Leveraging Quantum Swarm Optimization for Voltage Stability Enhancement in Distribution Networks Incorporating Distributed Energy Resources

Dimas Fajar Uman Putra; Aji Akbar Firdaus; Rony Seto Wibowo; Ni Ketut Ariyani; Vicky Andria Kusuma

doi:10.48084/etasr.15577

Authors

Dimas Fajar Uman Putra Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Aji Akbar Firdaus Universitas Airlangga, Surabaya, Indonesia
Rony Seto Wibowo Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Ni Ketut Ariyani Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Vicky Andria Kusuma Institut Teknologi Kalimantan, Balikpapan, Indonesia

Volume: 16 | Issue: 2 | Pages: 33058-33064 | April 2026 | https://doi.org/10.48084/etasr.15577

Received: 16 October 2025 | Revised: 12 December 2025 and 13 January 2026 | Accepted: 21 January 2026 | Online: 4 April 2026

Corresponding author: Aji Akbar Firdaus

Abstract

The increasing integration of Distributed Energy Resources (DER) and renewable energy units into modern distribution systems introduces significant operational challenges, particularly related to voltage stability, network losses, and dynamic power-flow management. This study presents a Dynamic Distribution Network Reconfiguration (DDNR) strategy optimized using the Quantum Binary Particle Swarm Optimization (QBPSO) algorithm to enhance voltage stability and minimize operational costs in time-varying network conditions. The proposed multi-objective optimization simultaneously minimizes total operating cost and the aggregated voltage stability deviation ) by adaptively determining the optimal configuration of sectionalizing and tie switches across four dynamic load intervals. A modified IEEE 33-bus radial distribution network incorporating Photovoltaic (PV), Wind Turbine (WT), Diesel Generator (DG), and Battery Energy Storage System (BESS) units was employed to evaluate the method's effectiveness under 24-hour varying load profiles and simulated line disturbances. Simulation results in MATLAB R2022a using MATPOWER demonstrate that the QBPSO-based DDNR significantly outperforms both the initial configuration and the conventional Binary Particle Swarm Optimization (BPSO) approach. The total operating cost decreased from 3,346.47 to 2,314.11 USD, whereas the total improved from 50.638 to 39.2509. Moreover, the active and reactive load shedding was nearly eliminated, reduced from 429.405 kW and 303.3 kVAR to 0.000896 kW and 0.00521 kVAR, respectively. These results confirm that the proposed QBPSO method provides superior convergence, improved voltage-profile uniformity, and enhanced economic efficiency, thereby offering a robust and scalable solution for dynamic smart-grid operation and voltage-stability enhancement in future distribution networks.

Keywords:

Dynamic Distribution Network Reconfiguration (DDNR), uantum Binary Particle Swarm Optimization (QBPSO), voltage stability, power loss reduction, smart grid

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