Robust Reconfiguration of Distribution Networks to Improve Flexibility in the Presence of Renewable Energy Sources

Subject Areas : Electrical and Computer Engineering

Mahsa Choobdari ¹ , Mahmoud Samiei Moghadam ² , Reza Davarzni ³ , Azita Azarfar ⁴ , Hesamodin Hoseinpour ⁵

1 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
2 - Department of Electrical Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran
3 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
4 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
5 - Department of Electrical Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

Received: 2024-10-11 Accepted : 2025-02-19 Published : 2026-06-21

Keywords: Distribution network, Reconfiguration, Renewable resources, Robust optimization,

Abstract :

Reconfiguring smart distribution networks is an economical strategy for reducing losses and voltage deviations, particularly in the face of emerging devices such as energy storage systems, demand-side management, and distributed generation sources. Recent studies have expanded optimization objectives to not only reduce distribution system losses but also minimize electricity procurement from the transmission network at distribution substations. This paper introduces a resilient reconfiguration model that uses a second-order cone programming optimization approach. It covers renewable energy sources, demand-side management, and fossil fuel-based distributed generation sources such as gas and diesel generators. The goal of this optimization model is to minimize a multi-objective function that reduces losses, electricity purchase at distribution substations, and costs associated with limiting renewable energy sources. The performance of the proposed model is validated through a simulation of the 33-bus IEEE network, showing that power losses have decreased by 22.5% (from 0.71 MW to 0.55 MW) compared to the case without demand-side management. The energy purchased from the grid has decreased by 19.5% (from 1.54 to 1.24 MWh). The minimum voltage has improved by 1.03% (from 0.972 p.u to 0.982 p.u). In the robust optimization scenario, there is a 10% reduction in the number of open lines, indicating improved performance under uncertainty conditions. These results highlight the significant impact of the proposed model in optimizing the performance of smart distribution networks and reducing costs and losses.

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