Optimal Incentive Strategy in Cloud-Edge Integrated Demand Response Framework for Residential Air Conditioning Loads
Qiangang Jia, Sijie Chen, Zheng Yan, Yiyan Li
Abstract
In the residential demand response area, currently the incentive-based method (e.g., direct load control, DLC) may impair users’ comfort and autonomy, while the price-based method can hardly guarantee users’ engagements. This paper proposes an edge-cloud integrated demand response framework to achieve an effect-predictable residential demand response without harming users’ benefits. First, we combine the cloud-computing resource (cloud) and the home-installed smart thermostats (edges) to formulate an efficient, cost-effective, and data-secured infrastructure to implement the demand response program. Then, we model the demand response problem between the load aggregator and its served residential users as a bi-level optimization problem, and the key is for the load aggregator to find the optimal incentive strategy. To solve this problem, we introduce an RL algorithm, i.e., Continuous Action Reinforcement Learning Automata, to quickly obtain the optimal incentive strategy under an incomplete information scenario. Simulation results based on 136 real-world residential users in Austin area demonstrate that the proposed CEI-DR framework can increase the social welfare by about $8.6/h compared to the traditional DLC method during a normal DR event.