电子科技 ›› 2019, Vol. 32 ›› Issue (3): 26-31.doi: 10.16180/j.cnki.issn1007-7820.2019.03.006

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微电网多目标鲁棒优化运行

张晋维,李国平   

  1. 三峡大学 湖北省新能源微电网协同创新中心,湖北 宜昌 443002
  • 收稿日期:2018-03-18 出版日期:2019-03-15 发布日期:2019-03-01
  • 作者简介:张晋维(1993-),男,硕士研究生。研究方向:微电网优化运行与控制、电力电子逆变器的控制。
  • 基金资助:
    国家自然科学基金(51507092)

Economic Operation of Microgrid Considering Operational Risk and Customer Satisfaction

ZHANG Jinwei,LI Guoping   

  1. Hubei Provincial Collaborative Innovation Center for New Energy Micro-Grid,China Three Gorges University,Yichang 443002,China
  • Received:2018-03-18 Online:2019-03-15 Published:2019-03-01
  • Supported by:
    National Natural Science Foundation of China(51507092)

摘要:

解决微电网中新能源出力存在的随机性与波动性是微电网优化运行的前提和关键。文中将鲁棒优化理论引入到微电网的运行优化中,以运行成本和环境成本为目标构建了含有风力发电、微型燃气轮机、配电网和蓄电池的微电网多目标鲁棒优化模型,并采用基于自然选择粒子群算法对模型进行求解。仿真结果表明,与传统确定性优化相比,鲁棒优化虽然牺牲了经济性,但换取微电网运行的可靠性,平衡了成本与风险的关系;同时多目标优化平衡了微电网的经济性和环保性,为微电网的优化运行提供了参考。

关键词: 微电网, 鲁棒优化, 优化运行, 风险, 多目标, 自然选择粒子群算法

Abstract:

Solving the randomness and volatility of the new energy output in the microgrid is the premise and key to the optimal operation of the microgrid. In this paper, the robust optimization theory was introduced into the operation optimization of microgrid, and the multi-objective robust optimization model of microgrid with wind power generation, micro gas turbine, distribution network and battery was constructed with the aim of running cost and environmental cost. The natural selection particle swarm optimization algorithm was performed to solve the proposed model. The simulation results showed that compared with the traditional deterministic optimization, the robust optimization sacrificed the economical, but the reliability of the microgrid operation was balanced, and the relationship between cost and risk was balanced. At the same time, multi-objective optimization balanced the economic and environmental protection of the microgrid, providing a reference for the optimal operation of the microgrid.

Key words: micro grid, robust optimization, operating risk, economic operation, multi-objective optimization, natural selection particle swarm optimization algorithm

中图分类号: 

  • TN271
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