Escalating load demand at the distribution level necessitates the incorporation of distributed generators (DGs) into power systems. Consequently, the utilization of DGs into power systems based on renewable energy has become a primary approach in the pursuit of affordable and sustainable energy supply. However, this integration introduces significant challenges owing to the inherent variability and uncertainty of both the load and renewable energy sources, particularly wind‐ and photovoltaic‐based DG. This paper addresses these challenges by proposing a comprehensive strategy that utilizes the minimax regret criterion to optimize two types of DG allocation throughout an entire year with the latest metaheuristic algorithms in the presence of uncertain generation in hybrid renewable energy systems. In addition to this primary contribution, this study considers proportional probability–based optimal battery scheduling using a metaheuristic algorithm. The key achievement of this study is the formulation of a multiobjective optimization (MOP) framework that evaluates four objective indices: active power loss (APL), reactive power loss (RPL), total voltage deviation (TVD), and line stability (LS). To the best of the authors’ knowledge, this is the first study that considers all four indices collectively for nonradial systems for such a comprehensive one‐year problem. Moreover, this study analyzes the performance of the newly developed growth optimizer (GO) algorithm for optimal DG allocation and battery scheduling. Three case studies are meticulously examined (1) a system with optimal battery scheduling, (2) a system with optimal DG allocation, and (3) a system with optimal battery scheduling and DG allocation. These case studies provide valuable insights into the benefits and trade‐offs of each approach, thereby demonstrating the importance of the proposed optimization framework. The findings are compared with those of the battle royale optimization (BRO) algorithm and reveal that optimal DG allocation significantly improves the performance of the hybrid renewable energy (HRE) system throughout the year.
Abbas et al. (Thu,) studied this question.