Document Type : Research paper
Authors
1
Teacher, Termiz University of Economics and Service, Farovon street 4-b, Termez, Surxondaryo, Uzbekistan
2
Professor, Scientific and Practical Center of Immunology, Allergology and Human Genomics, Samarkand State Medical University, Samarkand, Uzbekistan
3
Kimyo international university in Tashkent, Uzbekistan
4
PhD, Associate professor, Tashkent state transport university, 100167 Tashkent, Uzbekistan
5
PhD, Associate professor, Department of Russian Language and Literature, Kokand State University, Kokand, Uzbekistan
6
Samarkand State University of Architecture and Civil Engineering, Samarkand, Uzbekistan;
7
Teacher, Department of “Architecture”, Urgench State University, Urgench, Uzbekistan
8
PhD, Associate Professor, Mamun university, 220912 Khiva, Khorezm, Uzbekistan.
10.22098/joape.2025.18918.2469
Abstract
In this study a framework for the best possible simultaneous involvement of energy systems and energy hubs in day-ahead energy shops is presented. The suggested method is a multi-objective optimization problem and takes into account both wholesale and retail market structures. The primary objective function seeks to reduce the overall energy costs of thermal, gas, and electricity networks. By optimizing the difference between energy purchase and sales costs, the second goal function aims to reduce the energy costs of energy hubs in the retail market. The operational model of active resources and loads inside the energy hubs, as well as the optimal power flow calculations of the integrated energy systems, place limitations on the suggested model. To solve the optimization problem, a Pareto-based weighted sum method combined with fuzzy decision-making is employed to derive a compromise optimal solution. Finally, the proposed framework is implemented on a test system, and the numerical results confirm its effectiveness in successful economic performance of energy hubs and simultaneously enhancing the cost-effective and operational conditions of integrated energy networks which reduce energy cost up to a ~40%.,
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