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Assistant Professor, Kalinga University , Naya Raipur, Chhattisgarh , India
Assistant Professor, Kalinga University , Naya Raipur, Chhattisgarh , India
The evolution of traditional microgrids to integrate distributed renewable energy sources, such as solar and wind, has transformed them into complex cyber-physical systems (CPSs). While this enhances sustainability, it introduces challenges related to intermittency, uncertainty, and real-time operational decision-making. A major concern is the reliance on data-driven AI controllers, which often operate as black-box models, limiting trust and transparency in safety-critical environments. This research proposes an intelligent cyber-physical microgrid management framework based on Explainable Artificial Intelligence (XAI) to improve operational reliability, efficiency, and transparency under high renewable penetration. The framework integrates physical power components with cyber elements through a unified sensing, communication, and control architecture, enabling AI-driven decisions supported by predictive models for renewable forecasting, load balancing, and optimal power dispatch. An embedded explainability layer provides feature- and rule-based insights for all control actions, fostering operator trust and regulatory compliance. The adaptive control strategy coordinates distributed energy resources, energy storage systems, and controllable loads to respond dynamically to varying generation and demand. Simulation results show that, compared with conventional rule-based and non-explainable AI controllers, the proposed approach increases renewable utilization by 28% and reduces power imbalance by 32%, while maintaining superior voltage stability. The explainability layer further enhances diagnostic capabilities and decision justification. These results demonstrate that incorporating transparency and robustness in XAI-enabled microgrid management is as vital as operational performance, offering scalable and practical solutions for next-generation smart grids and sustainable energy infrastructures.
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