Optimal thermostatically-controlled residential demand response for retail electric providers

Abstract

In the face of the considerable volatility in electricity market prices, retail electric providers (REPs) occupy the difficult position between the utility and the end-user. They purchase electricity either through long-term contracts, in the day-ahead or spot markets, or self-generate. This electricity is then sold to the end-user at a fixed price. Thus, REPs bear the financial burden of electricity price uncertainty. In a market where prices can increase by orders of magnitude in as little as 15 min, single spikes have driven some REPs to bankruptcy. To mitigate the negative effects of market volatility, REPs can employ demand response (DR), shifting consumers’ electricity load from periods of high prices to lower-priced time periods. While DR comes in many forms, we focus on thermostatically-controlled residential DR, made possible through internet-connected thermostats. The timing of residential heating and cooling load makes it a particularly financially advantageous target for DR. We present a dynamic programming (DP) formulation designed to optimally schedule DR events to maximize their savings for the REP. We also consider the effects of price uncertainty on the savings through the use of stochastic dynamic programming (SDP). Our case study examines the profitability of thermostatically-controlled DR, specifically of air-conditioning (A/C) load, in the Electricity Reliability Council of Texas (ERCOT). We find that optimal management of thermostatically-controlled DR can generate significant savings to the REP, potentially improving their profit margins as much as 25%. However, we also find that few events generate most savings, necessitating precise scheduling facilitated by our formulation. Electricity price uncertainty marginally reduces these expected savings, but optimally-scheduled DR still offers REPs an effective method of mitigating risk.