Maximizing Renewable Energy Integration in Distribution Networks: An ESCSO Algorithm for Optimal PV and WT Planning under Uncertainty

Abstract

Abstract With the rapid growth of technology and population, there has been a corresponding increase in electricity demand and system load. To meet this demand, the installation of renewable energy sources (RESs), such as wind turbines (WTs) and photovoltaics (PVs), in distribution systems is widely accepted as the best solution. This paper proposes an efficient modified method called ESCSO, which combines the Sand Cat Swarm Optimizer (SCSO) and Marine Predators Algorithm (MBA) algorithms to obtain the optimal allocation of WT and PV in radial distribution systems (RDS), with and without inverters. The presented objective function considers system loss, variable load, and the probabilistic output of WT and PV over 24 hours. The IEEE 69-bus RDS is used as the test system, with its equality and inequality constraints. The ESCSO method is compared with the original SCSO and MBA algorithms to measure its efficiency. The results show that installing multiple WT and PV yields better results than using a single WT and PV in RDS. Furthermore, incorporating WT and PV with an enabling inverter yields better results than using WT and PV without an enabling inverter in RDS.