Metaheuristic Techniques for Optimizing Economic and Energy Management in Nano Grids: A Comprehensive Review

The emergence of Nano grids with integration of renewable energy bases gives an alternative to economic and energy management of small nodes of large grid systems for sustainable environmental development. The concept of Nano grid has become a critical zone of research for mitigation of climatic changes especially when integration with existing power systems. Nano grids are small-scale restricted energy systems that combine renewable energy homes and drive storages for demand-side management with advance energy security and lessen costs. In Nano grids, there are various challenges like distribution of renewable energy, minimization of operational costs, enhancement of grid stability, and assurance of sustainable energy management. In order to manage the structure and economic energy optimization, there is requisite of artificial intelligence designed algorithms that address the complex challenges related to generation, distribution and consumption of energy within Nano grids. This paper spectacle the literature review of metaheuristic techniques and their evolution in economic and energy management of Nano grids along with their comparative performance and emerging trends in the field. Additionally, the review identifies gaps in current research like unit commitment in hybrid models, integration of conventional with RES systems and real-time data processing for dynamic decision-making. Finally, this literature review provides a comprehensive understanding of various metaheuristic techniques that contribute to the operative economic and energy management of Nano grids, with offering insights for future research and practical solicitations in the energy sector.