Optimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning

• Published in: IET generation, transmission & distribution Vol. 15; no. 16; pp. 2309 - 2318
• Main Authors: Chen, Tao, Cui, Qiushi, Gao, Ciwei, Hu, Qinran, Lai, Kexing, Yang, Jianlin, Lyu, Ran, Zhang, Hao, Zhang, Jinyuan
• Format: Journal Article
• Language: English
• Published: Wiley 01-08-2021
• Subjects: Optimisation techniques; Power engineering computing; Power system management, operation and economics; Reinforcement learning
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/gtd2.12179

In this paper, the optimal demand response strategy of a commercial building‐based virtual power plant with real‐world implementation in heavily urbanised area is studied. Instead of modelling the decision‐making process as an optimisation problem, a reinforcement learning method is used to seek the optimal strategy, which could update its performance with minimal manpower manipulation. Specifically, the data collection from several commercial buildings, including hotel, shopping mall and office, in Huangpu district, Shanghai city is analysed to deploy the demand response program. Compared with the conventional demand response strategy based on optimisation, the learnt strategy does not rely on the forecasting information as input and could adapt to the changing demand response incentive automatically. It may not produce the best result every time, but can guarantee the benefit in a non‐deterministic way in long‐term operation. The real‐world deployment of the Huangpu virtual power plant involving hardware and software platform is also introduced, as well as its future development projection.