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

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 li...

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Bibliographic Details
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
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Summary: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).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3220256