THEORETICAL METHODS FOR THE OPTIMAL CONFIGURATION OF BACKUP POWER CAPACITY IN MICROGRIDS

Abstract

The growing reliance on microgrids as decentralized energy solutions has intensified the need for reliable backup power capacity that balances operational efficiency, economic viability, and environmental sustainability. This study investigates theoretical methods for the optimal configuration of backup systems in microgrids, employing a mixed-method approach that integrates quantitative optimization models with scenario-based simulations. Using mixed-integer linear programming (MILP), metaheuristic algorithms, and empirical reliability assessments, the research evaluates generator performance, battery storage characteristics, renewable energy contributions, load demand profiles, outage recovery dynamics, and scenario-driven performance indicators. Results across nine comprehensive tables and twelve diverse figures reveal that backup power optimization significantly reduces fuel consumption, CO₂ emissions, and lifecycle costs, while enhancing resilience and renewable energy penetration. Notably, hybrid systems that combine batteries, renewables, and diesel backup consistently outperform single-technology configurations by achieving higher efficiency and reliability under variable demand and uncertainty conditions. The study also demonstrates that scenario-based planning captures the complexity of real-world contingencies better than deterministic models, offering critical insights for policymakers and energy planners. By advancing a theoretical framework that integrates cost, reliability, and sustainability objectives, this research contributes to both academic discourse and practical microgrid deployment strategies. The findings underscore the importance of hybrid optimization frameworks and scenario analysis in designing resilient, sustainable, and future-ready microgrids, paving the way for further work on adaptive control systems and regulatory integration.