGM) and the propulsion motor module (PMM) are connected

directly to the ring bus via a corresponding converter or drive. The basic architecture includes a

dedicated converter for high power loads to connect two Radars and one Railgun to the main

distribution bus; however, for this case and the evaluation load set, the topology was modified

to replace the Railgun converter with a converter for EW and Laser elements in Zone 1, add a

second EW converter in Zone 2, and add a second Laser converter in Zone 4. The power

conversion module (PCM) represents the converter and inverter in each zone, connecting all

other loads to the port and the right bus. The size of these converters is taken directly from the

ESRDC concept, and the total distribution capacity by zone is summarized in Table 20.

Figure 13: Topology of the Conventional Split Ring Bus Distribution Architecture. Based on

(Smart, et al., 2017).

Each of the four electrical zones was independently assessed for its ability to meet 1,728

potential future electrical loading conditions (N) in the evaluation set. If the zone has sufficient

capacity in each of the three voltage categories, then a score of 1 is recorded for that Nth

condition, otherwise, if there is insufficient capacity in any of the three categories, a score of 0

is recorded. The sum of the 1,728 N-scores divided by the total number of load conditions N

determines the zone flexibility metric (DST zone), as shown in Table 20. The average score of

the four zones determines the total flexibility score of the power distribution system (PDSF) of 0.31.

With a PCM converter and inverter rating specified for the ESRDC concept, all four zones

are able to accommodate the maximum MVAC/MVDC and LVAC load combinations, given the

converter duplicate sets for ports and the right bus for redundancy. If the analysis is performed

assuming that only one set of PCMs is used at all times in each zone, zone 4 will not be able to

accommodate the maximum loading conditions in this voltage category and a score of 0 for this

N condition; All other zones can handle the maximum rating in these conditions with a set of

converters.

In each of the four zones, the delimiter distribution category is the MVDC converter rating

for the specific mission element. In design space exploration activities, these findings may lead

designers to investigate the ability of potential future elements to bring in additional specialized

converters when needed for future installations, along with verification of the architecture's

total flexible power capacity.

Table 20: Distribution Capacity of Conventional Split Ring Bus by Zone and voltage category;

with the distribution flexibility score of each zone taking into account

the permutation of the full evaluation loading set.

Zone 1

(kW)

Zone 2

(kW)

Zone 3

(kW)

Zone 4

(kW)

MVDC

(direct feed)

3,200 3,700 3,300 1,200

MVAC /

MVDC

8,000 17,800 12,400 5,800

LVAC 4,200 5,800 7,000 3,100

DST Zone 0.33 0.41 0.37 0.11

*Distribution capacity based on (ESRDC, 2017)

Variant 2: Ring Bus (later stage design evaluation). To simulate the progression from

the analysis of distribution flexibility from the early-stage concept design to the more mature

initial design baseline, the conventional ring bus architecture is used for the evaluation of the

second flexibility. In this case, the design space for potential zone requirements is narrowed and

the evaluation loading set is tailored to the requirements for each zone. Table 21 provides

refined requirements for evaluation loading determination criteria applicable to Zones 1-4. Zone

1, the foremost zone on the ship, was designated as the responsibility for the Sonar, due to the

formation of the hull shape and the location of the sonar dome. Radar requirements are allocated

to zones 2 and 3, which are covered by a deckhouse for mounting equipment at the top. The

Laser trading space does not change; however, the energy magazine requirement is reduced to 1

MW and the location is based in zones 2-4. The resulting increase in the flexibility score is

shown in Table 21, and the total power distribution system (PDSF) flexibility score increases to

0.64.

Note that zone 1 scored 1.0, as the evaluation loading set requirements were narrowed down to

match the MVDC converter for mission elements as intended.

Table 21: Enhanced Requirements Evaluation Loading Criteria

Zone 1 Zone 2 Zone 3 Zone 4

MVDC Limiter

Criteria (live

pass)

0x Radar

1x Unit EW

1x Laser

Maks

1x Unit

Radar 1x

Unit EW 1x

Laser Maks

1x Unit

Radar 1x

Unit EW 1x

Laser Maks

0x Radar

1x Unit EW

1x Laser

Maks

MVDC (kW) 3,200 4,900 4,900 3,200

MVAC / MVDC

Restriction Criteria

1x Sonar

0x Energy Mag

0x Sonar

Mag Energi <1MW

0x Sonar

Mag Energi <1MW

0x Sonar

Mag Energi <1MW

MVAC/MVDC (kW) 1,550 2,100 2,100 2,100

LVAC (kW) 2,000 2,000 2,000 2,000

DST Zone 1.0 0.65 0.59 0.33

Variants 3 and 4: IFTP (Future Block Base and Upgrade Model). The zone

distribution architecture is based on the concept of Integrated Fight-Through Power described in

the Roadmap of Next-Generation Integrated Power Systems (Doerry, 2007), with the notional

topology within the zones depicted in Figure 14. For this, the concept of a zone electricity

distribution system consists of 4 electrical zones, with a series of types of Power Conversion

Modules (PCMs) to convert power in each zone. The PCM-4 serves as a transformer rectifier to

convert MVAC power from the power generation module to 1000 VDC for distribution throughout

the ship. Within each zone, the PCM-1A converts 1000 VDC power into a variety of MVDC

voltages based on user needs. The PCM-2A then converts the 750-800 VDC power from the PCM-

100

1A into demand within the LVAC zone. In addition, for this concept, the notional PCM-X is

connected to a 1000 VDC bus in each zone to serve the high-power MVDC load throughout the

vessel. It is assumed that the rating of each PCM is scalable based on the number of modular

subcomponents included: the Marine Service Inverter Module (SSIM) or the Converter Module

(SSCM).

Figure 14: NGIPS Roadmap "Topology in Future Potential IFTP Zones" (Doerry, 2007)

101

Two variants of the IFTP zone concept were evaluated to demonstrate different

assessments of the flexibility associated with the architecture of the base model as originally

delivered, and the future block architecture, including several planned upgrades to the

distribution system. Section 6.5 will discuss the method for designing in "Real Options", which

requires flexible design preparation with the aim of reducing the upfront costs and risks

associated with the uncertainty of future load demand. Both variants of zonal IFTP are

consistent with this approach, as the basic model architecture includes design preparation in the

form of planned PCM growth capacity to accommodate additional SSIM/SSCM in the future, if

needed. The base model was delivered with a capacity of 5.5 MW PCM-X, 12 MW PCM-1A,

and 10 MW PCM-2A, and design preparation for the 22 MW PCM-X and 4 MW PCM-1A

SSCM/SSIM. Table 22 shows the PCM capacity for the base model configuration by zone, with

the associated zone flexibility metric (DSTzone). The total power distribution system flexibility

score (PDSF) for this configuration is 0.14. However, once the maximum PCM capacity is

installed in a future block configuration, as shown in Table 23, the total PDSF score increases

to 0.85.

