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A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization

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Listed:
  • Jicheng Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Fangqiu Xu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Shuaishuai Lin

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Hua Cai

    (Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Suli Yan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

Abstract

The optimal operation of microgrids is a comprehensive and complex energy utilization and management problem. In order to guarantee the efficient and economic operation of microgrids, a three-layer multi-agent system including distributed management system agent, microgrid central control agent and microgrid control element agent is proposed considering energy storage units and demand response. Then, based on this multi-agent system and with the objective of cost minimization, an operation optimization model for microgrids is constructed from three aspects: operation cost, environmental impact and security. To solve this model, dynamic guiding chaotic search particle swarm optimization is adopted and three scenarios including basic scenario, energy storage participation and demand response participation are simulated and analyzed. The results show that both energy storage unit and demand response can effectively reduce the cost of microgrid, improve the operation and management level and ensure the safety and stability of power supply and utilization.

Suggested Citation

  • Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3286-:d:185378
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    Cited by:

    1. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Ji-Won Lee & Mun-Kyeom Kim & Hyung-Joon Kim, 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy," Energies, MDPI, vol. 14(3), pages 1-21, January.
    3. Pan Wu & Wentao Huang & Nengling Tai & Zhoujun Ma & Xiaodong Zheng & Yong Zhang, 2019. "A Multi-Layer Coordinated Control Scheme to Improve the Operation Friendliness of Grid-Connected Multiple Microgrids," Energies, MDPI, vol. 12(2), pages 1-21, January.

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