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geosciences
Article
Measuring Beach Profiles along a Low-Wave Energy Microtidal Coast, West-Central Florida, USA Jun Cheng *, Ping Wang and Qiandong Guo
School of Geosciences, University of South Florida, Tampa, FL 33620, USA; [email protected] (P.W.); [email protected] (Q.G.) * Correspondence: [email protected]
Academic Editors: Yongwei Sheng and Jesus Martinez-Frias Received: 4 July 2016; Accepted: 11 October 2016; Published: 19 October 2016
Abstract: Monitoring storm-induced dramatic beach morphology changes and long-term beach evolution provides crucial data for coastal management. Beach-profile measurement using total station has been conducted along the coast of west-central Florida over the last decade. This paper reviews several case studies of beach morphology changes based on total-station survey along this coast. The advantage of flexible and low-cost total-station surveys is discussed in comparison to LIDAR (light detection and ranging) method. In an attempt to introduce total-station survey from a practical prospective, measurement of cross-shore beach profile in various scenarios are discussed, including: (1) establishing a beach profile line with known instrument and benchmark locations; (2) surveying multiple beach profiles with one instrument setup; (3) implementation of coordinate rotation to convert local system to real-earth system. Total-station survey is a highly effective and accurate method in documenting beach profile changes along low-energy coasts.
Keywords: beach profile; beach erosion; GPS; total-survey station; west-central Florida
1. Introduction
Beach erosion is a serious concern for coastal countries throughout the world [1–3]. Beach nourishment has become one of the most commonly used methods to mitigate beach erosion [4]. Physical monitoring of site-specific morphology following nourishments are essential to quantify and predict nourishment performance, gain a more complete understanding of the underlying causes of beach erosion, and improve project design [5]. Various methods have been applied to monitor the beach profile changes, including direct measurement using GPS-RTK(Global Positioning System-Real Time Kinematic) [6], total survey station [7], as well as remote sensing methods such as coastal imaging [8] airborne LIDAR (light detection and ranging) [9], and Unmanned Aerial Vehicles (UAVs) for coastal surveying [10].
With recent development of video-imaging technology, high-performance cameras have been applied to measure nearshore bathymetry and sandbar movements [11]. Generally, the shallow bar crest appears bright in the image due to foam generated by breaking waves, while deeper offshore and trough areas are dark due to the absence of wave breaking [12] ). Therefore, the location of bar crests can be identified from video images due to the close correlation between the main breaker lines with the crests of the sandbars [13]. The great advantage of video imaging is its much higher temporal resolution [14]. However, applications of video imaging along low-energy coast, (e.g., the coast of Gulf of Mexico and the Great Lakes) can be limited because significant waves breaking over sandbars occur only during energetic conditions. No apparent wave breaking occurs over the bar crests under typical conditions, making bar identification via breaking-induced foam practically impossible.
LIDAR is another remote sensing method in coastal morphology survey. Although LIDAR is capable of efficient and reasonably accurate characterization of the beach morphology with high spatial
Geosciences 2016, 6, 44; doi:10.3390/geosciences6040044 www.mdpi.com/journal/geosciences
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resolution [15], such surveys are typically conducted rather infrequently due to high cost and the prior required careful organization [16]. Therefore, LIDAR data tend to have high spatial resolution but poor temporal resolution, which limits their applications for time-series morphology analysis. In addition, turbidities and air bubbles generated by wave breaking can induce large uncertainties for LIDAR measurements, limiting its ability to conduct accurate measurements in the surf zone and over nearshore bars, where frequent changes occur due to active sediment transport. More recently, Unmanned Aerial Vehicles (UAVs) have been developed and widely adopted in beach surveying to obtain high-resolution data at much lower costs than airborne LIDAR [10,17]. The disadvantage of UAVs is that they mostly focus on subaerial beach and dune, and therefore the subaqueous portion of the beach is not adequately resolved.
As compared to airborne LIDAR and UAV surveys, total-station surveys using the principles of level and transit represent a much less costly and more feasible survey method. This method has been successfully applied along the low-energy west-central Florida coast, where wave energy is typically low and water is warm [18–21]. A beach profile spaced at 300 m has been surveyed monthly to bi-monthly since the completion of a beach nourishment project in 2006. Adequate spatial and temporal coverage of beach-profile monitoring is critical for evaluating the performance of the nourishment project [22,23]. Dense spatial coverage is necessary to identify localized erosional hot spots and to ensure the beach nourishment design adequately addresses them. Adequate temporal coverage is needed to accurately document beach changes and to investigate the causes of the changes. In addition, the long-term measured beach profiles allow for reliable estimates of background rates of beach erosion and accretion. Because of the largely unpredictable nature of extreme storms, it is difficult to plan and execute pre-storm field data collection. This problem can be resolved by regularly surveying the beach profiles (e.g., bimonthly or quarterly). The existence of pre-storm data makes it possible to quantify the dramatic morphological impact of storms as well as post storm recovery [24]. The pre-storm profile survey, for instance, was completed two weeks prior to the Tropical Storm Debby in 2012 [21]. The accurate pre- and post-storm beach profiles are valuable for various agencies to estimate the exact volume of sand lost during storms for emergency management [25].
The accuracy of total station surveying has been examined by researchers monitoring beach profile changes [7]. The procedure of conducting the survey, however, has not been well documented. This may prevent a wide application of this survey method, especially when unforeseen complications occur in the field. The purpose of this paper is to provide relatively detailed instructions on conducting beach-profile surveys using total station, using west-central Florida coastal as an example. The paper is organized as follows: the study area is described in Section 2, followed by methodology in Section 3. The results are presented in Section 4, and the conclusions in Section 5.
2. Study Area
The west-central Florida coast is composed of a chain of barrier islands (Figure 1). Sand Key, the longest barrier island along this coast [26], is bound to the north by Clearwater Pass inlet and separated to the south from Treasure Island by John's Pass inlet. Both inlets are mixed-energy with large ebb-tidal deltas [27]. Complex tidal inlet processes have significant influences on beach morphodynamics at the two ends of the barrier island [19,28]. The Sand Key barrier island has an overall shoreline orientation change of 65◦ from northwest-facing to southwest-facing beaches, controlled by the antecedent geology (Figure 1). The stabilized wave-dominated migratory Blind Pass [28] inlet separates Treasure Island to the north and Long Key to the south. Long Key is bound to the south by Pass-A-Grille inlet, which is one of the inlets entering the greater Tampa Bay. A large portion of Sand Key, northern and southern end of Treasure Island, as well as northern end of the Long Key have been identified by the Florida Department of Environmental Protection as sites where critical erosion is currently occurring. In order to mitigate the erosion, most of beach has been nourished every six to eight years, with the most recent beach nourishment implemented in 2012.
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Figure 1. Study area along the coast of west-central Florida.
The west-central Florida coast has a mixed tide regime, with spring tides typically diurnal with a 1 m tidal range while neap tides are semi-diurnal with a range of about 0.4 m. The wave energy is generally small along the west-central Florida coast, with averaged nearshore significant wave height of less than 0.3 m [28]. Waves are typically sea-type generated by local winds (Figure 2A). Higher waves are often associated with the passages of cold fronts every couple of weeks during the winter and the occasional passages of tropical storms (Figure 2B). Highly oblique waves generated by the post-frontal northerly winds result in more active southward longshore sediment transport as compared to the northerly transport by the predominant southerly approaching smaller waves. This results in a net annual southward longshore sediment transport [29].
(A) (B)
Figure 2. Study area under normal weather condition (A), as well as under Tropical Storm Debby in 2012 (B).
The track and landfall location of Tropical Storm (TS) Debby was several hundred km north of the study area. However, due to the very large size of the storm and the slow speed of the system, TS Debby induced significant impact to the study area. The high storm waves superimposed on the elevated water level reached the toe of dunes and impacted various sections of seawall (Figure 2B). A character of TS Debby is that the prolonged high wave and strong wind approached from the south, opposite to the net southward longshore transport, for over three days.
Figure 1. Study area along the coast of west-central Florida.
