Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion

Xiaolin Fu, Hong Wang, Zhijie Wang, Zhong Shi, Wanhao Yang and Pengchi Ma
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Xiaolin Fu: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Hong Wang: School of Economics & Management, Tongji University, Shanghai 200092, China
Zhijie Wang: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Zhong Shi: New Bei-yang Information Technology Co., Ltd., Weihai 264200, China
Wanhao Yang: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Pengchi Ma: College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China

Energies, 2019, vol. 12, issue 21, 1-25

Abstract: This paper aims to study the problems of surplus interaction, poor real-time performance, and excessive processing of information in the micro-grid scheduling and decision-making process. Firstly, the micro-grid dual-loop mobile topology structure is designed by using the method of block-chain and multi-agent fusion, realizing the real-time update of the decision-making body. Secondly, on the basis of optimizing the decision-making body, a two-layer model of intelligent decision-making under the decentralized mechanism is established. Aiming at the upper model, based on the theory of block-chain consensus mechanism, this paper proposes an improved evolutionary game algorithm. The maximum risk-benefit in the decision-making process is the objective function, which realizes the evaluation and optimization of decision tasks. For the lower layer model, based on the block-chain distributed ledger theory, this paper proposes an improved hybrid game reinforcement learning algorithm, with the maximum controllable load participation as the objective function, and realizes the optimal configuration of distributed energy in the micro-grid. This paper reveals the rules of group intelligent decision making in micro-grid under multi-task. Finally, the effectiveness of the proposed algorithm is verified by using Beijing Jin-feng Energy Internet Park data.

Keywords: block-chain; multi-agent; evolutionary game algorithm; group intelligence decision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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