Econn-08/Discussion

ECO 625 MILESTONE TWO 1

ECO 625 MILESTONE TWO 3

Milestone Two: Electricity Demand Analysis

Electricity rates in the United States have been quite low over the last few years. The decline in the rates is largely attributed to low costs of hydropower generation. Over 80 percent of residential homes are powered by hydroelectric power, a utility that is fully owned by the states. However, low electricity rates are quite difficult to maintain given that most of the facilities used in power generation get old and wear out (Fezzi & Bunn, 2010). This requires power generating companies to dig deep into their pockets to replace the worn out facilities. It therefore means that they have to build new power generation capacities that are quite expensive. As a result, there has been a steady increase in electricity rates over the last few years. According to Reiss & White (2005), energy self-sufficiency is now being used as policy constraint. This therefore calls for mitigation or delaying of future increase of electricity rates. This strategy can only be achieved if they can curb increase in the demand for electricity.

Owing to the above strategies, electricity generating companies have decided to apply the demand-side management (DSM) strategy. This is one of the ways of dealing with the issue arising from changes in demand for electricity and electricity rates. The strategy particularly addresses issues such as energy efficiency, rates initiatives, and measures to conserve energy. Through this policy, power generating companies in the United States have invested heavily in the demand-side management (DSM) initiative. All this is aimed at promoting energy conservation. The 2008 global recession affected the power generating companies in a big way. Most of the U.S hydropower generating companies replaced their flat electricity rates with two-step rates. This led to an increased-block rate for residential customers in need of power supply. By applying the new rate structure, it means that companies have to apply the lower step 1 rate when charging customers the first 1350 kWh power consumption while the higher step 2 rates will be applied when charging customers whose power consumption exceeds 1,350 kWh. These rates can be applied on a bimonthly basis (Espey, 2004). Comment by Emil Berendt: Great intro!

As opposed to regression analysis, more advanced econometric techniques can be used to establish the relationship between changes in electricity rates and demand. These techniques include constrained optimization and linear programming (Fezzi & Bunn, 2010). The benefiting of applying optimization technique is that there is maximizing and minimizing a given set of real values. For our case, this method can be used to determine the maximum electricity rates and demand as well. The benefit of applying linear programming in analyzing the relationship between electricity demands and changes in rates is that data envelopment analysis (DEA) can be used to provide a better analysis of the two economic variables. The DEA technique is beneficial in this analysis in the sense that it can minimize the hyper-spatial distance function separating power generating companies from their expected power supply demands (Reiss & White, 2005).

Even though econometric approach is always viewed as the best approach of dealing with data gaps, there are many data limitations that might make the whole analysis unsuccessful. One of the most notable data limitations in the analysis might arise as a result of lack of adequate data to be used in the analysis (Kennedy, 2003). Data on most of the variables can only be available within a period of less than ten years. This makes it hard to analyze trends in changes in electricity rates and demand for electricity among residential customers. To have a more focused analysis, electricity demand function should not be below 30 years (Espey, 2004). Comment by Emil Berendt: True, but it is still possible to get some good results from small sample sizes.

The diagrammatic model of flow of goods and services between households and firms can be used to provide an excellent analysis. In particular, through this model, it will be possible to determine how changes in household income affect the demand for electricity (Reiss & White, 2005). It therefore means that income elasticity is the most perfect microeconomic factor that can be used in this analysis. A multiple regression model can be the best model for this case given that it will be easier to determine how different variables such changes in electricity pricing rates and household income affect demand for electricity.

The possible results of the demand analysis are three econometric model specifications. These model specifications can produce a range of estimates of various variables under analysis. The three variables include income elasticity for electricity, demand-side management (DSM) elasticity expenditure, and price elasticity. Elasticity for the three variables is likely to range from -0.08 to -0.13 for price 2 elasticity, 0.22 to 0.36 for income elasticity, and -0.02 to -0.03 for DSM expenditure elasticity (Espey, 2004). Corrections affected the results in a significant way and that is why some of the ranges have negative values. Comment by Emil Berendt: You may want to elaborate more on exactly how this elasticity is defined.

Good start. Be sure to get the data and some analysis done in the near future.

References

Espey, J. A., & Espey, M. (2004). Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities. Journal of Agricultural and Applied Economic, 36(1): pp. 65-81.

Fezzi, C., & Bunn, D. (2010). Structural analysis of electricity demand and supply interactions, Oxford Bulletin of Economics and Statistics, 72(6): pp. 827-856.

Kennedy, P. (2003). A Guide to Econometrics. 5th ed. Cambridge: MIT Press.

Reiss, P. C., & White, M. (2005). Household Electricity Demand, Revisited. The Review of Economic Studies, 72(3): pp. 853-883.

Good start. Be sure to get the data and some analysis done in the near future.