Table 22: Distribution Capacity of IFTP Base Model Zones by Zone

Zone 1 (kW) Zone 2 (kW) Zone 3 (kW) Zone 4 (kW)

PCM-X 0 2,000 3,500 0

PCM-1A 3,000 3,000 3,000 3,000

PCM-2A 2,500 2,500 2,500 2,500

DST Zone 0.03 0.16 0.32 0.03

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Table 23: Future Distribution Capacity of IFTP Blocks by Zone

Zone 1 (kW) Zone 2 (kW) Zone 3 (kW) Zone 4 (kW) Total DST

Capacity (kW)

PCM-X 6,875 6,875 6,875 6,875 27,500

PCM-1A 4,000 4,000 4,000 4,000 16,000

PCM-2A 2,500 2,500 2,500 2,500 10,000

DST Zone 0.85 0.85 0.85 0.85 0.85

The flexibility metrics of the power distribution system of the four architectural variants

and the flexibility scores of individual zones are plotted in Figure 15. Each architecture is

modeled with four electrical zones, with distribution and conversion capacities varying in each

zone, across the MVDC, MVDC/MVAC, and LVAC assessment categories. Ring bus variants,

each with the same distribution and conversion capacity, are shown in blue. The early-stage

design assessment uses the full permutation of the evaluation load set, while the later stage

design assessment adjusts the evaluation load based on other known design decisions to reduce

the range of potential future load options desired in each zone. This maturation of design data

resulted in a 100% improvement in PDSF for ring bus architectures. The base model of IFTP

and future block variants are plotted in yellow, to demonstrate the increased distribution

flexibility provided by incorporating preparation in the design to accommodate the future

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(long-term) interruptions in the required load demands. The vessel concept for this IFTP variant

remains constant in addition to the installation of additional distribution and conversion

modules in future blocks, to represent an in-line increase in maintenance availability in the

same way where new load demand end-users are installed. In the exploration of design spaces,

a large number of representative architectures can be determined by the characteristics of their

respective zones, and are assessed against a series of general evaluation burdens to identify

viable options. In this limited example, the IFTP option is preferred based on the lower upfront

architectural cost and the ability to achieve higher flexibility of the power distribution system in

the future, when long-term disruption is realized.

432

Electrical Zone

Overall

Concept

0.00

0.20

0.40

0.60

0.80

IFTP (Basic Model)

IFTP (Future Block)

1.00

Ring Bus (later variant)

DST Zone

Ring Bus (early)

1.20

Power Distribution System Flexibility

PDSF

Distribution Flexibility

Systems/Zones

104

Figure 15: Power Distribution System Flexibility Score (PDSF) and individual zones (DSTzone)

6.3 Energy Storage

The flexibility of an energy storage system (ESS) provides the ability to respond to

disturbances with unique demands or load constraints in the operational view of the power and

energy system. The Naval Power Systems Technology Development Roadmap (McCoy &

Kuseian, 2013) identifies the need for energy storage to address "pulse power support for

advanced weapons and sensors, load balancing, emergency power, and generator transient

support and fuel economy initiatives." The demands of these missions, including advanced laser

weapons and radar systems, pose new challenges for P&E systems in terms of power loading

profiles, requirements for stable power cleanliness, and quality of service. In the view of a

logical system, energy

105

Storage systems focus on power capacity and power quality disruptions that impact the

upstream energy storage needs in the energy flow between supply and demand loads. Energy

storage can be placed within a primary or secondary power distribution system, depending on

its intended operational use and balanced with system integration and cost impact.

Energy Storage System Flexibility (ESSF) Metric. Like the flexibility of distribution in a

zone, ESS flexibility (ESSF) is determined by assessing the ability of an energy storage system

to meet each load scenario (S), equation (5). If the ESS has a maximum power rating and total

energy capacity to serve a load scenario, (Sj) will be rated as 1; otherwise, if it cannot meet the

total energy required, it will receive a value of 0. The set of load scenarios can be generated as a

combination of the demands of individual elements, such as complete set permutations,

variations in element peak shaving assumptions, uncertainty limits from stochastic modeling, or

with informed CONOPS requirements. The sum of the scores from the individual load profile

scenario assessment assessments is then divided by the total number of scenarios (Stots) to

provide a total measure of the platform's energy storage flexibility.

ESSF = S1+S2+ +Sj⋯

Stot

(5)

Power profiles for high energy loads and changes in the operating requirements of

propulsion systems and power plants can be modeled as an extension of the evaluation load set

developed for the flexibility of distributable power. In addition to the type and magnitude of

load used in Section 6.2, the operational scenario for assessing the flexibility of an ESS requires

a load profile to determine the load behavior, such as stochastic or pulse load, over a specified

duration. Table 24 provides an example of a load profile for an element that requires ESS

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services, based on a model proposed by the MIT Sea Grant (Tavagnutti, Chalfant,

Chryssostomidis, & Hernandez, 2023).

Compared to a distributable power evaluation set, the load profile has been expanded beyond a

single-zone focus to account for ship-wide configurations, including scenarios that require

energy storage for propulsion and ship service load reserves, referred to as spinning reserves.

The assumptions for the power demand and energy of this load profile, including the peak

shaving approach, will be discussed in detail in Case 4, Section 6.3.1. Additional load profiles

can be generated to account for mission load variations through stochastic modeling, such as

the method specified by (Stevens, Opilia, Cramer, & Zivi, 2015).

107

Table 24: Example load profiles for potential future operational scenarios that require energy

storage flexibility

Element

Types of

Operational

Behavior

Operatio

nal

Duration

Power -

Peak

(kW)

Stable

Bus

Load

(kW)

Peak Shear

Load (kW)

Max ESS

Energy Demand

(kWh)

Radar

Continuously

Stokastik 4200 1000 727.5 272.5 0.06

Laser

Intermittent

Stokastik

1800 1200 200 1000 92.3

Electronics

Warfare

(EW)

Continuously

Stokastik 4200 1500 950.3 549.7 0.79

Spinning

Reserve

Continuously

Deterministic 300 2000 0 ON 238

ESS recharging is considered in the definition of each individual operational scenario

demand based on the energy demand of the element over time, and the determination of the

power that can be drawn from the ship's power distribution system. Balancing the flexibility

performance of an ESS in the desired scenario against the cost of acquiring and integrating

vessels for a range of design alternatives will inform the decision to pursue a dedicated (point-

of-use) or integrated energy storage solution. This metric can be used to assess the flexibility of

108

specialized and integrated energy storage architectures.