The west-central Florida coast has a mixed tide regime, with spring tides typically diurnal with a 1 m tidal range while neap tides are semi-diurnal with a range of about 0.4 m. The wave energy is generally small along the west-central Florida coast, with averaged nearshore significant wave height of less than 0.3 m [28]. Waves are typically sea-type generated by local winds (Figure 2A). Higher waves are often associated with the passages of cold fronts every couple of weeks during the winter and the occasional passages of tropical storms (Figure 2B). Highly oblique waves generated by the post-frontal northerly winds result in more active southward longshore sediment transport as compared to the northerly transport by the predominant southerly approaching smaller waves. This results in a net annual southward longshore sediment transport [29].
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Figure 1. Study area along the coast of west-central Florida.
The west-central Florida coast has a mixed tide regime, with spring tides typically diurnal with a 1 m tidal range while neap tides are semi-diurnal with a range of about 0.4 m. The wave energy is generally small along the west-central Florida coast, with averaged nearshore significant wave height of less than 0.3 m [28]. Waves are typically sea-type generated by local winds (Figure 2A). Higher waves are often associated with the passages of cold fronts every couple of weeks during the winter and the occasional passages of tropical storms (Figure 2B). Highly oblique waves generated by the post-frontal northerly winds result in more active southward longshore sediment transport as compared to the northerly transport by the predominant southerly approaching smaller waves. This results in a net annual southward longshore sediment transport [29].
(A) (B)
Figure 2. Study area under normal weather condition (A), as well as under Tropical Storm Debby in 2012 (B).
The track and landfall location of Tropical Storm (TS) Debby was several hundred km north of the study area. However, due to the very large size of the storm and the slow speed of the system, TS Debby induced significant impact to the study area. The high storm waves superimposed on the elevated water level reached the toe of dunes and impacted various sections of seawall (Figure 2B). A character of TS Debby is that the prolonged high wave and strong wind approached from the south, opposite to the net southward longshore transport, for over three days.
Figure 2. Study area under normal weather condition (A), as well as under Tropical Storm Debby in 2012 (B).
The track and landfall location of Tropical Storm (TS) Debby was several hundred km north of the study area. However, due to the very large size of the storm and the slow speed of the system, TS Debby induced significant impact to the study area. The high storm waves superimposed on the elevated water level reached the toe of dunes and impacted various sections of seawall (Figure 2B). A character of TS Debby is that the prolonged high wave and strong wind approached from the south, opposite to the net southward longshore transport, for over three days.
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The water level variations measured at NOAA Clearwater Beach Tide Station, about 5 km north of the study area, illustrated a sustained storm surge of up to 1.0 m for three days. The peak significant wave height observed at NOAA’s NDBC station 42099 approached dominantly from the south with the highest wave reaching nearly 6 m, and a peak wave period of 10 s. Driven by the strong southerly wind, a northward directed longshore current and sediment transport was observed during the storm. In general, TS Debby generated waves that were four to five times higher than the average wave conditions along this coast, and with a much longer wave period of roughly 10 s versus the average period of 5 s.
3. Methodology
3.1. Measurement under Regular Conditions
Beach-profile surveying using Topcon total station follows the traditional level-and-transit principle and typically requires three people, with one instrument person, one rod-person responsible for the land part of the survey, and one swimmer for the ocean part of the survey (Figure 3). For this case, a 4-m survey rod is used. It is worth noting that a flat footer is attached to the bottom of the survey rod (Figure 3, left panel), instead of the typical pointy footer. A flat footer prevents the survey rod from sinking into the soft sand to ensure accuracy of the measurement. Field observations indicate that the sharp pointy ends can often penetrate into the sand for 5–10 cm.
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The water level variations measured at NOAA Clearwater Beach Tide Station, about 5 km north of the study area, illustrated a sustained storm surge of up to 1.0 m for three days. The peak significant wave height observed at NOAA’s NDBC station 42099 approached dominantly from the south with the highest wave reaching nearly 6 m, and a peak wave period of 10 s. Driven by the strong southerly wind, a northward directed longshore current and sediment transport was observed during the storm. In general, TS Debby generated waves that were four to five times higher than the average wave conditions along this coast, and with a much longer wave period of roughly 10 s versus the average period of 5 s.
3. Methodology
3.1. Measurement under Regular Conditions
Beach-profile surveying using Topcon total station follows the traditional level-and-transit principle and typically requires three people, with one instrument person, one rod-person responsible for the land part of the survey, and one swimmer for the ocean part of the survey (Figure 3). For this case, a 4-m survey rod is used. It is worth noting that a flat footer is attached to the bottom of the survey rod (Figure 3, left panel), instead of the typical pointy footer. A flat footer prevents the survey rod from sinking into the soft sand to ensure accuracy of the measurement. Field observations indicate that the sharp pointy ends can often penetrate into the sand for 5–10 cm.
Figure 3. Survey procedures include the use of an electronic level-and-transit total station and a 4 m survey rod
Prior to the total-station survey, GPS-RTK is typically used to acquire the accurate locations of the instrument and benchmark, from which the azimuth of survey lines can be computed. The instrument and benchmark points are usually established perpendicular to the shore line in order to obtain a cross-shore beach profile. Two orange cones visible to the rod-person are set on the survey line to help the rod-person to remain on the survey line (Figure 3). This is much more efficient than having the instrument person direct the rod-person to stay on line, although slight error may be introduced by the visual estimate of the rod-person. The positions of the instrument and benchmark are established by knocking short wood sticks or PVC pipes into the sand, typically in the dune field, where it is far away from anthropogenic disturbance and has low odds of being eroded away. These two semi-permanently established points allow for subsequent surveys to re-occupy the same line for temporal comparisons. It is important to note that the elevation of the benchmark needs to be stable and carefully measured using GPS-RTK, as it provides elevation control for the entire survey line and for temporal comparisons.
When executing the beach survey, the total station typically requires three input parameters as follows: (1) the location of the instrument in order to specify where the total survey station is placed; (2) the azimuth of the survey line—based on these two parameters, the location of the survey points
Figure 3. Survey procedures include the use of an electronic level-and-transit total station and a 4 m survey rod.
Prior to the total-station survey, GPS-RTK is typically used to acquire the accurate locations of the instrument and benchmark, from which the azimuth of survey lines can be computed. The instrument and benchmark points are usually established perpendicular to the shore line in order to obtain a cross-shore beach profile. Two orange cones visible to the rod-person are set on the survey line to help the rod-person to remain on the survey line (Figure 3). This is much more efficient than having the instrument person direct the rod-person to stay on line, although slight error may be introduced by the visual estimate of the rod-person. The positions of the instrument and benchmark are established by knocking short wood sticks or PVC pipes into the sand, typically in the dune field, where it is far away from anthropogenic disturbance and has low odds of being eroded away. These two semi-permanently established points allow for subsequent surveys to re-occupy the same line for temporal comparisons. It is important to note that the elevation of the benchmark needs to be stable and carefully measured using GPS-RTK, as it provides elevation control for the entire survey line and for temporal comparisons.
When executing the beach survey, the total station typically requires three input parameters as follows: (1) the location of the instrument in order to specify where the total survey station is placed;
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(2) the azimuth of the survey line—based on these two parameters, the location of the survey points can be computed using its angle and distance with respect to the position of the benchmark, which is automatically calculated by the internal processor of most modern total station—and lastly; (3) the height of the instrument and length of the survey rod. The first measurement is typically a “backshot” to the benchmark. It is important to compare the total station readings to the known benchmark location and elevation. This ensures that no mistakes are made during the setup of the instrument.
For this study, the surveys were conducted using NAD83 State Plane (Florida West 0902) coordinate system in meters. Other coordinate systems (e.g., UTM) can also be used. The elevations are referenced to NAVD88 in meters. NAVD88 zero is 8.2 cm above mean sea level (MSL) in our study area. The survey lines extend to roughly −3 m NAVD88, or to the short-term closure depth in this area [30]. The usually small waves allow the rod-person to hold the rod steady in the water to ensure the accuracy of the survey data. Instead of taking survey points with uniform fixed space intervals, which may miss crucial features such as scarps or bar crests, the rod-person decides the point location with the goal of capturing all important topographic changes. Typically, denser points are taken where slope changes occur (e.g., foreshore, berm crest, sandbar etc.), and less dense points are taken where topography is uniform (e.g., flat back beach). This procedure allows efficient measurement of the beach-profile changes.
