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.
Khan AA, Beg OA, Jin YF, Ahmed S. An explainable intelligent framework for anomaly mitigation in cyberphysical inverter-based systems. IEEE Access. 2023 Jan;11.
2.
Trivedi R, Patra S, Khadem S. Data-centric explainable artificial intelligence techniques for cyber-attack detection in microgrid networks. Energy Reports. 2025 Jun 1;13:217-29.
3.
Gokulraj K, Venkatramanan CB. Advanced machine learning-driven security and anomaly identification in inverter-based cyber-physical microgrids. Electric Power Components and Systems. 2024 Apr 24;1-8.
4.
Suprabhath Koduru S, Machina VS, Madichetty S. Cyber-attacks in cyber-physical microgrid systems: A comprehensive review. Energies. 2023 Jun 7;16(12):4573.
5.
Chen D, Lin X, Qiao Y. Perspectives for artificial intelligence in sustainable energy systems. Energy. 2025 Mar 1;318:134711.
6.
Nsoh J. Adaptive AI Frameworks to Secure and Manage Distributed Energy Networks in Smart Urban Environments. Science and Engineering. 2025;2:16-52.
7.
Sanjalawe Y, Fraihat S, Makhadmeh SN, Alzubi E. AI-powered smart grids in the 6G era: A comprehensive survey on security and intelligent energy systems. IEEE Open Journal of the Communications Society. 2025 Sep 11.
8.
Álvarez-López C, González-Briones A, Li T. Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State-of-the-Art Review. Electronics. 2026 Jan 15;15(2):385.
9.
Rehman MA, Talani RA, Sohu N, Safi A, Khan AH, Batool SN. A Deep Learning–Empowered Artificial Intelligence Framework for Intelligent Fault Detection and Performance Optimization in Photovoltaic Solar Energy Systems. Spectrum of Engineering Sciences. 2025 Dec 11;3(12):207-37.
10.
Egbuna IK, Salihu FB, Okara CC, Olayiwola DE. Advances in AI-powered energy management systems for renewable-integrated smart grids. World J. Adv. Eng. Technol. Sci. 2025;15(2):2300-25.
11.
Nallakaruppan MK, Shankar N, Bhuvanagiri PB, Padmanaban S, Khan SB. Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future. Ain Shams Engineering Journal. 2024 Jun 1;15(6):102740.
12.
Ukoba K, Olatunji KO, Adeoye E, Jen TC, Madyira DM. Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment. 2024 Nov;35(7):3833-79.
13.
Li TT, Huang Y, Hua Y, Hu T, Yang H, Alhazmi M, Alsaif S, Liu G. Cyber–physical resilience and security strategies for renewable energy-driven IoT power grids. Sustainable Energy Technologies and Assessments. 2025 Dec 1;84:104696.
14.
Habib A, Opy AI. Artificial Intelligence in Renewable Energy: A Pathway Toward an Adaptive, Equitable, and Sustainable Future–Systematic Review. Paradise. 2025 Sep 18;1(1):1-0.
15.
Ali O, Mohammed OA. A review of multi-microgrids operation and control from a cyber-physical systems perspective. Computers. 2025 Sep 25;14(10):409.
16.
Wagle R, Tricarico G, Sharma P, Rueda JL, Gonzalez-Longatt F. Experiences in a cyber-physical cosimulation testbed development for a smart-er distribution network. In2023 IEEE PES Conference on Innovative Smart Grid Technologies-Middle East (ISGT Middle East) 2023 Mar 12 (pp. 1-5). IEEE.
17.
Karthikeyan A, Karthikeyan K, Swathika OG. Cyber-physical security in a stand-alone photovoltaic system for rural electrification. Next-generation cyber-physical microgrid systems. 2024 Jan 1:29-75.
18.
Halgamuge MN. Leveraging deep learning to strengthen the cyber-resilience of renewable energy supply chains: A survey. IEEE Communications Surveys & Tutorials. 2024 Feb 12;26(3):2146-75.
19.
Arévalo P, Benavides D, Ochoa-Correa D, Ríos A, Torres D, Villanueva-Machado CW. Smart microgrid management and optimization: a systematic review towards the proposal of smart management models. Algorithms. 2025 Jul 11;18(7):429.
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