6.3.1 Case 4: Energy Storage System Flexibility Metrics

This case study evaluates the energy storage flexibility of the notional Energy Storage

System design space. One hundred and twenty-five individual ESS architectures, listed in

Appendix B

determined based on their withdrawal from the ship's power distribution bus, its energy

capacity, and maximum power rating. The design space is generated as the complete set of

discrete parameter combinations defined in Table 25.

Table 25: Space Divider Parameters of Notional Energy Storage System Design

Bus

Capacity

(kW)

Energy Storage

Capacity (kWh)

ESS Puncak

Power (kW)

1 200 1 1,000

2 2,000 10 1,600

3 3,250 100 2,200

4 4,500 250 3,000

5 5,000 300 3,200

The operational scenarios used to evaluate the design space are based on the element load

profile established by (Tavagnutti, Chalfant, Chryssostomidis, & Hernandez, 2023), and do not

reflect the actual performance of the Navy's systems. Columns two through six of Table 26

define five operational scenarios related to mission profiles of the three types of element loads

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and spinning reserves for the ship's power reserves. Two element loads, the Radar and Electronic

Warfare elements, are assumed to operate in a "peak shave" profile, where the average power

Requests for operating profiles are taken directly from the ship's power distribution bus and

The ESS is responsible for fluctuations in demand above and below this average. When actual

110

The demand exceeds the supply of the distribution bus, the ESS releases the required energy

delta, and when the demand is below the bus supply, the ESS uses the load delta to recharge.

The third element, the Laser gun, relies solely on the ESS for energy supply during its active

operating time, with a constant withdrawal of 200 kW from the bus to cover the standby

conditions between shots. The "combination" scenario takes into account the simultaneous

operation of three types of individual element loads. The three elements and their combination

scenarios are stochasticly modeled in MATLAB, 7.1Appendix C , for a seventy-minute

operation period, as defined by (Tavagnutti, Chalfant, Chryssostomidis, & Hernandez, 2023).

The last scenario, the "spinning reserve", is modeled separately. For this case, a simulation of

ten runs is run for each scenario to provide an overview of the impact of stochastic variability,

with output power

and energy profiles as depicted in Appendix C Table 26 provide the average power and energy

characteristics of each individual element simulation and system-level attributes including the total

number of elements of each type. For combination scenarios, the maximum rating of each of the

included elements is adjusted to match the number of included elements.

Table 26: Case 4 Operational Scenario for Energy Storage Flexibility

Element

Radar Laser Electronics

Warfare

(EW)

Spinning

Reserve

Combinatio

Number of

elements

in the System

3 1 2 ON 6 (included)

111

Modeled

Operational

Behavior

Continuous

active stochastic

noise

Basis Sinusoid

Stochastic Intermittent

pulse length and

Event

Continuum,

Stochastic

Continu,

Deterministi

Combine

Stochast

CONOPS

Scenario

Duration (s)

4200 1800 4200 300 4200

Individual Element Attributes

Peak Power -

Single Element

(kW)

1000 1200 1500 2000 7200

Stable Bus Load –

Single element

(kW)

727.5 200 950.3 0 4352.4

Peak Shaving

Power –

Single element

(kW)

272.5 1000 549.7 ON 2847.6

Ess Energy

Demand Max –

Single Element

(kWh)

0.06 92.3 0.79 ON ON

System-level attributes – all elements included

112

Total Bus Load –

All

Element (kW)

2182.5 200.0 1900.6 0 4352.4

ESS Max Power –

All Elements (kW)

817.5 1000 1099.4 2000 2847.6

Total ESS Energy

– All Elements

(kWh)

0.18 92.3 1.58 238 99.5

113

Radar Mission Load : Modeled as a sine wave with a maximum power of 1000 kW, Image

16. The operating profile runs continuously for the duration of the seventy minute

mission, with stochastic variability added as "noise" at each time step. The Radar

demand on the ESS is based on the peak shaving assumption, with the ESS responsible

for supplying the difference between the operational Radar power demand and the bus-

supplied power. Energy is the power over time, calculated at each step of time in the

profile. The maximum energy is found from the sum of energy requirements at each step

of time. While the determination of bus power as the average radar demand will

theoretically lead to an even amount of energy charged and discharged, stochastic noise

modeling provides an opportunity for energy demand to accumulate beyond a maximum

of one sinusoidal discharge cycle.

Figure 16: Radar power profile (Tavagnutti, Chalfant, Chryssostomidis, & Hernandez, 2023)

Electronic Warfare Mission Load : Modeled as a random instantaneous load between a

maximum power of 1,500 kW and a minimum power of 400 kW, Figure 17. The

operating profile runs continuously over the duration of the seventy minute mission,

with stochastic variability incorporated into the operational EW power demand at each

time step. The EW demands on the ESS maintain the same assumptions for peak

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shaving and determination of maximum energy demand as in the Radar profile.

Figure 17: Power profile of Electronic Warfare systems (Tavagnutti, Chalfant, Chryssostomidis,

& Hernandez, 2023)

115

Laser Weapon Mission Load : Modeled as an intermittent load profile where the firing

state draws a maximum power demand of 1,200 kW and the non-firing state retains 200

kW of standby power drawn from the power distribution bus, Figure 18. Laser weapon

scenarios incorporate stochastic variability in determining the time spent firing or in

standby, with a maximum beam duration of six seconds and a maximum time between

30 seconds. This scenario accounts for thirty minutes of active charging, consisting of

firing and standby states, followed by forty minutes of ESS recharging. Since the laser

pulls almost entirely from the ESS during a thirty-minute lasing period, the maximum

energy demand for each run depends on the stochastic model of the firing duration.

Figure 18: Laser Gun power profile during 70 minutes of operation time. Enter a 200-second

period. (Tavagnutti, Chalfant, Chryssostomidis, &

Hernandez, 2023)

Combined Mission Load : Modeled as a combination of stochastic load demands of

three Radar weapon elements, two EWs, and one Laser. The maximum energy

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demand is determined by taking the amount of energy demand at each step of the

lowest level time.

Spinning Reserves : The energy capacity of the ESS is required to provide 2 MW of

continuous power for at least five minutes of ship operation, as assumed for the ship

concept presented by (Tavagnutti, Chalfant, Chryssostomidis, & Hernandez, 2023).

Each of the 125 ESS concepts was evaluated for Energy Storage Flexibility (ESSF)

measured against five operational scenarios. Table 27 identifies the number of concepts with

sufficient power and energy required to meet each scenario. Eight concepts are able to meet all

five operational scenarios. The average ESSF score across the design space was 0.41 and the

average score was 0.40. The design space is generated based on the range of system-level

demands in each element evaluation. The total power demand of bus capacity ranges from 200

kW to 4.4 MW, the demand for energy storage capacity ranges from 817 kW to 2.8 MW, and

the total ESS energy required ranges from 0.18 to 100 kWh. Design 125 concepts

117

space includes architectures that are sized taking into account system cost and affordability to provide

options that are "right-sized" for the requirements of a particular scenario.

