Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

• Published in: IEEE access 2022, Vol.10, p.118108-118122
• Main Authors: Chang, Gary W., Chinh, Nguyen Cong, Sinatra, Christian
• Format: Article
• Language: English
• Subjects: Capacity planning; Distributed generation; Genetic algorithms; hosting capacity; Inverters; Linear programming; Load flow analysis; Maximization; metaheuristic approach; Metaheuristics; Particle swarm optimization; Photovoltaic cells; Planning; Power flow; reverse power flow; smart inverter
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3220256

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system's PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).