Article

Optimal Power Management in an Electrical Distribution Network With Demand Response Programs and Local Operation of Battery Storage Systems

Ali DaichiDepartment of Electrical Engineering , Islamic Azad University, Branch of Central Tehran , Tehran , IranForoozan SadriSpace Planner, Space Planning and Management , Lewis Katz School of Medicine , Temple University , Philadelphia , Pennsylvania, USA , temple.eduAidin Karimi MoghaddamDepartment of Mathematics and Computer Science , School of Science , University of Zanjan , Zanjan , Iran , znu.ac.irSaeed TalebianDepartment of Interior Architecture , University of North Carolina at Greensboro (UNCG) , Greensboro , North Carolina, 27412 , USAGayrat BekbergenovDepartment of Economics , Mamun University , Khiva , UzbekistanMirjalol Ismoilov Ruziboy UgliDepartment of Transport Systems , Urgench State University , Urgench , Uzbekistan , urdu.uzBarno MatchanovaDepartment of National Idea and Philosophy , Urgench State Pedagogical Institute , Urgench , UzbekistanOrtikjon MamasalievDepartment of Economy , Tashkent State Technical University , Tashkent , 100095 , Uzbekistan , tdtu.uzKhudaybergen KochkarovDepartment of Special Science , Tashkent State University of Economy , Tashkent , UzbekistanGularam MasharipovaDepartment of Social Science , Alfraganus University , Tashkent , UzbekistanKamol KomilovDepartment of Architecture , Tashkent University of Architecture and Civil Engineering , Tashkent , UzbekistanMohammad Tarek AzizDepartment of Computer Science and Engineering , Rangamati Science and Technology University , Rangamati , BangladeshRenzon Daniel Cosme PechoDepartment of Environmental Engineering , North Private University , Lima , PeruIkhlosbek JumabayevDepartment of Urgench Innovation University , Pedagogy and Primary Education Methodology , Urgench , Uzbekistan

International Transactions on Electrical Energy Systemsjournal2025en

ABI

Abstract

In recent years, managing power in the electrical systems that utilize intelligent infrastructures has become a modern solution for operators. This technology enables more effective control and improves the overall performance of electrical networks. Accordingly, this paper focused on economic and technical power management in an intelligent electrical distribution network (IEDN) with demand response programs (DRPs) at day‐ahead. The proposed approach is implemented in IEDN by the bilayer optimization approach considering the contribution of the electrical distribution company (EDC) and consumers. In the first layer, implementation of the DRPs such as local power generation (LPG) by battery storage systems (BSSs), power load curtailment (PLC) program, and power load shifting (PLS) program is scheduled for minimizing bills of consumers. On the other side, in the second layer optimization, income of EDC is maximized and power losses of IEDN are minimized considering scheduled load demand in the first layer optimization. The optimization in both the layers is modeled as multiobjective functions, and optimization of consumers’ bills is done subject to power prices in EDC. The effect of the suggested approach is examined on technical metrics such as voltage profile and peak‐to‐average ratio (PAR) index. The improved grasshopper optimization algorithm (IGOA) and Shannon entropy decision‐making method are used for solving bilayer optimization approach and multiobjective functions. In the end, the results reveal the optimal values of the objective functions of each layer, based on a comparative examination of different case studies, thereby considering consumer engagement.

Topics

Smart Grid Energy Management Optimal Power Flow Distribution Microgrid Control and Optimization

Identifiers

DOI: 10.1155/etep/5701233

Citations and references

Cited by 1 59 references

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