Strategic Allocation of Renewable Energy Resources: A Multi-Objective Optimization Framework for Enhanced Efficiency and Social Equity

The study offers a multi-objective optimization model that aims to enhance the strategic distribution of renewable energy sources (RES) across residential sectors within a given geographical region—New South Wales, Australia. Targeting Sustainable Development Goal 7 (SDG-7), the model adopts two basic mathematical principles—Pigeonhole Principle and Arithmetic Progression—to offer equitable and optimal energy supply to households. Based on real data from solar, wind, and hydroelectric power facilities, the approach takes into account capacity factors, power station characteristics, and energy requirements for 3.4 million homes. Sensitivity analyses and case-based simulations identify trade-offs between reducing energy loss, cost, reliability, and social equity. Findings indicate that mathematical models combined with energy infrastructure data can make significant contributions to policy and operational planning for decentralized energy systems. The research concludes by recommending the application of dynamic optimization models, machine learning, and real-time monitoring in future studies to enable scalable, resilient, and socially equitable distribution of renewable energy