Open AccessOptimal demand response strategy of commercial building‐based virtual power plant using reinforcement learning
Author(s) -
Chen Tao,
Cui Qiushi,
Gao Ciwei,
Hu Qinran,
Lai Kexing,
Yang Jianlin,
Lyu Ran,
Zhang Hao,
Zhang Jinyuan
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12179
Subject(s) - reinforcement learning , software deployment , demand response , computer science , incentive , on demand , process (computing) , software , operations research , simulation , engineering , artificial intelligence , electricity , software engineering , operating system , multimedia , electrical engineering , economics , microeconomics
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.