Simultaneous Optimal Network Reconfiguration and Allocation of Four Different Distributed Generation Types in Radial Distribution Networks Using a Graph Theory-Based MPSO Algorithm

Abstract

In this paper, for four different types of distributed generation (DG) units, a graph theory-based modified particle swarm optimization (MPSO) algorithm is proposed for the simultaneous optimal distribution network reconfiguration (DNR) and allocation (placement and sizing) of multiple DG units to minimize the total real power loss of the radial distribution network (RDN) while fulfilling all system operational constraints. Different cases are carried out for DNR and DG integration to evaluate the effectiveness of the proposed algorithm. To evaluate the objective function, an intelligent graph theory-based backward/forward (BW/FW) sweep load flow technique that can manage any topological alteration owing to DNR and DG integration is introduced. The proposed algorithm is assessed using IEEE 33-bus and IEEE 69-bus radial distribution systems and results are obtained using MATLAB software. The obtained simulation results show that the proposed algorithm can provide a wonderful solution in terms of real power loss minimization and voltage profile enhancement (compared with the base case, loss reduction of 74.29%, 92.15%, 54.3%, and 45.87% for DG type1, DG type2, DG type3 and DG type4, respectively for IEEE 33-bus, and loss reduction of 84.16%, 97.79%, 70.62%, and 63.01% for DG type1, DG type2, DG type3, and DG type4 respectively for IEEE 69-bus).

An Improved Northern Goshawk Optimization for Optimal Reconfiguration of Modern Electrical Distribution System for Loadability Enhancement

“…The NGO algorithm advises scouting a target area with a radius of R. Eqs. (21)(22)(23) are used to mathematically model the notions of the second phase.…”

“…Then, the algorithm keeps updating the population members based on Eqs. (18)(19)(20)(21)(22)(23) until the end of the program. The best suggested solution from the algorithm iterations is offered as a quasioptimal solution for the optimisation problem once the NGO technique is fully applied.…”

“…A meta-heuristic moth-flame algorithm (MMFA) for ONR and placement of PV and WT based DGs while reducing power losses, improving the voltage profile and stability, and increasing dependability is shown in [21]. Further, graph theory-based modified particle swarm optimization (MPSO) [22], hybrid plant growth simulation (PGS)-particle swarm optimization (PSO) [23], and iterative branch exchange (IBE) [24] are proposed for solving simultaneous allocation of DGs and ONR problems.…”