Techno-economic analysis of distribution system at various load models using MEWOA algorithm

In response to the market's increasing need for electricity and the escalating technical and environmental challenges, the Power System (PS) sector has strongly emphasised the escalating technical and ecological challenges, and the PS sector has placed a strong emphasis on integrating distributed energy resources (DERs) into distribution systems (DS). However, if not allocated optimally, integrating DERs can provide various technical topics such as power quality, stability, reliability, and voltage management concerns. Therefore, creating effective and efficient optimisation techniques to solve issues of DER integration is crucial. In this paper, the integration of DERs into DS is utilised by the Multi-Objective Evolution of the Whale Optimisation Algorithm (MEWOA). MEWOA is an optimisation method inspired by humpback whales' hunting strategies and has demonstrated promising results in solving challenging optimisation issues. The suggested approach tries to optimise the location and sizing of DERs in DS while considering several factors, including voltage variation, Power loss (PLoss) reduction, and operational cost. The extensive simulations are run on the Indian 28-bus and IEEE 69-bus distribution systems to show the viability of the suggested approach. The findings demonstrate that the proposed method can significantly enhance the voltage profile, lessen PLoss, lower the annual operating expenses and save revenue from PLoss minimisation. The results also show that MEWOA outperforms other optimisation techniques like the Grasshopper Optimisation Algorithm (GOA), the Dragonfly Algorithm (DA), and the Whale Optimisation Algorithm (WOA) in terms of convergence speed and solution quality. As a result, the suggested way for integrating DERs into DS utilising MEWOA is a successful and efficient optimisation technique. The results demonstrate that the proposed approach can enhance distribution system performance while lowering operational expenses and environmental impact.