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Optimal allocation of distributed energy resources through simulation-based optimization

机译:通过基于仿真的优化来优化分布式能源的分配

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摘要

In this study, a new two-layer simulation-based optimization (SBO) approach is proposed to determine the optimal allocation and capacity of distributed energy resources (DER) in a power distribution system with an imperfect grid connection. In the first layer, a dynamic optimal power flow (DOPF) routine is embedded in a simulation algorithm that is run for each system configuration based on a set of operational rules to calculate the cost and reliability level of the system over one year. In the second layer, a particle swarm optimization (PSO) algorithm uses the outputs of the first layer to optimize the location and capacity of wind turbines, PV panels, and grid-scale batteries, in order to minimize cost while meeting reliability requirements. The proposed approach is tested on a 16-bus U.K. generic distribution system (CDS) under different grid availability conditions, and the results are reported. The merits and limitations of the proposed approach are discussed, and the differences between it and rule-free constrained optimization approaches are highlighted.
机译:在这项研究中,提出了一种新的基于两层仿真的优化方法(SBO),以确定在电网连接不完善的配电系统中分布式能源(DER)的最优分配和容量。在第一层中,将动态最佳功率流(DOPF)例程嵌入到仿真算法中,该仿真算法基于一组操作规则针对每种系统配置运行,以计算一年内系统的成本和可靠性水平。在第二层中,粒子群优化(PSO)算法使用第一层的输出来优化风力涡轮机,PV面板和网格规模电池的位置和容量,以便在满足可靠性要求的同时将成本降至最低。在不同的电网可用性条件下,该提议的方法已在16总线的英国通用配电系统(CDS)上进行了测试,并报告了结果。讨论了该方法的优缺点,并重点介绍了该方法与无规则约束优化方法的区别。

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