Table 27: ESS Design Space Flexibility Results for 125 Total Concepts

Rada

Laser

Gun

Spinnin

Recom

mendat

ions

Combinatio

All

scenari

os pass

Sum

Passing Concept

75 75 64 30 12 8

Failed due to Bus

Capacity

50 0 25 0 75 NA

Failing due to

Energy

Capacity

0 50 25 75 50 NA

Failed because

of Max

Power Rating

0 0 25 50 75 NA

6.4 Interface Control

Interface control is essential for establishing system integration requirements for future

equipment installations in a broader system-of-system architecture. Identifying the right

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interface requirements, taking into account the physical, logical, and operational requirements

for a particular system, will increase system flexibility by minimizing the cascading effects of

unknown system changes in the future. There are two types of interface requirements documents

that are commonly used:

Interface Control Document: a formal means of assigning, defining, and controlling

interfaces. Document detailed interface design information between systems and sub-

systems for the platform.

Installation Control Drawings : provide ship installation data for future equipment, such

as mission system elements. These documents and drawings define and support the

engineering, installation, and construction of the platform.

For mission systems, Installation Control Documents may be written to establish maximum

physical characteristics, or "not exceeded" values, such as SWAP-C size limitations, location,

and required services of power and energy systems, and auxiliaries. In this case, the Installation

Control Document adds requirements for the capacity of the power generation system, and the

size of the distribution and conversion system in each zone.

Interface Control documents for other ship preparations, such as compartment reservations,

specify the physical and logical requirements of the reserved space on board. These documents

are often associated with a modularity approach, where the physical space and connectivity of

the modules are defined inside the ship to accommodate future theoretical systems that meet the

requirements of those interfaces. This approach determines the location of future load requests

and routes the necessary distributed systems, services, and utilities if needed.

119

Specifically for power and energy systems, an Interface Control Document can be written to

enable Real Options for future upgrades to the P&E system itself. Within the confines of the

P&E system, future requirements may drive the need for additional power generation, integrated

energy storage, and/or expanded distribution element capabilities. Section 6.5 further defines the

Real Options Analysis and provides examples related to options and preparations for future

upgrades to the power distribution system as future unknown loads are realized.

6.5 Real Options Analysis

Real Options Analysis (ROA) is a method of using flexibility in design to maximize the

expected value of a system while minimizing upfront procurement costs and operating lifecycle

and sustainability (O&S) costs. It allows designers to evaluate the uncertainties inherent in

system engineering issues and develop designs or plans that maximize value at specific times in

the system lifecycle, such as at the time of initial delivery, while maintaining the ability to adapt

to unknown requirements in the future. In other words, Real Options allows a design or project

to be ready to change, by incorporating accommodations (preparations) for flexibility. This

allows the system to retain value throughout its life cycle, rather than becoming obsolete in the

face of new requirements.

Real Options Analysis uses a Net Present Value (NPV) financial evaluation to

determine the value of real assets, such as construction projects or alternative investment

opportunities, along with a design decision model to account for the manager's role in the

Determine when to take action to implement design preparation during the system lifecycle.

Where NPV uses deterministic assumptions about cost and profit variables, ROA models

uncertainty in evaluation scenarios and looks for opportunities to use it to

system advantages. (Page J., 2012)

120

NPV analysis converts all cash flows over the life of the system into a common basis in the

present to obtain a single comparable value. This includes all life cycle costs and upfront

investments or construction costs, as well as future profits generated by the system in operations

or financial opportunities. As shown in equation (6), the NPV of future cash flows (Vt) is

determined by applying the discount rate (r) and taking into account the time between the present

and future periods for all cash flows. The discount rate is a value applied to reflect the difference

in the current value of money versus the value of the same amount of money in the future. This

allows decision-makers to recognize the cost impact or benefit of future investments in terms of

the effort spent now. In this type of valuation, the current value of money is greater than the

same amount in the future. In financial terms, the discount rate represents the opportunity cost of

capital, or the potential return on investment based on all other opportunities available to

investors, and is usually set as an industry standard. The project model that produces the naval

power and energy system is subject to the discount rate requirements set by the Office of

Management and Budget (OMB) under the Office of the Executive President. In 2022 the

discount rate is determined to be 0.5% for 30-

121

investment plus year (OMB Circular A-94 Guidelines and Discount Rates for Benefit-Cost

Analysis of Federal Programs, 2022).

NPV = Vt

(1+R)t

(6)

Ship design and acquisition are investments in the form of real assets that are not expected

to generate profits with the standard execution of NPV analysis. To balance current costs with

the value of the system in the ROA evaluation model, a measure of performance (MOP) is

required. For the analysis of power and energy systems, one of the flexibility metrics presented

in the above section is a valid MOP, depending on the scenario of interest. NPV and MOP are

modeled simultaneously in evaluation scenarios that take into account incurred costs and

variable changes that affect performance metrics over time, such as annually over the expected

lifetime of the system. The value of Real Options Analysis comes from the inclusion of

uncertainty in the evaluation scenario.

Uncertainty can be implemented by determining potential disruptions to the system, such as the

exercises shown in Section 5.2, setting minimum and maximum limits and possible uncertainty

parameters, and linking impacts to NPV and MOP variables.

Once the uncertainty parameters are linked, the designer can identify Real Options or

preparations in the design, which are necessary to minimize the risks identified in the baseline

case uncertainty analysis and provide cost-effective options to maximize system performance.

Decision models are then developed to establish the regulatory logic of when actions should be

taken in order to implement options in response to the realization of uncertainty.

6.5.1 Case 5: Analysis of Real Options of Future Integrated Power Systems

122

This case study demonstrates the use of Real Option Analysis of a flexible power

distribution architecture. A basic case and two Real Option alternatives of a notional naval

surface combatant are evaluated with the intent to maximize the platform's mission capabilities

while minimizing upfront procurement costs and O&S lifecycle costs over its expected 40-year

service life. The ROA evaluated the electrical power distribution system for the zonal IFTP

concept, an architectural variant identified in Case 3 that includes a notional energy storage

(ESM) module.

Evaluation Scenario:

Based on the combination of missions performed, operating speed, and other equipment

configurations, the power distribution system will experience various loading conditions.

Typically, distribution systems (as well as power generation systems) are measured based on the

most stressful conditions to ensure adequate capacity and performance in all conditions. This

system model evaluates performance and cost based on the most stressful scenario: the demand

for propulsion (from the PMM) plus the maximum margin electrical load (from the PLM).

Maximum margin electrical load including mission operation demand. While this scenario

requires the highest load demand on electricity

123

distribution system, it only accounts for about 12% of the ship's time at sea for the Navy's

current fleet of Arleigh Burke-class destroyers, as shown in Figure 19 (Anderson, 2013).

