Privacy Preserving Load Control of Residential Microgrid via Deep Reinforcement Learning
Zhaoming Qin, Di Liu, Haochen Hua, Junwei Cao
Abstract
Demand side management has been proved to be effective in improving the operating efficiency of microgrids, while posing a severe threat to user privacy. This paper proposes a novel privacy preserving load control scheme for the residential microgrid, in which the microgrid operator manages a multitude of home appliances including electric vehicles (EVs) and air conditioners (ACs). This problem is formulated as a partially observable Markov decision process, since users' privacy information including indoor temperatures associated with ACs and arrival/departure times of EVs cannot be observed by microgrid operator. To address the formulated problem with high-dimensional continuous action space caused by massive controllable appliances, we develop a novel deep reinforcement learning algorithm by introducing credit assignment mechanism. Moreover, we integrate recurrent neural network to accommodate the partial observability of state due to privacy issues. Simulation results demonstrate the superiority and flexibility of the developed algorithm and verify the advantages of the proposed scheme compared with prior privacy preserving load control method.