Unveiling Mountaineering Team-Based Optimization Algorithm forPerformance Enhancement of Radial Distribution

The inadequate capacity of Radial Distribution Networks (RDNs) to accommodate renewable energy sources and the suboptimal allocation of Distributed Generation Sources (DGs) pose significant challenges. This paper introduces a cooperative approach utilizing meta-heuristic algorithms to address these challenges simultaneously through Distribution Network Reconfiguration (DNR) and the optimal allocation (i.e., siting and sizing) of DGs. Integrating these two tasks is a complex endeavor, particularly within a multi-objective framework. To tackle this complexity, a multi-objective function is formulated, aiming to minimize ohmic losses, enhance voltage profiles, and reduce DG costs while adhering to system constraints. Fuzzification techniques are employed to transform this multiobjective function into a single-objective problem, simplifying the optimization process. DNR, involving optimized changes to tielines between distribution system feeders without altering their radial structure, is modeled as a solution to facilitate the optimal DG allocation and cost reduction. This study focuses on renewable-based DGs, such as Wind Turbines (WTs). This paper introduces a robust algorithm called Mountaineering Team-Based Optimization (MTBO), for the first time, applied to the challenge of simultaneous network Reconfiguration and DGs optimal allocation. MTBO is mathematically formalized to provide a robust optimization approach inspired by social behavior and human cooperation. Experimental investigations encompass the simultaneous integration of DNR and DG optimal allocation, utilizing the 33-bus IEEE and 69-bus IEEE distribution test systems to demonstrate the method’s efficacy and superiority. Simulation results are compared under uncertain conditions, highlighting MTBO’s efficiency compared to alternative algorithms like the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The outcomes affirm the feasibility and effectiveness of the proposed method, indicating its effectiveness and robustness in comparison with competitive algorithms. The repositioning of optimal DG units significantly improves performance, quality, and reliability in RDNs, as evidenced by these simulations.