3.2. Measurement under Special Conditions
The procedure described in the previous section works efficiently under typical well-controlled conditions. However, complications may occur in the field. This section describes several methods to ensure efficient and accurate data collection. When the adjacent survey lines are close to each other, it is efficient to survey multiple lines with one instrument setup. This requires that the equipment be set up at a position that is visible to the nearby profiles. The instrument point can be reset at a temporal location (e.g., around the berm crest of the beach; see the position of Instrument 2 in Figure 4). This temporary instrument point can be obtained by surveying from the original instrument location. This one-setup survey of multiple lines can save considerable amount of time by eliminating several instrument setup.
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can be computed using its angle and distance with respect to the position of the benchmark, which is automatically calculated by the internal processor of most modern total station—and lastly; (3) the height of the instrument and length of the survey rod. The first measurement is typically a “backshot” to the benchmark. It is important to compare the total station readings to the known benchmark location and elevation. This ensures that no mistakes are made during the setup of the instrument.
For this study, the surveys were conducted using NAD83 State Plane (Florida West 0902) coordinate system in meters. Other coordinate systems (e.g., UTM) can also be used. The elevations are referenced to NAVD88 in meters. NAVD88 zero is 8.2 cm above mean sea level (MSL) in our study area. The survey lines extend to roughly −3 m NAVD88, or to the short-term closure depth in this area [30]. The usually small waves allow the rod-person to hold the rod steady in the water to ensure the accuracy of the survey data. Instead of taking survey points with uniform fixed space intervals, which may miss crucial features such as scarps or bar crests, the rod-person decides the point location with the goal of capturing all important topographic changes. Typically, denser points are taken where slope changes occur (e.g., foreshore, berm crest, sandbar etc.), and less dense points are taken where topography is uniform (e.g., flat back beach). This procedure allows efficient measurement of the beach-profile changes.
3.2. Measurement under Special Conditions
The procedure described in the previous section works efficiently under typical well-controlled conditions. However, complications may occur in the field. This section describes several methods to ensure efficient and accurate data collection. When the adjacent survey lines are close to each other, it is efficient to survey multiple lines with one instrument setup. This requires that the equipment be set up at a position that is visible to the nearby profiles. The instrument point can be reset at a temporal location (e.g., around the berm crest of the beach; see the position of Instrument 2 in Figure 4). This temporary instrument point can be obtained by surveying from the original instrument location. This one-setup survey of multiple lines can save considerable amount of time by eliminating several instrument setup.
Figure 4. Example of survey line including instrument and benchmark.
Sometimes sea oats or trees grow in between the previously established instrument and benchmark points, particularly during the summer season. This blocks the line of sight from the instrument point to the benchmark point. Another complication can be caused by the occurrence of severe erosion around original instrument point which may make it impossible to set up the instrument there. In order to execute the total-station survey under these complications efficiently without re-establishing the line using GPS-RTK, the instrument can be set off the survey line in a place where the line of sight is not obstructed, for instance, at the location of “Instrument 3” in Figure
Figure 4. Example of survey line including instrument and benchmark.
Sometimes sea oats or trees grow in between the previously established instrument and benchmark points, particularly during the summer season. This blocks the line of sight from the instrument point to the benchmark point. Another complication can be caused by the occurrence of severe erosion around original instrument point which may make it impossible to set up the instrument there. In order to execute the total-station survey under these complications efficiently without re-establishing the
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line using GPS-RTK, the instrument can be set off the survey line in a place where the line of sight is not obstructed, for instance, at the location of “Instrument 3” in Figure 4. Arbitrary values for the instrument location and azimuth can be used in equipment setup. This local coordinate system can be later corrected during the data processing. In order to correct to the real earth coordinate system (e.g., NAD83), several control points (a minimum of two) need to be established using the GPS-RTK. The GPS-RTK measurements provide elevation control for the total-station survey. In addition, the GPS-RTK measured locations also provide reference points for shifting and rotating the local total-station coordinates to real-earth system. The coordinate system rotation can be conducted using the following formulas:
[ x′
y′ ] =
[ cosθ −sinθ sinθ cosθ
] [
x y ] (1)
where θ is the angle between the GPS-RTK measured line and the total station measured line. θ can be computed from the angle between the two vectors (F1 and F2),
θ = cos−1( F1 · F2 |F1| |F2|
) (2)
It is worth noting again that both the total-station measured points and GPS-RTK measured points need to be shifted first to an origin (0, 0) before applying Equation (1). After the rotation, the origin of the rotated total-station points should be shifted again to the corresponding GPS-RTK coordinate system.
4. Results and Discussion
4.1. Beach-Profile Changes in Seasonal-Annual Scale
As the main purpose of this paper is to present an efficient method of conducting beach profile surveys using electronic total-station surveys, the analysis of the survey data (beach profiles) is not the major focus. Beach-profile changes since the completion of the most recent nourishment in August 2012 up to August 2015 were examined to illustrate the capability of total-station surveys in documenting beach evolution.
The North Sand Key project area spans a 3 km distance in the North Sand Key (Figure 1). The constructed berm in this project area was wider than in the areas to the south, at approximately 60 m wide. A divergence in longshore sediment transport occurs in this project area caused by the wave refraction over the Clearwater Pass ebb shoal [19]. The divergence of sediment transport has resulted in an erosional hotspot along a stretch of beach between R59 and R61. An example profile, R61, located within this divergence zone is shown in Figure 5. Although the beach-profiles were surveyed monthly to bi-monthly, for the clarity of the figure, only two beach profiles per year representing summer and winter seasons are presented. The beach nourishment along this section of the beach was completed in August 2012. As apparent in Figure 5, substantial beach erosion (approximately 40 m) occurred during the first three years post nourishment, from August 2012 to August 2015. The entire beach-nearshore profile shifted landward, indicating erosion due to negative longshore transport gradient. Approximately two thirds of the dry beach width was lost during the three years after the nourishment. The rate of dry beach loss decreased considerably with time. This profile, R61, represents the largest profile-volume loss along this stretch of the beach. The adjacent profiles lost less volume and shoreline as compared to R61.
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Figure 5. Example profile from the North Sand Key project area at R61.
The municipality of Belleair Shore, just south of the North Sand Key project area (Figure 1), opted out of the 2012 nourishment, providing an opportunity to monitor longshore spreading from the nourishment. An example profile, R67 located approximately 300 m south of the North Sand Key nourishment area, is shown in Figure 6. The beach above 1.5 m NAVD88 remained stable from August 2012 to August 2015. However, the lower beach and the nearshore zones gained considerable amount of sand (exact volume and shoreline gain will be discussed in detail in the following section), apparently from the nourishment just to the north. Most of the gains occurred shortly after the nourishment in 2012. A nearshore bar is rather distinctive at this profile location during most of the study period. An offshore migration of the bar, which is a typical occurrence during winter seasons, was measured during the study period from August 2012 to February 2013. The volume gain in the intertidal area may have contributed to the offshore bar migration. The bar migrated onshore at the beginning of the summer from February to August 2013, also typical of the seasonal pattern of west- central Florida. In the following winter season, the bar migrated offshore, as expected.
Figure 6. Example profile from the area of no fill along Belleair Shore between North Sand Key and Indian Rocks, at R67.
Indian Rocks Beach is located just south of Belleair Shore and north of the headland (Figure 1). An example profile, R75 roughly in the middle of this section, is shown in Figure 7. The Indian Rocks
Figure 5. Example profile from the North Sand Key project area at R61.
The municipality of Belleair Shore, just south of the North Sand Key project area (Figure 1), opted out of the 2012 nourishment, providing an opportunity to monitor longshore spreading from the nourishment. An example profile, R67 located approximately 300 m south of the North Sand Key nourishment area, is shown in Figure 6. The beach above 1.5 m NAVD88 remained stable from August 2012 to August 2015. However, the lower beach and the nearshore zones gained considerable amount of sand (exact volume and shoreline gain will be discussed in detail in the following section), apparently from the nourishment just to the north. Most of the gains occurred shortly after the nourishment in 2012. A nearshore bar is rather distinctive at this profile location during most of the study period. An offshore migration of the bar, which is a typical occurrence during winter seasons, was measured during the study period from August 2012 to February 2013. The volume gain in the intertidal area may have contributed to the offshore bar migration. The bar migrated onshore at the beginning of the summer from February to August 2013, also typical of the seasonal pattern of west-central Florida. In the following winter season, the bar migrated offshore, as expected.
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Figure 5. Example profile from the North Sand Key project area at R61.
