Intelligente microgrid-optimierung einsatzplanung
This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total. This paper proposes an integrated framework to improve microgrid energy management through the integration of renewable energy sources, electric vehicles, and adaptive demand response strategies. An optimization strategy based on machine learning employs a support vector machine for forecasting.
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