Development of Hybrid Analitical-Particle Swarm Optimization Algorithm (Hapsoa) for Optimal Placement of Distributed Generation Units and Application to Jimeta Electricity Distribution Network (JEDN)
Optimal Location and Sizing of Distributed Generation (DG) units are a promising solution to many power system problems such as voltage regulation, power loss among others. In this work a method termed Hybrid Analytical Particle Swarm Optimization Algorithm (HAPSOA) was developed. HAPSOA comprised of two stage optimization approach. The first stage involved the determination of optimal DG sizes using analytical approach. In the second stage, the DG sizes obtained in the first stage were further optimized using a constrained Particle Swarm Optimization (PSO)-based approach while determining an optimal DG location on the network busses. HAPSOA was tested on IEEE 30, 33 and 69 buses for optimal DG placement and sizing. Results showed that a maximum DG size of 97 kW, 27.4 kW and 32.8 kW were deployed on the three buses. These improved the voltage profile of the buses by 27.78%, 25.21%, and 28.15% respectively. Optimal DG placement and sizing analysis was extended to Jimeta Electricity Distribution Network (JEDN). Here also, results showed that a maximum DG size of 2.165 kW was placed on NASSARAWO feeder, which resulted in an improved voltage of 34.33% and a power loss reduction of 18.86%. The smallest DG of 0.122kW was placed on FUTY network and this improved the voltage profile by 48.74% and a real power loss reduction of 28.57%. HAPSOA support a variety of DG placement simulation scenarios involving either heavily or lightly loaded networks by improving the voltage and increasing percentage loss reduction.