AI-Enabled Virtual Resilience Assessment Framework for Community-Managed Rural Hybrid Microgrids: A Case Study Approach

Reliable electricity access in remote tribal regions of India remains constrained by weak grid infrastructure and seasonal supply interruptions. This paper presents an AI-assisted virtual resilience assessment framework for community-managed rural hybrid microgrids using three real-world deployments in Central India: Harshdiwari, Kanarkheda, and Mudpar. The deployed 3 kW off-grid systems integrate solar photovoltaic generation, battery storage, and biogas-based auxiliary backup under a decentralized Rural Energy Bank model.

Operational field data, HOMER Pro-based virtual modelling, and AI-assisted analytical methods were used to evaluate system resilience under varying climatic and load conditions. Key indicators such as photovoltaic generation, renewable fraction, battery state-of-charge, unmet load, and generator dependency were analyzed along with socio-economic parameters including kerosene reduction and productive load utilization.

Results show that all systems satisfied more than 98% of annual energy demand, with renewable energy fractions between 97.9% and 100%, and unmet load below 1.05%. The systems maintained nearly 1.5 days of battery autonomy despite monsoon-induced variability. The proposed framework transforms operational rural hybrid microgrids into AI-assisted virtual resilience models for developing scalable and repeatable decentralized energy systems.