Figure 19: DDG-51 Mission Time-Type Operation Profile (Anderson, 2013)

The CONOPS system, modeled as the annual operating conditions of the concept, is based

on a combination of Anderson's (2013) mission speed time profile in Figure 19, and the PMM

plus PLM load set in Table 28. It is assumed that based on the load demands of the ship and

mission service systems, and the capabilities inherent in the IPS architecture, that the speed of

mission operations is allowed to decrease over the life of the vessel, in order to discharge the

propulsion power for the vessel's load demands if needed.

124

Table 28: CONOPS Condition Loading Profile

Hours % PMM (MW) PLM (MW)

Low-Speed Missions 0.57 15 20.16 (basis) / Uncertainty

of Stochastic future load

High-Speed Missions 0.12 60 20.16 (basic) / Stochastic

Uncertainty of future

burdens

Transit 0.27 18 3

Limited Maneuvers

Doctrine (RMD)

0.4 5 3

125

To determine the operational scenario on the delivery of the system (year 0), the initial

maximum margin electrical load was assumed to be 18.8 MW, based on the 85% efficiency

factor applied to the maximum distribution capacity of the current DDG 51-class destroyers,

plus a subset of the mission system equipment load of the ESRDC ship concept (Smart, et al.,

2017), as shown in Table 29.A 2 0 % l i f e t i m e a l l o w a n c e i s a p p l i e d t o n o n -

m i s s i o n s y s t e m l o a d s , a s s u m i n g a 4 0 - y e a r l i f e t i m e , r e s u l t i n g i n a n

E O S L m a x i m u m m a r g i n e l e c t r i c a l l o a d o f 2 0 . 1 6 M W , w h i c h i s u s e d

t o m e a s u r e t h e P C M w i t h i n t h e z o n e . L o a d g r o w t h i s a s s u m e d t o b e

r e a l i z e d i n 4 - y e a r i n c r e m e n t s ; T h u s , i n y e a r 4 t h e l o a d d e m a n d i s

18,94 MW, in year 8 the load demand is 19,07 MW, and so on.

Table 29: ESRDC 10,000 ton Combat Power Condition Mission System Ship Concept (Smart, et al.,

2017)

Maximum Margin Electrical Load in Year 0 (MW

)

Non-mission system load 6.8

Persenjataan

Active Rejection System 2.4

Command and Surveillance

Multi-Function Phased Array Radar 5

Integrated Top Side (InTop), including Surface Electronic

Warfare

Improvement Program (SEWIP) and communication

126

Hull Mounted Sonar , Crane Array Sonar 0.45

Total Ship Computing Environment (Integrated weapons,

sensors,

engine and navigation control system)

0.15

Vehicle

Helikopter/UAV 0

Small Boat / USV 0

Entire 18.8

Basic Architecture:

The basic architecture consists of the following modules:

Motor Propulsion Module (PMM): 2x 36 Permanent Magnet Motor.

Power Generation Modules (PGMs): 2x Rolls Royce MT-30 Large Gas Turbine

Generators, each with a capacity of 36 MW, and 2x Rolls Royce MT-5 Secondary Gas

Turbine Generators, each with a capacity of 5 MW, for a total of 82 MW of installed

power plants.

Power Load Module (PLM): propulsion and mission load demands, as identified in the

evaluation scenario, plus unknown future loads as identified below, in the Uncertainty

section.

Power Distribution Module (PDM): mainly electrical cables, sized to support

maximum distribution capacity. This capacity remains constant in the base project

model of 20.16 MW, based on PLM max margined electricity load. This assumption is

127

based on the difficulty in sizing ship cables once integrated, requiring wholesale

removal and replacement, and the complexity in modeling less significant cable

modifications between zone electrical loads.

128

Energy Storage Module (ESM): lithium-ion-based energy storage modules, sized for

specific mission load profiles.

Power Control Module (PCON): assumed to be designed alongside the base model

with preparation to support maximum system capability, not modeled in this case.

Power Conversion Module (PCM): based on the modified Zonal IFTP concept in Case

3, where the PCM-1A is primarily a power converter, with a power rating of 1MW. The

PCM-2A receives power from the PCM-1A and functions as an Integrated Power Node

Center (IPNC), to provide various types of low-voltage output power (Doerry, 2008).

The PCM-2A is a transformer rectifier and is assumed to have a rating of up to 500kW.

In addition to NGIPS-based PCM, the casing model uses a notional PCM-X and an ESM

converter to account for the demands of unknown future mission systems. The PCM-X is

assumed to have a 500kW rating and the ESM interface is assumed to have a 500kW

charging converter for every 1MW of mission load output.

The number of PCM-1A and PCM-2As required per zone is based on

maximum margin electrical load at the end of the ship's life (including 20% SLA), assuming

the ability to distribute a quarter of the total load in a given zone. It is then assumed that a

completely redundant set of PCMs is required in each zone. The PCM-X and ESM

interfaces are directly sized for the required mission load, with no redundancy or lifetime

allowance required.

Table 30 identifies the PCM rating assumptions, as well as the cost and

volumetric criteria for the ROA decision model. The fees for each PCM are notional and

do not represent the actual Navy system. Lithium-ion ESM assumes cost

$345/kWh, based on the 'National Renewable Energy Laboratory (NREL) Cost 2021

129

Projections for Utility-Scale Battery Storage' (Cole, Frazier, & Augustine, 2021)

Table 30: Asumsi PCM

Function Rating Cost Volume (m3)

PCM-1A Converter 1000 kW 1,200,000 40

PCM-2A Transform-Repair 500 kW 340,000 18

PCM-X Converter/Trafo 500 kW 1,200,000 12

ESM InterfaceConverter 500 kW $1,200,000+

$345/kWh

450 kWhr/m3

Ship Integration Interface: as the PDM size assumption, the volumetric capacity of the

ship concept dedicated to the power distribution system is measured for the maximum

distribution capacity, reflected in the number and type of PCM required, and kept

constant. Any future PLM that requires the support of an electrical distribution system

can only be activated if adequate ship compartments (volumes) are provided for the

required PCM equipment.

Performance:

The performance of the power distribution system in this model is represented in terms of

Flexible Power Capacity (FPC, equation 1). FPC is calculated for each step of time (years) and

the overall measure of performance for power flexibility is taken as a weighted average of the

130

existing conditions in 1-40 years. Weighting is based on notional temporal priority, where mid-

life flexibility of the system is prioritized over the beginning and end of life. In this case, the

desire to improve operational performance during the second half of the system's life is higher

than immediately after the delivery of the system (requiring taking new mission system assets

offline) and at the end of the service life (limited operational value based on hull life).

