Dynamic Pricing Tariffs for DTE’s Residential Electricity Customers

By May 10, 2010Library

By Arie Jongejan, Brian Katzman, Thomas Leahy, and Mark Michelin, Erb ’10. Faculty Advisor: Greg Keoleian.

Abstract: Despite temporal changes in wholesale electricity prices, retail prices are typically constant throughout the day. To address this economic inefficiency, Detroit Edison, a subsidiary of DTE Energy (DTE), can introduce residential dynamic pricing rates to incent customers to shift load away from peak periods, at which time wholesale electricity prices are high. This paper estimates the financial and environmental impacts of implementing dynamic electricity pricing rates for residential customers within the Midwest Independent System Operator (MISO). Based on these estimates, we recommend that DTE pilot specific residential dynamic pricing rates, all of which may be suitable for wide-scale deployment. We researched existing pricing programs that have been piloted throughout the country to determine which options present the most potential to reduce or shift peak load. In addition, we obtained cost estimates for enabling technology to be used in conjunction with these tariffs. We then constructed a dispatch model which simulates the MISO electricity market by using electricity supply and demand forecasts for 2010-2030. Applying residential peak load reduction and shifting estimates from previous pilots to the dispatch model, we calculate avoided capacity savings, avoided energy savings, and emissions impacts for various dynamic pricing programs. Specifically, we analyzed a Time of Use (TOU) tariff and TOU/Critical Peak Price tariff with and without enabling technology (smart thermostat and in-home display), as well as a TOU/Peak-time Rebate tariff. We investigate these tariffs using peak and critical-peak period window lengths ranging from four to eight hours. There were three central results. First, deployment of demand response programs to a subset of residential customers with a four-hour peak window results in financial outcomes ranging from a net loss of $350 million to a net gain of $400 million. Second, enabling technology increases peak load reduction, but technology costs may exceed the savings of the increased load reduction. Third, the length of the peak window is an important driver of economic benefits; increasing the window length may enhance net economic benefits.

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