With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

中文翻译：

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利用遗传算法对分布式发电进行最优布置以及使用模糊推理系统进行能源管理策略的需求

随着负载需求和功率损耗的增加，公用事业网络中的设备通常会在其极限范围内运行，从而迫切需要安装新的分布式发电机（DG）。适当安置他们是充分获得利益的先决条件之一；否则，可能会导致其性能变差。如果未安装有效的能源管理系统（EMS），这甚至可能导致进一步恶化。首先，针对这些问题，本研究利用遗传算法（GA）在配电系统中正确放置新的DG。该方法基于系统损耗，电压曲线和相角跳变。其次，利用模糊推理系统设计能源管理模型。然后在重载和故障条件下分析模型。这项研究是在模拟环境中的六总线径向测试系统以及实时电源硬件在环（PHIL）设置下进行的。结论是，3.33 MVA同步DG的最佳放置是在负载中心附近，并且通过在操作周期的大约2.5个周期内缓解不同的意外事件，证明了所建议EMS的鲁棒性。