Peer-to-peer energy trading strategy for prosumers based on model predictive control
DOI：DOI: 10.19783/j.cnki.pspc.211078
Key Words:Peer-to-peer energy trading strategy for prosumers based on model predictive control
Author NameAffiliation
ZHOU Wei 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
GAO Yao 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
PENG Feixiang 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
WU Jianzhong 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
DANG Wei 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
LIU Ying 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
WANG Zhonghui 1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 
Hits: 4293
Download times: 1021
Abstract:Under the market development of new energy without subsidies, an effective market mechanism is necessary for ensuring the accommodation of distributed PV. It can also promote the initiative of participants. Peer-to-peer energy trading is a method, and it can make electricity consumers and producers trade directly and then facilitate local power balance. This paper proposes a P2P trading strategy based on a model predictive control method and continuous double auction. The model predictive control is used to optimize the charge and discharge power of energy storage in order to guide the electricity bidding of market players. To realize the autonomous decision-making of each participant, a zero-intelligence-plus strategy with learning ability is adopted as the bidding price method. The calculation results of a regional power grid in west Liaoning province show that the trading strategy of distributed P2P market based on model predictive control can guide the bidding behavior of users. This strategy can further increase the benefits of prosumers and improve distributed photovoltaic hosting capacity in distribution networks. This work is supported by the National Natural Sciences Foundation of China (No. 61873048).
View Full Text  View/Add Comment  Download reader