Optimization of Renewable Energy Microgrids Using Smart Control Algorithms

Abstract

Renewable energy microgrids have emerged as a promising solution for decentralized power generation, enabling greater energy independence, resilience, and sustainability. With the increasing penetration of intermittent renewable sources such as solar and wind, smart control algorithms are essential for ensuring stable operation, optimal power flow, and reliability. This review provides a comprehensive examination of optimization techniques used in microgrid energy management systems. It explores classical control methods, heuristic optimization, model predictive control, and AI driven algorithms that address uncertainties in generation and demand. The paper assesses strategies for real-time scheduling, load balancing, energy storage optimization, and demand-side management. Challenges such as variability of renewable generation, communication constraints, cybersecurity risks, and integration with legacy systems are discussed in detail. The review also highlights recent advancements in multi-agent systems, reinforcement learning, and adaptive controllers that enhance microgrid flexibility and resilience. Practical case studies demonstrate how smart optimization improves energy efficiency, reduces operational cost, and supports carbon-neutral energy ecosystems. The paper concludes by outlining future research opportunities in digital twin modeling, transactive energy systems, and hybrid control architectures that will shape next- generation renewable microgrids.