Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program

Abstract

Demand response providers (DRPs) are intermediaries between the upper-level distribution system operator and the lower-level participants in demand response (DR) programs. Usually, DRPs act as leaders and determine electricity pricing strategies to maximize their economic revenue, while end-users adjust their power consumption following the pricing signals. However, this profit-seeking bi-level optimization model often neglects the satisfaction of end-users participating in DR programs. In addition, the detailed mathematical models underlying user decision-making strategy and satisfaction evaluation mechanism are typically unavailable to DRPs, posing significant challenges to conventional model-based solution methods. To address these issues, this paper designs a user-side satisfaction evaluation mechanism and proposes a multi-branch temporal fusion twin-delayed deep deterministic policy gradient (MBTF-TD3) reinforcement learning algorithm. User satisfaction feedback is incorporated into the reward function via a dynamically adjusted penalty term. The proposed MBTF structure effectively extracts temporal feature dependencies in the time-series observation data, and the dynamically adjusted penalty function successfully enhances the overall satisfaction level of users. Several experiments are conducted to validate the performance and the effectiveness of our proposed solution algorithm.