Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning

The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.