Optimization of microgrid scheduling: current approaches and prospects

THE RELEVANCE. This article provides a review of modern approaches to the optimization of microgrid planning, including multi-objective optimization methods, uncertainty considerations, and the application of intelligent algorithms. Microgrids, as a key component of modern energy systems, integrate distributed energy resources, storage devices, and loads, thereby enhancing the efficiency, reliability, and environmental sustainability of energy supply. METHODS. The paper examines key optimization models, such as minimizing operational costs, reducing emissions, and improving power supply reliability. Special attention is given to methods for addressing uncertainties related to renewable energy sources and load variability, as well as the role of energy storage systems and demand response. The article also analyzes traditional and intelligent optimization algorithms, including genetic algorithms, particle swarm optimization, and deep learning. RESULTS. The application of modern models such as SRSM-SOCR, modified Bet algorithm (MBA), deep reinforcement learning (DRL) and deep recurrent neural network (DRNN) made it possible to reduce the operating costs of microgrids by 18-25%, increase the share of generation from renewable sources to 70-75% and reduce CO₂ emissions by up to 60%. Real-life examples of microgrids in Germany and Greece are also presented, confirming the effectiveness of these approaches. CONCLUSION. Based on a literature review, key directions for future research are identified, such as the integration of transfer learning and reinforcement learning to enhance model adaptability. The findings of this study can be useful for developing effective microgrid management strategies in the context of increasing renewable energy penetration and evolving energy system requirements.

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