Date of Award
Doctor of Philosophy (PhD)
Engineering and Public Policy
Demand response and dynamic pricing are touted as ways to empower consumers, save consumers money, and capitalize on the “smart grid” and expensive advanced meter infrastructure. In this work, I attempt to show that demand response and dynamic pricing are more nuanced. Dynamic pricing is very appealing in theory but the reality of it is less clear. Customers do not always respond to prices. Price differentials are not always large enough for customers to save money. Quantifying energy that was not used is difficult.
In chapter 2, I go into more detail on the potential benefits of demand response. I include a literature review of residential dynamic pilots and tariffs to see if there is evidence that consumers respond to dynamic rates, and assess the conditions that lead to a response.
Chapter 3 explores equity issues with dynamic pricing. Flat rates have an inherent cross-subsidy built in because more peaky customers (who use proportionally more power when marginal price is high) and less peaky customers pay the same rates, regardless of the cost they impose on the system. A switch to dynamic pricing would remove this cross subsidy and have a significant distributional impact. I analyze this distributional impact under different levels of elasticity and capacity savings.
Chapter 4 is an econometric analysis of the Commonwealth Edison RTP tariff. I show that it is extremely difficult to find the small signal of consumer response to price in all of the noise of everyday residential electricity usage.
Chapter 5 looks at methods for forecasting, measuring, and verifying demand response in direct load control of air-conditioners. Forecasting is important for system planning. Measurement and verification are necessary to ensure that payments are fair. I have developed a new, censored regression based model for forecasting the available direct load control resource. This forecast can be used for measurement and verification to determine AC load in the counterfactual where DLC is not applied. This method is more accurate than the typical moving averages used by most ISO’s, and is simple, easy, and cheap to implement.
Horowitz, Shira R., "Topics in Residential Electric Demand Response" (2012). Dissertations. 197.