The municipality of Belleair Shore, just south of the North Sand Key project area (Figure 1), opted out of the 2012 nourishment, providing an opportunity to monitor longshore spreading from the nourishment. An example profile, R67 located approximately 300 m south of the North Sand Key nourishment area, is shown in Figure 6. The beach above 1.5 m NAVD88 remained stable from August 2012 to August 2015. However, the lower beach and the nearshore zones gained considerable amount of sand (exact volume and shoreline gain will be discussed in detail in the following section), apparently from the nourishment just to the north. Most of the gains occurred shortly after the nourishment in 2012. A nearshore bar is rather distinctive at this profile location during most of the study period. An offshore migration of the bar, which is a typical occurrence during winter seasons, was measured during the study period from August 2012 to February 2013. The volume gain in the intertidal area may have contributed to the offshore bar migration. The bar migrated onshore at the beginning of the summer from February to August 2013, also typical of the seasonal pattern of west- central Florida. In the following winter season, the bar migrated offshore, as expected.
Figure 6. Example profile from the area of no fill along Belleair Shore between North Sand Key and Indian Rocks, at R67.
Indian Rocks Beach is located just south of Belleair Shore and north of the headland (Figure 1). An example profile, R75 roughly in the middle of this section, is shown in Figure 7. The Indian Rocks
Figure 6. Example profile from the area of no fill along Belleair Shore between North Sand Key and Indian Rocks, at R67.
Indian Rocks Beach is located just south of Belleair Shore and north of the headland (Figure 1). An example profile, R75 roughly in the middle of this section, is shown in Figure 7. The Indian Rocks
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Beach area is an example representing the “typical” beach state along Pinellas County. During the winter months, the sandbar migrated offshore, followed by onshore migration during the summer months. Similar seasonal patterns have been documented by Brutsche et al. [20] and Roberts and Wang [19]. This is different from the general seasonal beach cycle [31,32], which is composed of wide gentle summer beach-berm and steep narrow winter beach.
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Beach area is an example representing the “typical” beach state along Pinellas County. During the winter months, the sandbar migrated offshore, followed by onshore migration during the summer months. Similar seasonal patterns have been documented by Brutsche et al. [20] and Roberts and Wang [19]. This is different from the general seasonal beach cycle [31,32], which is composed of wide gentle summer beach-berm and steep narrow winter beach.
Figure 7. Example profile from Indian Rocks, R80.
The project area “Headland” is termed so due to its location on the broad headland approximately in the middle of Sand Key, reflecting a shoreline orientation change of 65 degrees from northwest- to southwest-facing beaches (Figure 1). The headland project area extends from monuments R82 to R89 and is just over 2 km in length. An example profile, R84, is shown in Figure 8. The magnitude of beach-profile changes along the protruding headland is greater than that along the project area to the north. The offshore bar migration during the winter and onshore bar migration during the summer were also measured at the headland. Considerable landward berm crest (at approximately 1.5 m NAVD88) retreat occurred during the first a few years post the nourishment. Sand loss in the nearshore zone landward of the trough was also measured. Some of the sand eroded from the dry beach and was deposited on the nearshore bar, while some of the sand moved to the south driven by the net annual southward longshore transport.
Figure 8. Example profile from the Headland, R84.
Figure 7. Example profile from Indian Rocks, R80.
The project area “Headland” is termed so due to its location on the broad headland approximately in the middle of Sand Key, reflecting a shoreline orientation change of 65 degrees from northwest- to southwest-facing beaches (Figure 1). The headland project area extends from monuments R82 to R89 and is just over 2 km in length. An example profile, R84, is shown in Figure 8. The magnitude of beach-profile changes along the protruding headland is greater than that along the project area to the north. The offshore bar migration during the winter and onshore bar migration during the summer were also measured at the headland. Considerable landward berm crest (at approximately 1.5 m NAVD88) retreat occurred during the first a few years post the nourishment. Sand loss in the nearshore zone landward of the trough was also measured. Some of the sand eroded from the dry beach and was deposited on the nearshore bar, while some of the sand moved to the south driven by the net annual southward longshore transport.
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Beach area is an example representing the “typical” beach state along Pinellas County. During the winter months, the sandbar migrated offshore, followed by onshore migration during the summer months. Similar seasonal patterns have been documented by Brutsche et al. [20] and Roberts and Wang [19]. This is different from the general seasonal beach cycle [31,32], which is composed of wide gentle summer beach-berm and steep narrow winter beach.
Figure 7. Example profile from Indian Rocks, R80.
The project area “Headland” is termed so due to its location on the broad headland approximately in the middle of Sand Key, reflecting a shoreline orientation change of 65 degrees from northwest- to southwest-facing beaches (Figure 1). The headland project area extends from monuments R82 to R89 and is just over 2 km in length. An example profile, R84, is shown in Figure 8. The magnitude of beach-profile changes along the protruding headland is greater than that along the project area to the north. The offshore bar migration during the winter and onshore bar migration during the summer were also measured at the headland. Considerable landward berm crest (at approximately 1.5 m NAVD88) retreat occurred during the first a few years post the nourishment. Sand loss in the nearshore zone landward of the trough was also measured. Some of the sand eroded from the dry beach and was deposited on the nearshore bar, while some of the sand moved to the south driven by the net annual southward longshore transport.
Figure 8. Example profile from the Headland, R84. Figure 8. Example profile from the Headland, R84.
Geosciences 2016, 6, 44 9 of 12
North Redington Beach was the southern-most area nourished on Sand Key in 2012 (Figure 1). The North Redington Beach project extends from survey monuments R101 to R107, along a 2.1 km stretch of beach. An example profile, R105, is shown in Figure 9. The seasonal trend of offshore and onshore bar migration during the winter and summer season, respectively, was also measured at this profile. The beach at elevation of 1 m NAVD88 was eroded considerably during the first a few years post nourishment. The beach in the intertidal zone varied modestly without a clear trend of erosion or accretion. Overall, this profile did not have excessive sand loss, thereby suggesting that the end loss at the southern terminus of the nourishment project is not significant.
Geosciences 2016, 6, 44 9 of 12
North Redington Beach was the southern-most area nourished on Sand Key in 2012 (Figure 1). The North Redington Beach project extends from survey monuments R101 to R107, along a 2.1 km stretch of beach. An example profile, R105, is shown in Figure 9. The seasonal trend of offshore and onshore bar migration during the winter and summer season, respectively, was also measured at this profile. The beach at elevation of 1 m NAVD88 was eroded considerably during the first a few years post nourishment. The beach in the intertidal zone varied modestly without a clear trend of erosion or accretion. Overall, this profile did not have excessive sand loss, thereby suggesting that the end loss at the southern terminus of the nourishment project is not significant.
Figure 9. Example profile from North Redington Beach, R105.
4.2. Beach-Profile Change in Storm Scale
Several beach profiles surveyed before and after Tropical Storm Debby in 2012 are illustrated as an example of quantifying storm induced beach profile changes. Regarding storm induced beach- profile changes, beach-profiles surveyed prior and post Tropical Strom Debby in 2012 are discussed here. Although dune-beach-nearshore erosion was measured at nearly all the profile locations, different patterns of sand bar movement associated with the storm, including offshore migration, upward accretion, and onshore migration, were measured at different locations. At profile R80, located north of the headland (Figure 1), erosion was measured on the dry beach and in the nearshore region, while deposition was measured seaward of the nearshore bar, resulting in an offshore bar migration (Figure 10A). At beach profile R87, located on the headland (Figure 1), erosion in the beach- nearshore area and upward accretion of sandbar was measured (Figure 10B). The trend of bar movement was different from that north of the headland. At beach profile R105, located south of the headland (Figure 1), erosion in the beach-nearshore area and landward migration of sandbar was measured (Figure 10C). It is worth noting that most profiles mapped with the total station survey technique easily capture offshore profile convergence, as illustrated by three different profile locations within the study area. It is beyond the scope of this paper to discuss the mechanism of sandbar movement. The results on the sandbar migration are discussed in Roberts and Wang [19], Cheng et al. [33], and Cheng and Wang [34].
Figure 9. Example profile from North Redington Beach, R105.
