Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating

• Published in: Applied energy Vol. 288; p. 116623
• Main Authors: Zhong, Shengyuan, Wang, Xiaoyuan, Zhao, Jun, Li, Wenjia, Li, Hao, Wang, Yongzhen, Deng, Shuai, Zhu, Jiebei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.04.2021
• Subjects: deep Q network; Demand response; Load aggregators; Regenerative electric heating; Weber–Fechner law; Demand response; Regenerative electric heating; Weber–Fechner law; Load aggregators; deep Q network
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.116623

•Quantifying the response behavior of heating users by Weber–Fechner law.•Constructing the typical response behavior model of heating users using K-means.•Simulating user participation in demand response via an evolutionary game model.•Using deep Q network to build a dynamic subsidy price generation framework.•The dynamic subsidy price generation framework can benefit all parties. Applications of electric heating, which can improve carbon emission reduction and renewable energy utilization, have brought new challenges to the safe operation of energy systems around the world. Regenerative electric heating with load aggregators and demand response is an effective means to mitigate the wind curtailment and grid operational risks caused by electric heating. However, there is still a lack of models related to demand response, which results in participants not being able to obtain maximum benefits through dynamic subsidy prices. This study uses the Weber–Fechner law and a clustering algorithm to construct quantitative response characteristics models. The deep Q network was used to build a dynamic subsidy price generation framework for load aggregators. Through simulation analysis based on the evolutionary game model of a project in a rural area in Tianjin, China, the following conclusions were drawn: compared with the benchmark model, regenerative electric heating users can save up to 8.7% of costs, power grid companies can save 56.6% of their investment, and wind power plants can increase wind power consumption by 17.6%. The framework proposed in this study considers user behavior quantification of demand response participants and the differences among users. Therefore, the framework can provide a more reasonable, applicable, and intelligent system for regenerative electric heating.