Optimizing Renewable Energy Allocation & Distribution Network Reconfiguration: A Multi-Criteria Approach with Modified Artificial Electric Field Algorithm

This study presents a groundbreaking approach for concurrent system reconfiguration and optimal placement of wind turbines (WTs) and photovoltaic panels (PVs) in radial distribution networks. Addressing the challenge of optimizing network performance and integrating renewable energy sources, the research leverages the modified artificial electric field algorithm (MAEFA) with a log-sigmoid function, representing a significant innovation in network optimization and renewable energy integration. Diverse scenarios on both 69-node and 33-node networks have unveiled nuanced trade-offs, providing valuable insights into the interplay between system reconfiguration and strategic placement of WTs and PVs. Introducing a pioneering multi-criteria methodology, the research concurrently considers multiple objectives, including power loss, voltage deviations, energy costs, and system reliability. The comprehensive objective function ensures a holistic evaluation of network performance, catering to the increasing need to optimize distribution networks amid rising renewable energy integration. Incorporating the proposed unscented transformation (UT) based stochastic model, this study enhances the analysis by considering the uncertainty inherent in load demand. The results have revealed that compared to the deterministic model, given the network demand uncertainty based on the UT-based stochastic model, power losses, voltage deviations, energy not served (ENS) values, and distributed generation (DG) costs are increased by 2.89%, 6.25%, 3.24%, and 5.61%, respectively.