4.2. Beach-Profile Change in Storm Scale
Several beach profiles surveyed before and after Tropical Storm Debby in 2012 are illustrated as an example of quantifying storm induced beach profile changes. Regarding storm induced beach-profile changes, beach-profiles surveyed prior and post Tropical Strom Debby in 2012 are discussed here. Although dune-beach-nearshore erosion was measured at nearly all the profile locations, different patterns of sand bar movement associated with the storm, including offshore migration, upward accretion, and onshore migration, were measured at different locations. At profile R80, located north of the headland (Figure 1), erosion was measured on the dry beach and in the nearshore region, while deposition was measured seaward of the nearshore bar, resulting in an offshore bar migration (Figure 10A). At beach profile R87, located on the headland (Figure 1), erosion in the beach-nearshore area and upward accretion of sandbar was measured (Figure 10B). The trend of bar movement was different from that north of the headland. At beach profile R105, located south of the headland (Figure 1), erosion in the beach-nearshore area and landward migration of sandbar was measured (Figure 10C). It is worth noting that most profiles mapped with the total station survey technique easily capture offshore profile convergence, as illustrated by three different profile locations within the study area. It is beyond the scope of this paper to discuss the mechanism of sandbar movement. The results on the sandbar migration are discussed in Roberts and Wang [19], Cheng et al. [33], and Cheng and Wang [34].
Geosciences 2016, 6, 44 10 of 12 Geosciences 2016, 6, 44 10 of 12
Figure 10. Pre- and post-storm surveyed beach profiles at (A) R80; (B) R87; and (C) R105.
5. Conclusions
Beach-profile surveys using total station have been conducted along the coast of west-central Florida for 10 years. Considerable longshore variations of beach-profile changes at a seasonal-annual scale were measured. Severe shoreline retreats occurred at an erosional hot spot at North Sand Key. The shoreline remains relatively stable at a “typical” beach profile, with bar migration in response to wave condition variations. At the storm temporal scale, various bar behaviors were measured including both onshore and offshore bar migration, as well as upward bar accretion.
Figure 10. Pre- and post-storm surveyed beach profiles at (A) R80; (B) R87; and (C) R105.
5. Conclusions
Beach-profile surveys using total station have been conducted along the coast of west-central Florida for 10 years. Considerable longshore variations of beach-profile changes at a seasonal-annual scale were measured. Severe shoreline retreats occurred at an erosional hot spot at North Sand Key. The shoreline remains relatively stable at a “typical” beach profile, with bar migration in response to wave condition variations. At the storm temporal scale, various bar behaviors were measured including both onshore and offshore bar migration, as well as upward bar accretion.
Geosciences 2016, 6, 44 11 of 12
An efficient field data collection method was developed and described here. It yielded a large and valuable dataset for conducting research on the mechanisms of beach erosion/accretion and sandbar migration, among other topics. Time-series beach-profile data allow quantitative evaluation of the performance of beach nourishment projects and help to optimize beach nourishment design, which is crucial for successful coastal management.
As compared to the increasingly applied airborne LIDAR survey method, the total station survey is more labor intensive in the field and with much lower spatial resolution. The various details discussed above, such as using marker cones to help the rod-person to follow the survey line and surveying multiple lines with one instrument setup, are aimed at improving the survey efficiency and subsequently the spatial coverage and resolution. Procedures of coordinate system rotation are discussed for the case in which a local coordinate system has to be used in field data collection.
The main advantages of total station survey include: (1) the ease of planning that allows for the efficient execution of storm-related data collection; (2) low costs that can lead to high temporal coverage and resolution; and (3) accurate data, especially in the dynamic surf zone, for detailed analysis of beach changes. The above advantages make the total station survey method an ideal tool for graduate research. Total-station survey is a practical and accurate method for documenting beach changes, especially for low wave-energy coasts.
Acknowledgments: This study was funded by Pinellas County, Florida and the University of South Florida. We also would like to thank several graduate and undergraduate students for their assistance in field data collection.
Author Contributions: Jun Cheng conceived the research, conducted the field measurement, analyzed the data and wrote the article. Ping Wang designed the research, reviewed the results and the article, Qiandong Quo helped in the discussion of the remote sensing technology in coastal surveying.
Conflicts of Interest: The authors declare no conflict of interest.
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30. Wang, P.; Davis, R.A. Depth of closure and the equilibrium beach profile—A case study from Sand key, west-central Florida. Shore Beach 1999, 67, 33–42.
31. Komar, P.D. Beach Processes and Sedimentation, 2nd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 1998. 32. Roberts, T.M.; Wang, P.; Puleo, J.A. Storm-driven cyclic beach morphodynamics of a mixed sand and gravel
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© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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For the second year in a row, Florida State University has been
named 2014’s “Most Efficient High Quality University in the
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energy conservation program was one of many key elements that
allowed the university to attain national recognition for efficiency.
HOW FLORIDA STATE UNIVERSITY ESTABLISHED AN EFFECTIVE ENERGY CONSERVATION PROGRAM
Use appropriate technology. Process simplification has been key in maintaining FSU’s status
as a national leader in conservation. In recent years, well
intentioned design teams have specified every type of energy
reduction technology conceivable without fully considering the
consequences. The problem is that some technology is very
difficult to maintain over the life of the system. A design
philosophy that only puts in what equipment is necessary,
reliable and maintainable is the only way to assure that project
return on investment meets the target.
Plan and collaborate. At FSU, we believe in long-range capital planning. If a project
within a facility can be integrated into other planned projects, it
meets multiple needs and potentially gets more done for fewer
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& Engineering Services, and Facilities Maintenance teams all
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team has a replacement project planned, for example, and an
energy project is planned for the same building, the project
scopes are compared to see if there is a way to drive value.
Use all of your tools. Establishing trust between an energy partner and university
continues to be critical. If cost and savings are not understood,
the return on investment of any project can not be accurately
determined. When an institution utilizes an energy services
provider, it is necessary to select an organization that shares
your goals and values. Good or bad, your institution has to live
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“Energy conservation is a process, not a project.”
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shares best practices for achieving this mission
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solar energy in FL.pdf
___________________________Journal of Multidisciplinary Research___________________________
25
Journal of Multidisciplinary Research, Vol. 12, No. 2, Fall 2020, 25-40. ISSN 1947-2900 (print) • ISSN 1947-2919 (online) Compilation Copyright © 2020 by St. Thomas University. All rights reserved.
Simulating Utility-Scale Solar Energy Profitability in Florida
Arnel Garcesa
Florida State University
and
Crystal Taylor Florida State University
Abstract
Is solar energy an economically competitive option for Floridian consumers? By 2028,
Florida Power and Light (FPL), the leading electricity provider for the State of Florida, projects
to source nearly 15% of its electricity supply from solar power. As an Investor-Owned Utility
serving the electricity needs of approximately half of Floridians, policy changes to its energy
portfolio greatly impact consumers through base rate increases to subsidize the technological
transition. The present study investigates the potential profitability of FPL’s solar projects by
conducting a cost-benefit analysis (CBA) and a sensitivity analysis to determine how varying
generation parameters impacts profits. This research simulates 110,880 CBA scenarios with only
2.4% of scenarios showing profit while holding base rates constant. Among scenarios with a
time-period of 30 years, this study finds approximately 19.5% are profitable. However, this study
shows that FPL’s solar projects are profitable, but likely not within the regulatory rate of return.
Simulations in this study show less than 1% of scenarios are profitable. This research suggests
that consumer base rate increases are likely to enable FPL to achieve its allowable rate of return
for its future solar projects.
Keywords: solar energy, utilities, renewable energy, electricity, energy pricing, sensitivity
analysis, cost-benefit analysis
___________________________Journal of Multidisciplinary Research___________________________
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Introduction
Florida’s population is growing and with that growth comes with an increase in consumer
energy needs from utility providers. An estimated 21.1 million people lived in the state of
Florida as of April 2019 (Florida Office of Economic and Demographic Research, 2019b), and
that number could increase to between 23 and 24 million in 10 years (Florida Office of
Economic and Demographic Research, 2018). As a result, utility-scale energy providers will
need to properly plan for the growing electricity needs of current and future residents. Florida
residents consume an overwhelming majority of their electricity from natural gas, followed
distantly by nuclear, coal plants, and nonhydroelectric renewable sources (U.S. Energy
Information Administration, 2019). Currently, the State’s energy generation profile has a much
smaller percentage of electricity from renewable alternatives than nonrenewable sources. As
Florida’s present leading renewable energy source (Florida Public Service Commission, 2018),
experts predict solar energy will grow significantly in the coming decades. The Florida Public
Service Commission (FPSC) (2018b) expects the installed capacity of solar to increase nearly
tenfold by 2027. While, the FPSC predicts the capacity of other renewable energy sources will
remain nearly constant during the same time period
Over three-fourths of residents in the State of Florida are served by Investor-Owned
Utilities (IoUs) (Florida Public Service Commission, 2018a; Florida Office of Economic and
Demographic Research, 2019a). The largest IoU, Florida Power and Light (FPL), will provide a
leadership role in determining the future energy portfolio for the State. The present article makes
the following new contributions concerning the profitability of utility-scale solar:
• We identify the industry cost trends, policy supports, and regulatory processes impacting the adoption of solar-based utility projects.
