New research introduces a sophisticated Grey Wolf-based multi-objective optimization technique designed to enhance the self-sufficiency, reliability, and economic feasibility of wind-solar-battery microgrids. This innovative approach, detailed in a recent study, offers a robust solution for optimally sizing distributed energy resources (DERs) in residential communities, including high-rise urban buildings and remote areas where traditional grid connections are unviable. The methodology directly addresses the inherent unpredictability of renewable energy sources, a key challenge in integrating them into stable power systems.

The proposed optimization algorithm meticulously determines the optimal sizing of wind turbines, solar photovoltaic arrays, and battery storage systems based on specific microgrid load profiles. Unlike conventional methods, this technique employs a three-dimensional objective function that simultaneously minimizes renewable energy costs while ensuring high reliability and efficient energy utilization. This comprehensive approach aligns with the global energy trilemma, balancing economic viability, system reliability, and environmental sustainability, which is paramount for sustainable energy transitions.

The study highlights the growing prominence of microgrids as essential components of modern energy infrastructure, particularly for decentralized power supply. While commercial tools exist for system sizing, their limitations in handling complex, stochastic variables and flexible objective functions necessitate advanced custom optimization techniques. The Grey Wolf-based algorithm, a meta-heuristic approach, demonstrates superior capability in navigating the complex constraints of microgrid design, overcoming the limitations of classical and some probabilistic methods that often converge to local optimum points.

Researchers developed a detailed mathematical model for the wind-solar-battery configuration, ensuring precise representation of each distributed unit, from PMSG-based wind generators to PV arrays and battery storage units. The model accounts for power fluctuations and aims to smooth intermittent renewable output through effective battery integration. Furthermore, a crucial aspect of the evaluation involves a numerical analysis of the capacity degradation factor, providing a realistic assessment of battery lifetime and its impact on the overall system's long-term performance and operational costs. This holistic evaluation across various configurations underscores the algorithm's potential to deliver highly efficient and enduring microgrid solutions.