An efficient hybrid multi-population algorithm (HMPA) for enhancing techno-economic benefits

This paper presents an innovative multi-objective optimization methodology aiming to address the complex problem of optimal network reconfiguration simultaneously with the allocation of capacitor banks (CBs) and distributed generations (DGs) in a radial distribution system (ONRSACD). Integrating CBs, DGs, and network reconfiguration (NR) in distribution systems enhances power quality and reduces energy costs at distribution substations. Nevertheless, their incorporation poses challenges due to the significant associated investment costs. The proposed approach leverages the hybrid multi-population algorithm (HMPA) to maximize techno-economic benefits and satisfy critical operational constraints to achieve a robust solution. The primary objective is to achieve a good compromise between mutually conflicting costs: substation energy costs and device investment costs. In pursuit of a realistic network behavior model, the method considers the hourly variations of diverse load types and DGs output. To address the inherent multi-objective nature of the problem, the proposed approach seamlessly integrates the fuzzy logic tool. Each objective function is converted into the fuzzy domain using its respective membership function, and upper and lower bounds are determined through a carefully devised strategy. Validation and performance assessment of the proposed methodology are conducted on the 33-bus and 69-bus radial distribution test systems. Additionally, the efficacy of the hybrid multi-population algorithm (HMPA) is benchmarked against several well-established algorithms, including artificial ecosystem-based optimization (AEO), Harris Hawks optimization (HHO), particle swarm optimization (PSO), and moth flame optimization (MFO).