AI-Driven Energy Management for Interconnected Microgrids: A Comparative Performance Analysis
P. Motevakel, C. Roldán-Blay, C. Roldán-Porta Institute for Energy Engineering, Universitat Politècnica de València, Valencia (Spain). 2026-06-27

Abstract

This paper proposes a lightweight AI-based energy management framework for the coordinated operation of interconnected microgrids and evaluates its performance relative to conventional non-cooperative operation. The study examines two microgrids with photovoltaic generation and battery storage operating in parallel with the main grid. The proposed coordination strategy is based on key system variables, including battery state-of-charge and local power imbalance, enabling real-time decision-making without reliance on complex optimization or large-scale data-intensive training, while requiring only a lightweight offline training phase. A time-series simulation framework is used to assess system performance through key indicators, including grid-imported energy, excess energy, battery utilization, and power exchange. The results show that cooperative operation improves performance and resource utilization, reducing total grid-imported energy from 279.71 kWh to 269.61 kWh and curtailed energy from 69.66 kWh to 61.00 kWh. More balanced battery state-of-charge profiles also confirm improved battery management through coordinated energy sharing. Although the improvements are moderate due to the short simulation horizon, the findings demonstrate that even simple coordination strategies can enhance flexibility and resilience in networked microgrids. The proposed framework provides a promising and scalable basis for real-time energy management, with future work focused on long-term techno-economic assessment and optimal system design.

Key words: AI-based decision making, cooperative microgrids, energy management systems, interconnected operation, renewable energy integration.

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