The amount of power capacity that can be distributed is based on the number of power

conversion modules of each type and the capacity of the PDM in advance. In this model, the

distributable power demand is initially based on the Navy's standard design practice of the

Service Life Allowance. The PCM and PDM architectures of the base case are sized to meet the

deterministic predictions of end-of-life condition requirements, based on projected SLA

requests, so that the Flexible Power Capacity performance metric is a positive value near zero.

When uncertainty is introduced in the ROA model, the performance value will reflect the impact

of the magnitude of the change in actual demands and when in the lifecycle of their systems is

realized.

NPV:

Net Present Value is calculated in this model based on Basic Construction Costs (BCC) and

Operating and Sustainability costs. BCC covers the material, labor, and overhead costs

associated with purchasing, constructing, installing, and activating a vessel. In this model, the

BCC cost relationship is developed using the 'MIT 2N Ship Cost Model' (2016), with input from

the ESRDC ship concept (Smart, et al., 2017) as a replacement platform and the cost of the

power distribution system module as defined in the basic architecture. To determine the impact

of implementing Real Options, the BCC cost model was adapted to identify the cost of additional

manageable vessel volume and the cost of the required PDM equipment.

131

Table 31: Cost Parameters

Cost Parameters Value

BCC ($M) $1,003+(PCM Fee)+(ESM Fee)+(Fee Volume)+(PDM

Fee)

Volume cost ($M/m3) $0,031

PDM Fee ($M) ((delta MW)/0.85PF/13800V)/800A*$250/ft*775ft

O&S Fees ($M) $200+($3)*(3000Hours)/(6.8*(Fuel Consumption

Rate)/1000)

O&S costs take into account the annual costs of personnel, operations, maintenance, energy,

replenishment, and support activities. This model has isolated the annual energy impact

associated with the ability of the power distribution system architecture to meet the required

PMM and PLM power load demands. This requires characterization of the interface of the power

distribution system with the PGM, including fuel consumption and generator lineup. In

particular, the level of fuel consumption depends on the load demand of PLM plus PMM, the

selection of the generator lineup, and the efficiency of the generator under those operating

conditions. At each annual time step, the fuel consumption level is determined by pairing the

appropriate PGM range, Table 32, in each operating condition specified in CONOPS, Table 28.

Fuel consumption

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the next level is adjusted to account for notional PGM efficiency based on the generator load

level, Figure 20 (Smart, et al., 2017).

Table 32: Power Plant Lineup

PGM Composition MW online Fuel Consumption

(ltons/hour)

MT5 5 1.36

2x MT5 10 2.73

MT5 + MT30 41 8.70

2xMT5 + MT30 46 10.06

2x MT30 72 14.67

MT5 + 2x MT30 77 16.03

2xMT5 + 2xMT30 82 17.39

Figure 20: Efficiency of notional generators as a function of power levels (Smart, et al., 2017)

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Uncertainty:

In evaluation scenarios, a number of disruptions can affect performance measures and NPV.

For this case, the main source of uncertainty is modeled based on long-term disruption to

"change in mission system load", with a sub-type of "increased load demand", as identified in

Table 4. To account for future variations in mission system loads, the probability distribution

function was developed from a combination of potential high-energy weapons and sensors, as

identified by ESRDC (Smart, et al., 2017). To simulate the normal ship placement, maintenance,

and upgrade cycles, the system model assumes that the upgrade of the mission system occurs

gradually over and over again.

Therefore, the probability that an increase in the load of the mission system is realized in a

given year is modeled as a probability of 10%.

In addition to large fluctuations in the electrical load demand of the mission system itself,

secondary uncertainty parameters for the "new type of load (pulse load, ramp rate)" sub-type

interference are included in the model to account for the portion of the future mission system

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loads that require energy storage, and a sub-type of "secondary impact realized in auxiliary

systems" to account for future mission system load efficiency. This secondary

The uncertainty parameter takes into account the impact of the ship's service distribution system

associated with auxiliary and supporting equipment, such as thermal management and control

equipment.

The uncertainty input factor is shown in Table 33.

Table 33: Mission system load uncertainty input factors

Variable Uncertainty

Probability of Schedule

Increase

10%

Realized Request (Mission

Load)

LOGNORMAL PDF

Mission System Load

Efficiency

30-70%

Part of the new Mission

Payload

Requires ESM

0-100%

Probability Distribution

Log-Normal Distribution

mean 1.34

Standard deviation 1.7

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Excel Result Model:

Given that the Mission System Load is the highest source of uncertainty, a decision model

was developed to account for the variability of the electrical load demand of the mission system

elements and the necessary additional equipment, to ensure the performance of the distribution

system over the 40-year service life of the platform. Decision models are also needed to

minimize the cost impact of NPV required to provide performance flexibility.

In the base model, the expected mission system load is specified to be 12 MW, served by 24

dedicated 500kW PCM-X. The Navy's standard 20% electrical power SLA is applied to the

Primary and Secondary power distribution element sizes, PCM-1A and PCM 2A, but no design

considerations are included for mission system elements that require dedicated PCM-X utility.

In standard practice, the ability to add the necessary capabilities in the future will depend on a

combination of separate SLA categories for vessel displacement, KG, and manageable areas, so

a custom PCM-X can be added. Once the specific SLA capacity of other non-electrical systems

has been consumed, the standard basic approach will require the one-for-one removal of existing

equipment to be replaced with a new desired element or demand load.

The excel decision model is built to support the evaluation of Real Options that takes into

account the uncertainty of the mission system load and provides a cost-effective means to

improve the future load capability of the mission system. The decision criteria in the model are

structured around the identified uncertainties: the schedule of the increase in the load of the

mission system, the magnitude of the increase in the load of the mission system, the efficiency

of the new load, and the type of load. If the conditions to meet the identified decision criteria are

realized, design flexibility options can be implemented.

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Table 34: Decision Criteria

Sources of Mission

System Load

Uncertainty

Characterizat

ion of

Uncertainty

Decision Criteria System Impact

Upgrade Schedule

Event

10% chance in each

year

New mission system

Load request

Quantity and types

PCM, dan ESM

Amount of Load Log-Normal PDF Mission system load

Increase

Quantity and types

PCM, dan ESM

Load Efficiency 30%-70% Additional support

Equipment required

Number of PCM-1A

and

PCM-2A

Load Type 0%-100% of the new

load that requires ESM

Energy storage

module required

kWh ESM capacity

and charging amount

Converter

The flexibility options (Real Options) evaluated in this model include the option to install

additional PCM-X, PCM-1A, PCM-2A, ESM Charging Converters, and ESM battery modules.

The preparations required to activate this option are the provision of a dedicated electrical

distribution system compartment (volume) on board, a maximum rating of PDM that is initially

too large, and the ability of IPS to discharge PMM power for the load demand of the mission

system. Capacity limitations for flexibility options are based on this preparation and are shown

in Table 35.