• We explore the economic prospects for FPL implementing utility-scale solar projects by conducting a cost-benefit and sensitivity analysis of 110,880 scenarios. FPL is the
largest utility provider and serves the current electricity needs of approximately half
of Floridians.
• We identify under what specific scenarios and conditions solar projects become profitable.
• We discuss how transitioning to solar can impact electricity base rates for Floridian consumers.
• We offer recommendations for future solar energy research to include the other Investor-Owned Utilities (IoUs) serving more concentrated urban areas and expand to
include Municipally-Owned Utilities serving low populations.
Background
Cost Trends in Solar Power Generation
Utility-scale solar is becoming more cost competitive to its nonrenewable counterparts
due to technical advancements and policy stimulus. The utility sector has been the fastest-
growing sector of the photovoltaic (PV) market since 2007 (Bolinger, Weaver, & Zuboy, 2015)
and has led the overall U.S. solar market in installed capacity since 2012 (Bolinger & Seel,
___________________________Journal of Multidisciplinary Research___________________________
27
2018). The cost to construct utility-scale solar plants has dropped leading to increased
implementation across the United States.
The cost to install equipment for generating electricity from solar is decreasing. Among
projects completed nationally in 2017, research shows that median installed PV project costs
have fallen by two-thirds since 2007-2009 to $2/WAC (Bolinger & Seel, 2018). Among Power
Purchase Agreements, in which solar installers enter into a contract with a purchaser of
electricity (usually utilities), levelized Power Purchase Agreements prices for utility-scale PV
have fallen to at or below $40/MWh (in real 2017 dollars) (Bolinger & Seel, 2018). Only two
years prior in 2015, industry reports were questioning if $50/MWh would be possible (Bolinger,
Weaver, & Zuboy, 2015). In 2017, solar constituted 31% of all U.S. capacity additions behind
natural gas (42%) (Bolinger & Seel, 2018). Between 2010 and 2018, total costs for utility-scale
PV installations decreased 66% (International Renewable Energy Agency, 2019). The levelized
cost of energy for coal is $36, and for natural gas is $41 (Lazard, 2018).
In 2017, the southeastern United States was the leading region adding solar capacity.
Florida was the fourth-ranking state in the southeast region (Bolinger & Seel, 2018). Florida’s
solar development is behind other states as it does not have a renewable portfolio standard and
does not allow power purchase agreements (Solar Energy Industries Association, 2019).
Nevertheless, researchers predict PV penetration will be cost competitive in Florida within the
next decade (Hale, Stoll, & Novacheck, 2018). In all but the most pessimistic of assumptions,
solar could provide approximately 10-24% of the state’s annual electricity generated.
Investment Tax Credit
The Energy Policy Act of 2005 introduced the federal Investment Tax Credit (ITC)
(Strokes & Breetz, 2018). The federal government extended the ITC multiple times including,
most recently, in December 2015 (Bolinger & Seel, 2018). The government’s recent extension
allows residential and commercial installers of solar to claim a 30% federal income tax credit on
solar projects, which began construction on or before December 31, 2019. The latest extension is
an improvement over previous years. Commercial organizations that install solar may deduct
30% of a project’s total cost from corporate income taxes. In order to receive the full 30% credit,
the project must have begun construction on or before 2019. Beginning in 2020, the 30% credit
decreases to 26% (Bolinger & Seel, 2018). In 2021, the credit decreases to 22%, and in 2022 the
credit drops to 10%.
Research shows that state and federal policy have been significant in supporting solar PV
capacity growth (Crago & Koegler, 2018; Herche, 2017). Researchers attribute the cost-
competitiveness of solar to other sources of energy, including coal and natural gas, to policies
including ITC, renewable energy credits, and net metering (Comello, Reichelstein, & Sahoo,
2018). As recently as 2017, a study reported that utility-scale solar without the ITC was not as
cost-competitive as other generation sources (Comello, & Reichelstein, 2016). This indicates that
solar may not be competitive without subsidies.
Nonetheless, the utility-scale solar system literature points to declining average
installation costs and levelizing electricity costs. Researchers expect in the near future that solar
may become cost-competitive to other energy generation sources with no ITC at all (Lazard,
2018; Comello, Reichelstein, & Sahoo, 2018). Solar is already naturally cost-competitive in
states with high insolation, such as California (Comello, Reichelstein, & Sahoo, 2018). By 2025,
___________________________Journal of Multidisciplinary Research___________________________
28
research predicts solar will be generally cost-competitive across the entire United States
(Comello & Reichelstein, 2016).
SoBRA Mechanism
In Florida, the Solar Base Rate Adjustments (SoBRA) mechanism allows the IoU to
construct new electricity-generating solar power stations and recover a set amount of these costs
through increased base rates charged to their customer (Florida Public Service Commission,
2018b). Each IoU undergoes multiple hearings and discussions with the Florida Public Service
Commission, which is the public agency tasked with exercising regulatory authority over utilities
in the key areas of base rate and competitive market oversight (Florida Public Service
Commission, 2019b The SoBRA mechanism enabling the IoU to recover costs from solar
projects also sets the base rates the IoU charges its customers for the next four-year period.
Through the SoBRA mechanism, each IoU must demonstrate a reduction in cumulative
present value revenue requirement (CPVRR) that signifies a solar project is more beneficial to
consumers than if the IoU did not implement the project. This process involves formally
petitioning the FPSC and proceeding with public hearings where the IoU presents evidence
supporting their CPVRR argument. IoUs must submit a 10-year site plan for review that
estimates each utility’s power-generating needs and the general location of its proposed power
plant sites.
The FPSC allows construction of up to 300 MW of solar capacity per calendar year
(Florida Public Service Commission, 2016). Post-facility construction and the launch of
operations, the IoU can recover incremental annualized base revenue requirement for 12 months
via base rate increases. In order for this process to occur, the utility must prove implementing the
solar projects are cost effective. Formally, the cost effective calculation answers if the project
lowers the projected system Cumulative Present Value Revenue Requirement, compared to each
CPVRR without the solar project. As part of the SoBRA process, the utility presents its own
CPVRR analysis and generates its own cost estimations and considers the factors of solar
revenue requirements, avoided generation costs, and avoided system costs (Florida Public
Service Commission, 2018c). This research assesses how current conditions in the solar market
could affect FPL’s net profit given that the FPSC regulates its rates and the corresponding
SoBRA mechanism. The research also assists in investigating the utility of the SoBRA
mechanism in present market conditions.
Methodology
Case Study: FPL
FPL serves as the single critical case for this analysis as it is the largest energy provider
in Florida and plans to install the most solar among all its competitors within the next ten years.
IoUs serve approximately 78.5% of Floridians (Florida Public Service Commission, 2018a;
Florida Office of Economic and Demographic Research, 2019a). Among projected energy
sourced from solar, FPL represents the majority of growth (Figure 1). We chose FPL because it
plans to implement considerable additions of solar capacity to its existing network under the
SoBRA mechanism (Florida Public Service Commission, November 2018).
___________________________Journal of Multidisciplinary Research___________________________
29
Figure 1
Projected Energy Generated from Solar by Utility (Adapted from TECO, 2019; FPL, 2019;
Duke Energy Florida, 2019; Arnold & Yildiz, 2015)
We are analyzing FPL’s solar profitability as it received the most lenient SoBRA
mechanism cost-recovery parameters, compared to the other Floridian IoUs. The SoBRA
mechanism caps FPL’s eligible cost of the components, engineering, and construction at
$1,750/kW. Meanwhile, Duke’s limit is $1,650 and TECO’s is $1,500. In addition, the FPSC
caps the IoUs’ return on equity (ROE): FPL’s is 10.55%, Duke’s is 10.5%, and TECO’s is
10.25%.
