Adversarial Attack for Deep Reinforcement Learning Based Demand Response
Document Type
Conference Proceeding
Date of Original Version
1-1-2021
Abstract
Demand response (DR) has been considerably used to improve the stability and efficiency of power system. Among the existing optimization and control models for DR programs, deep reinforcement learning (DRL) can directly learn optimal management policy from the input data and provide dramatic improvement in capability. However, DRL is vulnerable to adversarial attack where small perturbation is added into the input data. To investigate the vulnerability of DRL-based DR under adversarial attack, this paper formulates this problem as a nonlinear integer programming problem. A Particle Swarm Optimization (PSO)-based method is proposed to solve this problem and generate the attack vector. The proposed approach can identify the most vulnerable data points. To the best of our knowledge, this is the first time that adversarial attack for DRL-based DR is investigated. The performance of the proposed method is evaluated under extensive simulation analysis.
Publication Title, e.g., Journal
IEEE Power and Energy Society General Meeting
Volume
2021-July
Citation/Publisher Attribution
Wan, Zhiqiang, Hepeng Li, Hang Shuai, Yan Lindsay Sun, and Haibo He. "Adversarial Attack for Deep Reinforcement Learning Based Demand Response." IEEE Power and Energy Society General Meeting 2021-July, (2021). doi: 10.1109/PESGM46819.2021.9637826.