Table 35: Capacity Limitations of Flexibility Options

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Capacity Type Flexibility Limits Value

Max PLM

Power

(MW)

Minimize the specific PMM Power required 72 MW (with minimum PMM

10 MW requirement)

Volume (m3) Adjustable space allocated for electricity

Distribution on ship shipping

50% improvement from base

Max PDM

Rating

(MW)

The number/size of cables (etc.) installed on

the vessel

Delivery

72 MW (a 350% increase

from

basic request)

Basic Case (With and Without Uncertainty):

The basic case is a deterministic model that represents a standard Navy design approach in

which propulsion, ship service, and mission system load are specified for the initial system

delivery conditions, and all desired future growth is accounted for with a standard 20% SLA. In

this way, the power distribution system is designed for the number of PCMs, PDMs, and in this

case there is no accommodation for ESM. The performance NPV and cost results from the base

case assume the results are exactly as predicted and indicate a cost-effective way to deliver the

required performance. However, the final outcome of the next 40 years is not easily predicted

and risks being affected by uncertainty.

The static base case was developed as an initial project model, without uncertainty, to

provide a deterministic analysis of the notional power distribution system. It is used to

investigate the direct relationship between input variables, model assumptions and constants,

and derived parameters. It assumes that the power distribution system for IPS electric boats

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designed for a 40-year service life is based on the propulsion power required for sustained

speed,

139

plus the ship's service power and mission system load requirements as described in the

Evaluation Scenario. The deterministic result is a system with a FPC Power Capacity Flexibility

performance value = 0.055 with an NPV cost of $9.07B. Of this $9.07 billion, BCC is

responsible for

$1.07 billion, and $71 million of BCC is attributed to the distribution system (about 7%).

Table 36: Fixed Input Parameters

Demand projections

Demand in year 1 (MW) (maximum

margin

power)

40-Year Power Demand Growth 20%

Load X Projected Mission (MW) 12.0

Future ESM Load (kWh) 0

Cost Parameters

Basic Construction Costs ($M) $1,003+(PCM Fee)+(ESM Fee)+(Vol Fee)+

(Fee

PDM)

Ship area addition fee ($/m3) USD $0,031

Delta of additional cable charges ($M) ((delta

MW)/0.85PF/13800V)/800A*$250/ft*775ft

O&S Fees ($M) 200+($3)*(3000Hours)/(6.8*(Fuel Consumption

Tariffs)/1000)

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Performance Parameters

Propulsion Power (Continuous Speed

30kt)

(MW)

Total installed power (MW) 82

Types of Architecture IP

Until MVAC

DST compartment (m3) 2,165

PCM1 – (kW DC:DC) 1000

PCM2 - (kW AC:DC) 500

PCMX –kW 500

kW Energy Storage Module 500

PDM (cable, junction, other) rating

(MW)

20.16

NPV

Time horizon (years) 40

Discount rate 0.5%

The basic case modeled with uncertainty supports the stochastic analysis of the value of

the power distribution system. The primary and secondary uncertainty parameters identified in

the Uncertainty section, above, reveal the risks inherent in static-deterministic models.

Figure 21 shows the cumulative distribution function (CDF) for Flexible Power Capacity, given

the uncertainty in future mission system loads, and Figure 22 shows the associated NPV. In each

cumulative distribution function, the red line indicates a deterministic case. In both aspects of

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the evaluation, the static case is in the favorable region of the possible outcome, on the right side

of the CDF curve. Most of the areas below the left curve of the deterministic line represent

uncertainty structured as risk. In other words, if a deterministic case is used to design a power

distribution system, there is a high chance that the system will have insufficient capacity.

142

Figure 21: Performance – Flexible Power Capacity (FPC) of CDF for a base case with

uncertainty

Basic Case with Uncertainty - Flexible Power Capacity Model Performance

Cumulative distirbution function

100. 0%

90.0%

80.0%

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

0. 0%

-2.5

Policies with

Uncertainty

Deterministic basis

2.0

1.5

1.0

0.5

Static Base Case with Uncertainty - Flexible Power Capacity (FPC) Performance

Probability that the realization value is less than

the target value

143

Figure 22: CDF net present value for the base case of a power distribution system with

uncertainty

Real Options:

In order to minimize the risks identified in the baseline case uncertainty analysis and

provide cost-effective options to improve the performance of the power distribution system, an

evaluation of flexible design options is conducted. Focusing on the uncertainty of the load of the

mission system, the Real Options model was developed to identify the preparations needed to

enable future growth in the capacity of the power distribution system, while minimizing the

impact on NPV costs.

In the case of the Real Option, the vessel platform includes the preparation of flexibility on

the initial shipment to provide additional unused volumes dedicated to the growth of the

electricity distribution system and

upgraded power distribution modules, including cables sized for future electrical loads greater

than the PLM load of initial mission conditions. The cost to include this upfront preparation is

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It is taken into account in the basic construction cost in year zero. Otherwise, in the initial

system delivery, the number and type of PCM are the same as in the basic case.

As in the basic case with uncertainty, the source of the load uncertainty of the mission

system is modeled to show the variability in the demand for the load of power distribution, and

its associated impact on the derived parameters. However, unlike the base case, the Real Options

case with flexibility is enabled by the decision model to determine the time, magnitude, and

variation of the capacity of additional PCM and ESM modules to be added to the system.

The decision model evaluates the actual electrical load demand against capacity over the

previous 4 periods to determine whether the available flexibility options should be realized. The

decision to expand the system by adding PCM and ESM depends on the capacity of the

remaining initial preparation: volume, capacity of the Power Distribution Module and capacity

of the Drainable Power Generation Module from propulsion. Two Real Options cases are

presented below to evaluate the impact of decision module variables on capacity expansion

rates. Real Option 1 is intended to provide adequate power capacity as demand increases, while

minimizing the cost impact by installing only the desired number of PCMs and ESMs to match

current demand. Real Option 2 is intended to maximize the value of system performance by

anticipating trends in increasing mission loads and taking advantage of the availability of

selected vessels to install additional capacity (double the current increase in demand loads). In

both cases, the capacity limit is kept constant as shown in Table 35. The cumulative distribution

function of performance and NPV for this Real Options case, compared to the base case, is

shown below in Figure 23 and Figure 24.

Real Option Implementation - Performance Cumulative distribution

function Flexible Power Capacity

Deterministic Basic

Base with Real

Option Uncertainty

Real Option 2

0. 3

0. 2

0. 1

-2.5-2.0-1.5-1.0-0.50. 0 0.

