The Optimal Operation of Honeycomb Distribution Network With Multi-agent System

被引：1

作者：

Xu, Jie ^{[1
]}

Chen, Ding ^{[1
]}

Li, Chun ^{[1
]}

Zhang, Xiaodi ^{[2
]}

Gao, Qiang ^{[2
]}

Zhu, Naixuan ^{[3
]}

Hu, Pengfei ^{[3
]}

机构：

[1] State Grid Jiaxing Power Supply Co, Jiaxing, Peoples R China

[2] State Grid Zhejiang Elect Power Co Ltd, Hangzhou, Peoples R China

[3] Zhejiang Univ, Coll Elect Engn, Hangzhou, Peoples R China

来源：

2022 INTERNATIONAL CONFERENCE ON MECHANICAL, AUTOMATION AND ELECTRICAL ENGINEERING, CMAEE | 2022年

关键词：

Distributed renewable energy; multi-agent system; microgrid cluster; smart grid; optimal dispatching; MICROGRIDS;

D O I：

10.1109/CMAEE58250.2022.00039

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Growing renewable energy access poses serious challenges for traditional distribution network, especially from the point of view of uncertainty and volatility. In this paper, a new type of distribution network structure called honeycomb distribution network (HDN) is introduced, as well as the core device smart power/information exchange station (SPIES), which performs as the agent for power exchange and electricity trading. Firstly, the characteristics and benefits of HDN are described. Then the mathematical physics models of HDN system with the functions of multi-agent system are established. The two-level optimization operating model of HDN with multi-agent system is proposed and then solved and verified through a numerical simulation of simplified HDN system. The optimal results show that HDN with multi-agent system can realize the economic operation of the distribution network and the consumption of renewable energies, which reveal the potential of HDN in the future new power network.

引用

页码：183 / 188

页数：6

共 14 条

[1] Review of the cooperation and operation of microgrid clusters [J].

Bandeiras, F. ;

Pinheiro, E. ;

Gomes, M. ;

Coelho, P. ;

Fernandes, J. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 133

[2]

Burer S., 2012, Surv. Oper. Res. Manag. Sci., V17, P97, DOI 10.1016/j.sorms.2012.08.001

[3] Microgrids Operation Based on Master-Slave Cooperative Control [J].

Caldognetto, Tommaso ;

Tenti, Paolo .

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, 2014, 2 (04) :1081-1088

[4] Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm [J].

Feng, Jinhong ;

Zhang, Jundong ;

Wang, Chuan ;

Jiang, Ruizheng ;

Xu, Mingyi .

IEEE ACCESS, 2020, 8 :73204-73219

[5]

[江道灼 Jiang Daozhuo], 2019, [电力系统自动化, Automation of Electric Power Systems], V43, P1

[6] Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk [J].

Khodabakhsh, Raheleh ;

Sirouspour, Shahin .

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2016, 7 (03) :1264-1273

[7]

Naixuan Zhu, 2020, 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), P455, DOI 10.1109/ICIEA48937.2020.9248161

[8] State of the Art in Research on Microgrids: A Review [J].

Parhizi, Sina ;

Lotfi, Hossein ;

Khodaei, Amin ;

Bahramirad, Shay .

IEEE ACCESS, 2015, 3 :890-925

[9] Multi agent system: concepts, platforms and applications in power systems [J].

Sujil, A. ;

Verma, Jatin ;

Kumar, Rajesh .

ARTIFICIAL INTELLIGENCE REVIEW, 2018, 49 (02) :153-182

[10] Optimal distributed renewable generation planning: A review of different approaches [J].

Tan, Wen-Shan ;

Hassan, Mohammad Yusri ;

Majid, Md Shah ;

Rahman, Hasimah Abdul .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2013, 18 :626-645

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