Component capacity optimization of a renewable energy system using data-driven two-stage algorithmic approach

Navigating the complexities of optimizing Renewable Energy System components requires addressing economic, technical, and regulatory challenges. However, existing research often overlooks crucial aspects such as grid economic interactions, the limited scope of Renewable Energy System objectives, and the scalability of energy strategies. This study introduces a novel two-stage optimization algorithm integrating a non-dominated sorting algorithm-II for capacity optimization and a multi-integer linear programming model for energy management, offering a comprehensive solution with diverse decision-making metrics. With implementation of the dynamic population decay algorithm, the computation time was reduced by 60.42%. The study identified nine Pareto efficient configurations, with the highest Internal Rate of Return reaching 4.86% and a maximum Energy Independence Score of 0.51. The financial cost associated with improving environmental indicators surged by 839%. Furthermore, the proposed optimization approach outperformed the rule-based non-dominated sorting algorithm, achieving an 18.12% higher Internal Rate of Return with a comparable energy independence level. This decision-making framework guides system owners towards medium-sized systems for balanced objectives while offering flexibility for various sizes tailored to specific local regulations, energy markets, and goals, extending its applicability to diverse international contexts.