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MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations

机译:MILP-PSO组合优化优化算法,具有详细电池效率模型和政策考虑的岛屿化微电网调度

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摘要

This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone for the practical microgrid. The results also indicated that simplification or neglect of ESS efficiency when applying MILP to scheduling may cause a constraint violation.
机译:本文在使用详细的电池ESS能效模型时呈现柴油发电机和能量存储系统(ESS)的最佳调度。最佳调度迄今为止受到电池ESS的非线性和复杂性的拖欠。这是由于电池ESS效率是逆变器和电池效率的乘法以及逆变器的依赖性以及对负载和充电和放电的任何相关的电池效率。我们提出了一个组合的混合整数线性编程和粒子群优化(MILP-PSO)算法作为解决这些考虑因素的新方法。在算法中,MILP用于查找PSO的一些初始点,以便它可以找到更好的解决方案。此外,将一些额外的算法添加到PSO中以修改,从而提高其处理约束条件和局部最小问题的能力。仿真结果表明,该算法仅针对实际微电网单独执行比较MILP和PSO。结果还表明,在将MILP应用于调度时,简化或忽视ESS效率可能导致约束违规。

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