Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

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