We use CBA to investigate the economic viability of FPL’s solar projects without the
proposed SoBRA base-rate increases. The CBA projects if FPL could gain benefit from
installing the solar projects with no change in base rates. The CBA evaluates net profit by taking
the difference of revenue and costs. If revenue is greater than costs, then net profit is positive. If
revenue is less than costs, then net profit is negative.
We use a sensitivity analysis to vary inputs into the CBA to determine under which
scenarios, if any, the CBA finds FPL still receives benefit via net profit. We alter various
variables including year time period, module efficiency, insolation, total capital cost, and
operations and maintenance (O&M) cost. We held other variables constant, such as the
following: the ITC credit (at 30%), constant base rates, project capacity added annually,
degradation rate, and no state-level subsidy nor carbon tax.
The sensitivity analysis can conduct repeated simulations and evaluate the net profit by
varying input parameters. For example, assume one desired simulation calculates net profit when
each of the variables are set to their lowest value. Then assume one wishes to find net profit
when module efficiency is set to its highest value while the other variables remain the same. In
addition to these two scenarios, we used the sensitivity analysis to calculate many other
scenarios. The revenue is the financial value of electricity FPL would receive from selling to a
consumer at the current base rate. Our methodology borrows from research by Arnold and Yildiz
(2015) that applies Monte Carlo Simulation methods with nonconstant probability density
functions of variables to measure economic risk associated with renewable energy systems. Our
research assumes constant probability density for changing variables. Due to pricing uncertainty,
we do not consider future state-level charges to sources of carbon generation (such as fossil fuel
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plants). We used MATLAB programming software to conduct calculations. Table 1 introduces
the variables in our analysis. We explain each of the variables in the following section.
Table 1
Variables in the CBA Model with Range and Iteration Sizes
Variable Range Iteration Size
Module Efficiency (kWh produced / kWh insolation) .16 - .21 0.01
Insolation (kWh/(m2 * day)) 5 - 6 0.1
Years 1 - 30 1
Capital Cost ($/kW installed) 950 - 1,300 50
Operations and Maintenance Cost ($/(kW installed * year) 9 - 12 0.5
Base Rate ($/1,000 kWh) 98.56 N/A
Degradation Rate 0.992 N/A
Installed Capacity (kW) 298,000 N/A
Revenue Parameters
Generation parameters link to revenue as FPL gains revenue from the electricity it sells to
its customers. Generally, the higher the value the parameter takes, the more electricity FPL can
generate and revenue it can receive.
Module Efficiency
This represents the efficiency that a solar module can convert solar energy to electricity.
Most industry reports claim utility-scale projects often install crystalline modules. The National
Renewable Energy Laboratory (2019) organizes a database of record-holder manufacturers by
module type. Recorded efficiencies from this database for crystalline range from 20.4% to
24.4%. In addition, average PV efficiency rose to about 17% (Fraunhofer Institute for Solar
Energy Systems, ISE, 2019). Our range for this research is 16 to 21% to simulate average
module efficiency as well as those that may be close to achieving the record efficiencies in real-
world conditions or within the next few years. The simulations evaluate the range in 1%
increments.
Insolation
Insolation represents the annual average daily total solar resource in kWh per square
meter per day. The National Renewable Energy Laboratory (2017) maintains a graphical
database of all 50 states in the United States. Insolation values from Florida’s map range from
4.7 to 6 (Figure 2). We start our insolation rate at five for this research. FPL’s service area ranges
throughout south Florida, along the east coast and stopping short of Jacksonville. In Figure 2,
FPL’s service area does include 4.7 territories. However, due to their rare occurrence, FPL is
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likely to find a suitable area nearby to install the solar plant. The simulations evaluate the range
in 0.1 increments.
Figure 2
Map of Solar Insolation in Florida (National Renewable Energy Laboratory, 2017)
Years
This represents the number of project years. FPL (2019) estimates solar projects have a
lifespan of 30 years. The simulations evaluate the range from 1 to 30, in 1-year increments.
Revenue Formula
The product of module efficiency, insolation, and years represents the total amount of
electricity FPL generates from solar at the end of years. However, a portion of the solar product
becomes obsolete annually. In reviewing worldwide projects, researchers estimate degradation at
0.8% annually (Jordan & Kurtz, 2015). Therefore, we use the following equation to calculate the
electricity generated, in kWh, in year x:
Electricity Generated (year) = Insolation*Module Efficiency*Capacity
Where Capacity = Installed Capacity (kW) * (.992(Year-1)). .992 represents the portion of
capacity that remains usable from one year to the next.
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The total electricity generated from year 1 until the end of year x is the sum of annual
production of all years prior to and including that year.
Total Electricity Generated = Electricity Generated (year = 1) + Electricity Generated
(year = 2) + … + Electricity Generated (year = x)
We held base rates constant for the purpose of the present research. The total revenue
available to FPL through the generation of electricity is the following:
Revenue = Base Rate * Total Electricity Generated
Where base rate equals $98.56/1,000. This is different than the $66.88 value because the
base rate is only one component of the per unit consumption charge. $98.56 is the price per 1,000
kWh. We divide the above expression by 1,000 to determine the value of electricity FPL can
receive from revenue per kWh.
Revenue is the first term for calculating Net Profit.
Cost Parameters
We associate the following parameters with the cost of supplying electricity from solar
energy. We link cost parameters to profit as FPL must pay a cost to fund these solar projects in
order to generate revenue.
Solar Module Cost
This represents the total capital cost per kW installed. Researchers project this cost to
range from $950 to $1,250 (Lazard, 2018; Fu, Feldman, & Margolis, 2018). The cost could be as
high as $1,250 (Fu, Feldman, & Margolis, 2018). We added $50 to the model to yield a final
upper bound of $1,300. The simulation evaluates in $50/kW increments. Similar to research by
Varghese and Sioshani (2020), each simulation’s capital cost relates to the fixed capacity and,
therefore, is fixed.
Operations and Maintenance Cost
This represents the annual cost per kW to maintain proper functioning of infrastructure in
relation to solar generation. Researchers project this cost to range from $9 to $12 (Lazard, 2018).
Our simulation replicates this range and evaluates simulations in $0.5/kW increments.
Results
Of 110,880 scenarios, we found only 2,619 yielded an economic profit for FPL. This is
2.4% of all scenarios. Figure 3 plots the revenue generation parameter combinations for each of
the 2,619 profitable scenarios as well as the resulting profit. The numbers below represent the
lowest values of the profitable scenarios:
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Years = 21
Module Efficiency = 17%
Insolation = 5
We did not find any profitable scenarios less than 21 years, a module efficiency of less
than 17%, or insolation of less than 5 kWh. Figure 3 shows only profitable solar scenarios
illustrating FPL’s profit projections in relation to module efficiency, insolation, and years.
Figure 3
MATLAB-generated Graph Displaying Simulated Profit versus Insolation and Module Efficiency
Combinations for All Profitable Scenarios. Yellow dots indicate scenarios in later years (toward
year 30) while blue dots indicate scenarios in earlier years (toward year 21)
We conducted an analysis on those scenarios in which the revenue parameters are equal
to the lowest profitable values. The range of profit for FPL is approximately -$45 million to $-
137 million. This range of values further substantiates a requirement for relatively high values
for two of the revenue parameters when one of the parameters is relatively low.
We found that, having a relatively low value for one of the revenue factors would require
a relatively high value for the other factors. For example, there were only five scenarios in which
solar was profitable at the end of 21 years of operation. These scenarios require the module
efficiency to be 21% (the maximum in the range) and the insolation to be at least 5.9 (the second
highest iteration in the range). FPL’s maximum profit in these simulations was approximately
$7.59 million.
Additionally, we examined the median of parameters that lead to positive profitability.
The numbers below represent the medians of the revenue parameters in the profitable scenarios:
Years = 28
Module Efficiency = 21%
Insolation = 5.8
Of the 56 scenarios with the above median parameters, 32 returned a profit at a rate of
approximately 57%. The average profit was approximately $6.4 million.
