基于CDOA算法的配电网弹性提升方法
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引用本文:郭艺1,姜新丽2,王波3.基于CDOA算法的配电网弹性提升方法[J].电网与清洁能源,2022,38(5):56~64
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作者单位
郭艺1 1. 三峡大学 电气与新能源学院 
姜新丽2 2. 许昌开普电气研究院有限公司 
王波3 3. 武汉大学 电气与自动化学院 
基金项目:国家自然科学基金项目(51777142)
中文摘要:近年来频繁的极端灾害对电力系统的稳定运行提出新的挑战。将大型船舶微电网系统集成到配电网中以提高沿海地区或岛屿配电网的弹性和可靠性,提出一种考虑船舶微电网系统的配电网弹性提升方法。在建立最小化船舶微电网系统与配电网的总运行成本的同时,最大化配电网弹性的多目标优化模型;针对不同的系统分别构建船舶微电网系统与配电网系统的约束条件;为了克服模型求解的复杂性和非线性,采用集体决策优化算法(collective decision optimization algorithm, CDOA)对所提模型进行求解。仿真结果表明,在正常情况下,协调调度移动式船舶微电网系统可以提高网络的可靠性,而在极端灾害发生时,通过合理调度船舶微电网系统可以显著提高配电网的弹性,补偿灾后条件下的大量电力。
中文关键词:弹性配电网  船舶微电网系统  极端灾害  集体决策优化算法
 
Method to Improve the Elasticity of Distribution Network Based on CDOA Algorithm
Abstract:In recent years, frequent extreme disasters have challenged the stable operation of the power system. This paper integrates the large-scale ship micro-grid system into the distribution network to improve the flexibility and reliability of the distribution network in coastal areas or islands, and proposes a strategy to improve the flexibility of the distribution network considering the ship micro-grid system. First, a multi-objective optimization model that minimizes the total operating cost of the ship microgrid system and the distribution network while maximizing the flexibility of the distribution network is established; second, for different systems, the constraints of the ship microgrid system and the distribution network system are constructed. Finally, in order to overcome the complexity and nonlinearity of the model solution, the Collective Decision Optimization Algorithm (CDOA) is used to solve the proposed model. The simulation results show that under normal conditions, the coordinated dispatch of the mobile ship microgrid system can improve the reliability of the network, while in the event of an extreme disaster, the reasonable dispatch of the ship micro-grid system can significantly improve the resilience of the distribution network and compensate much electricity for the post-disaster conditions.
keywords:resilient distribution network  ship micro-grid system  extreme disasters  collective decision optimization algorithm
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