Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty

Behzad Esmaeilnezhad, Hossein Amini, Reza Noroozian () and Saeid Jalilzadeh
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Behzad Esmaeilnezhad: Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran
Hossein Amini: The Bradly Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Reza Noroozian: Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran
Saeid Jalilzadeh: Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran

Energies, 2025, vol. 18, issue 2, 1-26

Abstract: The primary objective when operating a distribution network is to minimize operating costs while taking technical constraints into account. Minimizing the operational costs is difficult when there is a high penetration of renewable resources and variability of loads, which introduces uncertainty. In this paper, a flexible, dynamic reconfiguration model is developed that enables a distribution network to minimize operating costs on an hourly basis. The model fitness function is to minimize the system costs, including power loss, voltage deviation, purchased power from the upstream network, renewable generation, and switching costs. The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. To evaluate the performance of the presented approach, it is validated on the IEEE 33-bus radial system and TPC 83-bus real system. The simulation results show the impact of dynamic reconfiguration on reducing operation costs. It is found that dynamic reconfiguration is an efficient solution for reducing power losses and total energy drawn from the upstream network by increasing the number of switching operations.

Keywords: coati optimization algorithm; dynamic reconfiguration; operation cost; renewable generation; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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