A Multi-Objective Optimization Method for the Best Concurrent Involvement of Energy Networks and Energy Hubs

Document Type : Research paper

Authors

1 Termiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.

2 Scientific and Practical Center of Immunology, Allergology and Human Genomics, Samarkand State Medical University, Samarkand, Uzbekistan.

3 Kimyo International University in Tashkent, Tashkent, Uzbekistan.

4 Tashkent State Transport University, 100167 Tashkent, Uzbekistan.

5 Department of Russian Language and Literature, Kokand State University, Kokand, Uzbekistan.

6 Samarkand State University of Architecture and Civil Engineering, Samarkand, Uzbekistan.

7 Department of “Architecture”, Urgench State University, Urgench, Uzbekistan.

8 Mamun University, 220912 Khiva, Khorezm, Uzbekistan.

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%.,

Keywords

Main Subjects

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Journal of Operation and Automation in Power Engineering
Volume 13, Special Issue
Intelligent and Sustainable Power Systems (ISPS): AI-Driven Innovations for Renewable Integration and Smart Grid Resilience
2025
Pages 62-74

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History

  • Receive Date: 27 November 2025
  • Revise Date: 23 December 2025
  • Accept Date: 25 December 2025
  • First Publish Date: 25 December 2025

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