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FPL projects the lifespan for solar modules to be approximately 30 years. There were 722
profitable scenarios at the end of 30 years. In particular, one scenario is profitable when
insolation is only 5.4 kWh and module efficiency is 18%. This insolation rate is encouraging
considering FPL’s service territory generally receives at least 5.4 kWh. Moreover, the 18%
module efficiency is the median parameter from our simulation. However, this outcome depends
on having minimum cost parameters.
Even in scenarios in which revenue parameters are high, FPL could still fail to make a
profit. Profitability depends crucially on cost parameters. Among scenarios in which year,
module efficiency, and insolation are set to their maximum values, profit ranges from -17 million
to 83 million with an average profit of 33 million. We analyzed all the 3,696 scenarios of 30
years and found an average profit of approximately $21.9 million. However, 722 profitable
scenarios out of 3,696 possible scenarios is a rate of approximately 19.5%. FPL may recover the
cost of its investment at the end of its usable life if the cost parameters remain low.
Figure 4
MATLAB-generated Scatter Plot of Mean Module Efficiency vs. Insolation Parameters for
Profitable Scenarios by Year. Darker colors represent earlier years (dark blue is for year 21)
and lighter colors represent later years (yellow is for year 30)
Figure 4 indicates a trend of lower insolation and module efficiency combinations as
years increase. This is reasonable since as time increases, there are more occurrences of
scenarios in which revenue is greater than cost. For example, the marginal profit for some
scenarios is positive regardless of time. However, the large capital cost has a greater relative
effect when the number of years is low. New scenarios that become profitable after a certain year
generally have lower revenue parameter values than those that were profitable in fewer years.
These new scenarios gradually decrease the mean of revenue parameters.
We analyzed scenarios within FPL’s allowed rate of return. The FPSC authorizes a rate
of return between 6% and 7%. We found 908 scenarios yielded a rate of return greater than 7%,
and another 165 had a rate of return between 6-7%. Less than one percent (0.97%) of scenarios
had a rate of return within FPL’s allowed range.
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Discussion
Our simulations suggest that FPL can potentially yield economic profit at the end of a
utility-scale solar project’s lifespan of 30 years. Even with approximately 19.5% of 30-year
scenarios being profitable, the average profit for all 30-year scenarios was $21.9 million. It is
nonetheless crucial that the solar projects generate their electricity projections consistently
throughout a long period. We projected only about 2.4% profitable scenarios out of 110,880
possible simulations. The earliest time to reach profitability is in the 21st year of operation.
Should one of the revenue parameters be relatively low, the other two revenue parameters
must be relatively high. Our minimum projections for profitability were 21, 17%, and 5,
respectively. Our median number of years, module efficiency, and insolation rate among
profitable scenarios was 28, 21%, and 5.8, respectively. Although 21% module efficiency is the
upper limit for that parameter, this is encouraging since 5.8 kWh daily insolation is available in
FPL’s service area, and two additional years can supplement profitability.
We did not find any profitable scenarios without the ITC. For now, subsidies need to be
in place for solar profitability. There is extensive literature available on the agent-based models
of consumer solar PV system adoption including neighborhood effects (Bollinger & Gillingham,
2012; Graziano & Gillingham, 2015) and consumer preferences (Schelley, 2014; Wolske, 2020).
Instead of a public utility providing solar, residents chose to adopt and install rooftop solar
panels. Consumers of this solar option are often high-income individuals, homeowners (Wolske,
2020), and retirees (Schelley, 2014). In a California study, low-income households that received
their PV system for free shared some similarities to their high-income counterparts including
attraction to novelty and environmental values (Wolske, 2020). In most cases, renters and low-
income individuals are unlikely to be able to make a long-term investment in rooftop solar.
These residents rely heavily on public utilities to provide the most cost effective and sustainable
source of energy. If trends of increasing module efficiency progress in the near future, there are
better prospects for FPL to make a positive profit through solar projects and provide low-income
Floridians a renewable energy source.
Limitations
In order to assess the SoBRA policy mechanism, we only considered financial impacts in
the present analysis. The present study differs from FPL’s CPVRR analysis, as it does not
include avoided system generation costs and environmental compliance costs. As a replacement
for a solar project, FPL notes a combined-cycle plant would be brought online one year earlier
than under the current schedule. The avoided system generation costs are valuations and are not
direct expenditures or revenues. Comparing the solar projects to other forms of electricity
generation is not within the scope of the present research. For a detailed comparison of wind
versus solar energy storage systems, researchers Zakeri and Syri (2015) conducted a detailed life
cycle cost-benefit analysis. Environmental compliance cost forecasts vary (Florida Public
Service Commission, 2018c), and the uncertainty of public regulation does not give a clear
valuation. A limitation of excluding environmental compliance costs in this study is a potential
overestimate of profitability.
Solar projects must have begun construction before 2020 to receive the full 30% credit.
The present research uses a static ITC valuation by applying the full credit, regardless of the
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start-time on a project. By applying less-beneficial credits, we would have found a majority of
scenarios with lower net profits.
Conclusion
The ITC is currently subsidizing Florida’s solar future. Without the credit, we did not
find any profitable scenarios. Solar projects still need subsidies to be profitable within the
regulated rate of return. Along with the ITC, improvements in solar technology in recent years is
pushing costs down and making solar a more competitive utility-scale energy source. The present
study examined FPL, Florida’s leading electricity provider. FPL benefits from economies of
scale and a more favorable cost recovery environment over its competitors from the SoBRA
mechanism.
Can FPL gain financial profitability without base rate increases? We found that nearly
one-fifth of 30-year scenarios yielded positive profit for FPL. However, we also found that less
than 1% of all scenarios reached FPSC’s allowed rate of return. Since our simulations held base
rates constant, utility providers may need to raise consumer rates to achieve the FPSC’s
profitability standards. Although future improvements in module efficiency are foreseeable on
the horizon, there is still some lag time for solar cost-competiveness in the market. We found that
more than 85% of scenarios were not profitable and that 90% of all scenarios did not reach the
regulated rate of return.
A subsequent question is “Why should Florida residents incur higher bills to support
technology that is not yet economically competitive?” Changes to the energy portfolio are likely
to impact low-income residents first as they are highly sensitive to rate increases (Cai, Adlakha,
Low, De Martini, & Chandy, 2013).. In terms of policy, the ITC credit needs to remain in place
until solar technology becomes a more competitive cost option. Additionally, policymakers can
reexamine the FPSC’s allowable rate of return to redefine what a profitable scenario entails.
Broadening this definition will allow utility providers some regulatory flexibility that may prove
beneficial to the consumer.
In addition to examining Florida’s largest IoU, future research could investigate the two
other major IoUs in Florida using the SoBRA mechanism in order to compare and assess solar
profitability. With IoUs serving more populous areas, studies also can examine Municipally-
Owned Utilities serving more rural populations. Future studies can examine the valuation of non-
financial benefits including reduced environmental and health costs from pollution or carbon
emissions.
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About the Authors
Arnel Garcesa, WELL Accredited Professional, LEED Green Associate
([email protected]), is a research assistant at the DeVoe L. Moore Center in the College of
Social Sciences and Public Policy at Florida State University. His research examines sustainable
energy pricing. Currently, Arnel is pursuing his second undergraduate degree, in Statistics, at
Florida State University. He also holds an undergraduate degree in Mathematics from Florida
State University and a Master of Arts in Global Sustainability from the University of South
Florida. Arnel works professionally as an Energy Specialist, conducting on-site energy audits
with the goal of reducing commodity consumption, and serving as the on-site liaison for data
measurement and verification matters.
Crystal Taylor, Ph.D., AICP ([email protected]), is Director of Public Policy at the
DeVoe L. Moore Center and teaches land use, housing, and urban planning in the College of
Social Sciences and Public Policy at Florida State University. Dr. Taylor’s research examines
how influences from built environment practices, land use policies, and market-driven
approaches impact sustainable innovation projects. She leads applied research projects and the
data analytics team for the DeVoe L. Moore Center on real estate, mapping, and regulatory
processes. She mentors approximately 30 interns per semester who work on a wide variety of
data-driven projects.
Discussion Questions
1. What are the economic prospects for FPL when utility-scale solar projects are implemented?
2. Under what scenarios do solar projects become profitable?
3. Are base rate increases necessary to maintain legislated profitability?
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To Cite this Article
Garcesa, A., & Taylor, C. (2020, Fall). Simulating utility-scale solar energy profitability in
Florida. Journal of Multidisciplinary Research, 12(2), 25-40.
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