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    方磊, 牛玉刚, 祖其武. 微网可转移负荷调度与储能控制策略[J]. 华东理工大学学报(自然科学版), 2018, (4): 588-594. DOI: 10.14135/j.cnki.1006-3080.20170525001
    引用本文: 方磊, 牛玉刚, 祖其武. 微网可转移负荷调度与储能控制策略[J]. 华东理工大学学报(自然科学版), 2018, (4): 588-594. DOI: 10.14135/j.cnki.1006-3080.20170525001
    FANG Lei, NIU Yu-gang, ZU Qi-wu. Transferable Load Dispatching and Energy Storage Control Strategy in Microgrid[J]. Journal of East China University of Science and Technology, 2018, (4): 588-594. DOI: 10.14135/j.cnki.1006-3080.20170525001
    Citation: FANG Lei, NIU Yu-gang, ZU Qi-wu. Transferable Load Dispatching and Energy Storage Control Strategy in Microgrid[J]. Journal of East China University of Science and Technology, 2018, (4): 588-594. DOI: 10.14135/j.cnki.1006-3080.20170525001

    微网可转移负荷调度与储能控制策略

    Transferable Load Dispatching and Energy Storage Control Strategy in Microgrid

    • 摘要: 针对新能源发电的不确定性和随机性,以及新能源发电与用户负荷难以匹配的问题,构建了风、光、储共存的微电网系统,并提出了一种基于可转移负荷和储能系统协调控制的微网能量管理策略。首先在现有的微网模型基础上构建了集中式微网控制系统,用于对风、光、储系统进行相应的控制,同时建立了兼顾用电负荷优化调度与储能系统安全高效运行的多目标函数。然后采用改进粒子群优化算法求解目标函数,引入新的负荷调度策略降低计算复杂度和提高用户满意度,最终获得可转移负荷调度量,实现可转移负荷的调度策略。同时,充分考虑了储能系统的影响,对荷电状态(SOC)进行控制监测,控制储能系统波动情况,提高储能系统性能并延长其使用寿命。最后通过数值仿真,验证了本文提出的能量管理策略在满足用户满意度与实际储能要求的前提下,可以提高微网运行性能,实现微网能量管理和经济运行。

       

      Abstract: There exist uncertainties and randomness in renewable power generation and the difficult matching between the renewable generation and the user load. In order to deal with the above problems, this paper constructs a micro-grid system of integrating the wind power generation, the photovoltaic power generation and the energy storage system. And then, this paper proposes a micro-grid energy management strategy based on the coordinated control between transferable load and energy storage system. Firstly, a centralized micro-grid control system is designed via the existing micro-grid model to control the wind power generation, the photovoltaic power generation and the energy storage system. By taking into account the optimization of the user load and the safe and efficient operation of the energy storage system, a multi-objective function is introduced, which is solved by using the improved particle swarm optimization algorithm. Moreover, a new load scheduling strategy is proposed to reduce computational complexity and improve user satisfaction, by which the transferrable load dispatch value is received and the transportable load scheduling strategy is achieved. The key features of the proposed energy management strategy are that the impact of energy storage system is fully considered, the state of charge (SOC) is controlled and monitored, and the fluctuations of the energy storage system is also controlled. Thus, the proposed strategy can effectively improve the energy storage system performance and extend its service life. Finally, numerical simulation results show that the proposed energy management strategy can satisfy the users and meet the actual energy storage requirement, and achieve the economic operation.

       

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