Real Option Implementation - Performance Flexible Power Capacity (FPC)

Probability that the realization value is less than

the target value

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Figure 23: Performance – CDF Flexible Power Capacity (FPC) for Real Options

The cumulative distribution function of the performance model shows the maximum FPC

value that each architecture can achieve. The deterministic base case shows a reference point

for the initial architecture assuming there is no uncertainty for future growth in the mission

system load beyond the planned 20% SLA. The basic case with uncertainty suggests that the

likelihood of

146

poor performance and significant loss of system value. The case of real options uses preparation

in design to allow decision-makers to respond to uncertainty when it is realized and minimize

the possibility of poor performance. This is represented by a "shift" of the CDF curve to the

right, and an increase in the maximum FPC outside the deterministic case.

Figure 24: Net present value of CDF for Real Option power distribution system

The cumulative distribution function of the NPV model shows the potential lifecycle

cost of each architecture, compared to the deterministic base case. The static base case is shown

as a lower cost option, however, we know from the performance model that it is not possible to

provide adequate system value over time because the disruption in the load requirement is

realized, as represented by the base case with uncertainty. The case of real options requires

additional acquisition costs upfront to include preparation in the design that allows decision-

makers to respond to future uncertainties. It is important to note that while NPV reflects the

total life cycle cost, the preparation cost for Real Option Case 2 is only $70 million (6.5%)

greater than the base case BCC. Although this is a relatively small contribution to the overall

147

NPV cost, which is greater than $9 billion, the power distribution system itself is only 6.7% of

the ship's total BCC.

Discussion:

This Real Options Analysis model shows the value of flexible design options in terms of

Flexible Power Capacity and NPV performance. Designing a distribution architecture to

accommodate modular power conversion modules with a wide range of voltages and capacities,

allowing the option to add PCM and ESM as the load demands of unknown mission systems

increase in the future, and the decision model criteria are met. The preparations required to

activate this option are the provision of a dedicated electrical distribution system compartment

(volume) on board, a maximum rating of PDM that is initially too large, and the ability of IPS to

discharge PMM power for the load demand of the mission system. Capacity limitations for

flexibility options are based on this preparation. In the exploration of design space, variations in

148

Preparation variables and modeling of additional uncertainty parameters will provide designers

with insight into the feasibility and dominance of alternative power and energy system

architectures.

Results from the two Real Option cases show that both flexible architectures will improve

system performance over the base case, with Real Option 2 providing a 65% improvement over

the base case, and positive power flexibility to accommodate future high-energy mission system

loads. While the distribution system did not generate the gains to counter the upfront costs and

annual expenses identified in the NPV analysis, the project results showed the feasibility of

achieving the desired performance capabilities with a 6.5% increase in initial capital

expenditures and a 1.4% increase over the life of the system. These results demonstrate the

ability to achieve significant system performance improvements at relatively low system costs

through the implementation of flexible design options.

7Conclusion

This thesis presents a framework for outlining ility-based requirements into metrics to

identify the dominant architecture in the early-stage design trading space. Ilities are defined as

emerging system properties that affect the system's ability to retain value over time.

Ilities are not a primary functional requirement, as defined in the Initial Capability Document or

the Capability Development Document that defines the system's objectives, but rather attributes

used to measure the system's ability to respond to change. Research from a robust literature

review of these system systems, their relationships, and methods for distinguishing between

preferred solutions in the design trade space was used to develop a hierarchy of "ility"

relationships for naval power and energy systems.

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The framework for design space exploration considers the physical, logical, and operational

aspects of architecture to produce a set of disruptions that are likely to impact

the ability of the system to retain value throughout its life cycle. Disruption is a mechanism or

influence on a system that requires change. The thesis focuses on the application of the

framework for the design of multidisciplinary naval power and energy systems, which is

responsible for the flow of energy across mechanical, electrical, thermal, and signal domains.

For specific interests, a comprehensive set of potential disturbances affecting emerging

system properties should be identified and correlated with preparation in design. A viable

solution design space should be filled with options that meet functional and ility requirements,

based on the initial identification of design preparation. This preparation can be broken down

into its basic attributes in the physical, logical, and operational appearance of the system. Finally,

design metrics to measure the value of a system under the influence of changes caused by a

given perturbation can be generated by linking independent and dependent variables to the

identified system attributes.

This thesis implements a framework for developing a measure of flexibility of power and

energy systems; This specific ility is selected based on the frequency of its occurrence in

150

a literature review and interest in the wider naval design community. Flexibility is defined as the

ability of a system to accommodate changes in response to disruptions in requirements. For

naval power and energy systems, flexibility is measured within system boundaries, in response

to interference from new and changing loads requiring power or changes to energy flow sources.

Four case studies were conducted to develop metrics for Flexible Power Capacity, Removable

Power Flexibility, Distributable Power Flexibility, and Energy Storage Flexibility. The fifth case

presents the application of Real Options Analysis to balance system performance and cost for the

"right size" of the P&E system on the initial delivery with the inclusion of preparation in the

design to react to future uncertainties.

The maturation of development mission system technology with new and increasing demand

for electrical power drives requirements for emerging properties, beyond typical functional

requirements. The U.S. Navy's surface fleet currently faces challenges related to the pace of

technological change and the uncertainty of future combat systems, and significant investment

costs to design and build a new class of ships. Uncertainty affects the system's ability to maintain

mission relevance affordably in an evolving operational context. Affordability constraints in the

Navy's acquisition environment, and the timeline for designing new and modified classes of

ships, emphasized the need to make informed decisions in early-stage design. This work is

intended to present a repeatable process for developing metrics that can be integrated in early-

stage design tools to create and evaluate naval power and energy systems. The application of

traditional and new methods of determining metrics, and implementation in design tools such as

Smart Ship System Design (S3D), will allow system architects to quickly assess a large number

of potential solutions and quickly characterize the cost versus capability sacrifice of discrete

architectural features.

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The views expressed here are the personal opinions of the author and are not necessarily the

official views of the Department of Defense or any military department.

7.1 Future Jobs

This research has identified several opportunities for further action in the development of

ility-based design requirements standards and further application of the design framework. A

deeper dive into the Department of Defense's requirements and acquisition process can identify

means to directly link ilities to threshold requirements and objective capabilities and bring

greater attention to their value in the programmatic requirements and decision-making and

requirements communities.

The design framework should be further implemented in other technical domains, beyond

power and energy systems, and in the application of additional non-functional requirements, to

better understand trends in the exploration of design spaces and the relationships between areas

of interest.

152

The Navy and the academic community should strive to validate and implement the metrics

presented here for power and energy system flexibility in the Smart Ship System Design (S3D)

program and integrate with standard early-stage design tools in the Leading Edge Architecture

for Prototyping Systems (LEAPS) toolkit, including interfaces to the Rapid Ship Design

Environment (RSDE).

Finally, the Navy has the opportunity to implement this design framework, including the

P&E system flexibility metric, in its current design and acquisition program for its next-

generation large surface combatant, DDG(X).

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