A Deep Demand Response Program for Local Electricity Systems

The decarbonization of power systems facilitates the electrification of appliances, many of which can be operated in a flexible way. Demand response (DR) programs can exploit this flexibility with retail price adjustment, thereby addressing several operational challenges. In this paper, we address the welfare optimization problem of local utilities that procure electricity for their customers at the wholesale market. We demonstrate how DR programs can be designed for local electricity systems where electricity demand and its response to temporary price changes is unknown. For this purpose, we address a novel and complex pricing problem—pricing under unknown, time-interdependent, and discontinuous demand—leveraging Deep Reinforcement Learning. Using a numerical case study calibrated on Californian electricity market data, we show that such a “Deep DR program” helps to identify effective prices that improve social welfare. The performance of the program is consistently positive across a variety of system conditions. We further demonstrate that our approach beats Time-of-Use tariff-based benchmarks already after five and a parametric benchmark after 19 simulation days, on average. Second, we provide novel insights regarding an important but frequently overlooked aspect of DR program design: The length of the notification interval, that is the timespan for which future prices must be set in advance. We find that the timing of price information is important and that longer notification intervals can improve social welfare. Finally, we provide insights into DR price setting and find that DR prices co-move with wholesale market prices but are lower for longer notification intervals and shorter event sequences. The presented Deep DR program provides an example of how advances in machine learning-based algorithms can help to meet the complex operational requirements of future local electricity systems.