Deep learning-enabled spatiotemporal sustainable energy strategy for rural multiple microgrids

Sustainable economic development entails effective energy management. Yet there is a dearth of schemes that jointly capture spatial and temporal energy dynamics, while ensuring fairness in underserved regions for geographically distant microgrids through virtual energy sharing. This work proposes a spatiotemporal framework, for microgrid energy management, stimulating exchange of excess renewable energy. The recommended scheme facilitates intelligent utilization of renewable energy, with moderated costs of generation and distribution. A simulated case study of two rural microgrid locations in India, compliant with India's National Grid, validated the propriety of the proffered approach. Findings showed a 30.7% reduction in dependency on the main grid, with drops in annual energy imports from (19.1 - 13.2) MWh. Besides, multi-microgrid coordination delivered cost savings exceeding INR 15,000, validated during monsoon and winter seasons. This study corroborates the potential of coordinated microgrids to enhance resilience, inclusivity and clean energy, in support of UN Sustainable Development Goals 7, 9, 11, 12, and 13.