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Zhong Shi
New Bei-yang Information Technology Co., Ltd., Weihai 264200, China

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Journal article
Published: 03 November 2019 in Energies
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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.

ACS Style

Xiaolin Fu; Hong Wang; Zhijie Wang; Zhong Shi; Wanhao Yang; Pengchi Ma; Wang; Fu; Shi; Yang; Ma. Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion. Energies 2019, 12, 4196 .

AMA Style

Xiaolin Fu, Hong Wang, Zhijie Wang, Zhong Shi, Wanhao Yang, Pengchi Ma, Wang, Fu, Shi, Yang, Ma. Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion. Energies. 2019; 12 (21):4196.

Chicago/Turabian Style

Xiaolin Fu; Hong Wang; Zhijie Wang; Zhong Shi; Wanhao Yang; Pengchi Ma; Wang; Fu; Shi; Yang; Ma. 2019. "Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion." Energies 12, no. 21: 4196.

Journal article
Published: 11 October 2019 in Applied Sciences
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In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system.

ACS Style

Sitong Lv; Jianguo Li; Yongxin Guo; Zhong Shi. A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm. Applied Sciences 2019, 9, 4262 .

AMA Style

Sitong Lv, Jianguo Li, Yongxin Guo, Zhong Shi. A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm. Applied Sciences. 2019; 9 (20):4262.

Chicago/Turabian Style

Sitong Lv; Jianguo Li; Yongxin Guo; Zhong Shi. 2019. "A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm." Applied Sciences 9, no. 20: 4262.

Preprint
Published: 08 September 2019
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In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

ACS Style

Sitong Lv; Jianguo Li; Yongxin Guo; Zhong Shi. Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis. 2019, 1 .

AMA Style

Sitong Lv, Jianguo Li, Yongxin Guo, Zhong Shi. Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis. . 2019; ():1.

Chicago/Turabian Style

Sitong Lv; Jianguo Li; Yongxin Guo; Zhong Shi. 2019. "Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis." , no. : 1.