Document Type : Research paper
Authors
1
Professor of the Department of Engineering of Electrical Machines and Drives, Tashkent State Technical University, University street No2, Tashkent, Uzbekistan
2
PhD student, "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers institute" National Research University, Kari Niyazov Street 39, 100000, Tashkent, Uzbekistan
3
Kimyo international university in Tashkent, Shota Rustaveli street 156, 100121, Тashkent, Uzbekistan
4
PhD, Assistant Professor, Alfraganus University, Uzbekistan
5
Urgench State University, 14, Kh.Alimdjan str, Urganch, Khorezm, Uzbekistan
6
DSc, Associate Professor, "Tashkent institute of irrigation and agricultural mechanization engineers" National Research University, 100000 Tashkent, Uzbekistan
7
PhD student, Research Institute of Environmental and Nature Protection Technologies, 100000, Tashkent, Uzbekistan
8
Department of General Professional Subjects, Mamun university, Khiva, Uzbekistan
10.22098/joape.2025.18916.2468
Abstract
This paper proposes a fully decentralized reinforcement learning–based energy management framework for hybrid electrical–thermal microgrids with distributed energy resources. Uncertainties in renewable energy generation, variations in load demand, and the nonlinear nature of battery systems make it difficult to achieve optimal energy management in microgrids. Additionally, using centralized controller techniques in large-scale systems increases computational complexity and makes controller procedure implementation more challenging. This study proposes a fully decentralized multi-agent architecture in which the stochastic performance of agents in the microgrid is modeled using Markov decision processes. This model treats consumers, batteries, and distributed thermal and electrical resources as intelligent, self-governing agents that learn from their surroundings and converge to their best policies through decentralized exploitation. The proposed model-free learning-based approach is designed to not only maximize the profits of producers but also minimize the costs for consumers and reduce the microgrid's reliance on the main grid. Finally, using real-world data from renewable power plants and electricity market data, the performance of the proposed method is evaluated through simulation and accuracy assessment.
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