Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis

Abstract

• The majority of academics focused more on Distributed Generations (DG) ideal siting and size with less research being done on economic studies. • The economic analysis of the ideal sizing and placement of distributed generation is also viewed as a contribution to this work. In addition to technical analysis of the placement and sizing of DGs, economic analysis is performed as an added advantage. • Additionally, the majority of the research was concentrated on placing a single DG; however, in this work simultaneous installation of DGs at various sites was carried out and its techno-economic analysis was examined. • Furthermore, simultaneous multi-objective formulation tasks have included assessing annual economic losses by positioning DGs optimally and minimizing real power loss. In radial distribution networks, the appropriate placement of properly sized Distributed Generation (DG) units can significantly improve the performance of the system. The biggest techno-economic benefits can be obtained by reducing annual economic losses that include the expenses of deployment, operation and maintenance along with voltage variations and power loss across the buses. The current problem is examined in light of various multi-objective frameworks as well as the optimum compromise solution also termed the Pareto-optimal solution is presented. When dealing with a multi-objective optimization problem, certain constraints on equality and inequality are also examined. The focus of this paper is on a one-of-a-kind multi-objective whale optimization (MOWOA) algorithm for multi-objective problem-solving. To test its effectiveness, the method that was suggested is implemented on radial bus distribution systems IEEE-33 and IEEE-69. This paper also includes a comparison with other recent multi-objective algorithms such as opposition-based chaotic differential evolution (OCDE), Krill herd algorithm (KHA) and Power Loss Sensitivity Factor and Simulated Annealing (LSFSA). It has been discovered that the method proposed may improve power loss, annual economic loss mitigation and voltage profile improvement.