Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network
The sudden surge in electric vehicle (EV) adoption has significantly increased electricity demand, posing new challenges for radial distribution networks (RDNs). The large-scale deployment of electric vehicle charging stations (EVCS) introduces operational issues such as elevated power losses, volta...
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2025-09-01
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| author | T. Yuvaraj M. Thirumalai T.D. Suresh Sudhakar Babu Thanikanti Mohammad Khishe |
| author_facet | T. Yuvaraj M. Thirumalai T.D. Suresh Sudhakar Babu Thanikanti Mohammad Khishe |
| author_sort | T. Yuvaraj |
| collection | DOAJ |
| description | The sudden surge in electric vehicle (EV) adoption has significantly increased electricity demand, posing new challenges for radial distribution networks (RDNs). The large-scale deployment of electric vehicle charging stations (EVCS) introduces operational issues such as elevated power losses, voltage instability, and line overloading, thereby testing the grid's robustness and efficiency. To address these challenges, this research proposes a novel optimization framework that integrates the quasi-refined slime mould algorithm (QRSMA) with conventional slime mould algorithm (SMA), particle swarm optimization (PSO), and genetic algorithm (GA) techniques for the optimal placement of solar-based distributed generators (SDGs) and shunt capacitors (SCs). The proposed method aims to minimize a multi-objective function incorporating real and reactive power losses, voltage deviation, and voltage stability, thereby enhancing the stability and reliability of RDN operations. The framework employs stochastic modeling based on four years of hourly meteorological data to account for uncertainties in solar irradiance and temperature. Additionally, the EVCS model includes dynamic operational aspects such as mean queue lengths, waiting times, and demand response (DR) load control. The methodology is validated using both a practical Indian 28-bus RDN and the large-scale IEEE 118-bus system to assess scalability and generalizability. Simulation results confirm the superior performance of QRSMA in improving voltage profiles, reducing power losses, and achieving better computational efficiency compared to conventional optimization algorithms. Sensitivity analyses further demonstrate the robustness of QRSMA under varying objective priorities. Moreover, the economic assessment using the levelized cost of energy (LCOE) indicates strong financial viability for real-world implementation. This work underscores the importance of coordinated planning of SDGs and SCs to mitigate EVCS-induced challenges and provides a scalable, efficient solution for modern, EV-integrated smart grids. |
| format | Article |
| id | doaj-art-fd92ebe632da42f1ab50cc1536dd2270 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-fd92ebe632da42f1ab50cc1536dd22702025-08-25T04:14:42ZengElsevierResults in Engineering2590-12302025-09-012710680410.1016/j.rineng.2025.106804Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution networkT. Yuvaraj0M. Thirumalai1T.D. Suresh2Sudhakar Babu Thanikanti3Mohammad Khishe4Centre for Smart Energy Systems, Chennai Institute of Technology, Chennai 600069, IndiaDepartment of Electronics and Communication Engineering, Saveetha Engineering College, Chennai 602105, IndiaDepartment of Mechatronics, T.S. Srinivasan Centre for Polytechnic College and Advanced Training (CPAT-TVS), Chennai 600 095, IndiaDepartment of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India; Centre for Cyber Physical food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South AfricaDepartment of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran; Applied Science Research Center, Applied Science Private University, Amman 11937, Jordan; Corresponding author.The sudden surge in electric vehicle (EV) adoption has significantly increased electricity demand, posing new challenges for radial distribution networks (RDNs). The large-scale deployment of electric vehicle charging stations (EVCS) introduces operational issues such as elevated power losses, voltage instability, and line overloading, thereby testing the grid's robustness and efficiency. To address these challenges, this research proposes a novel optimization framework that integrates the quasi-refined slime mould algorithm (QRSMA) with conventional slime mould algorithm (SMA), particle swarm optimization (PSO), and genetic algorithm (GA) techniques for the optimal placement of solar-based distributed generators (SDGs) and shunt capacitors (SCs). The proposed method aims to minimize a multi-objective function incorporating real and reactive power losses, voltage deviation, and voltage stability, thereby enhancing the stability and reliability of RDN operations. The framework employs stochastic modeling based on four years of hourly meteorological data to account for uncertainties in solar irradiance and temperature. Additionally, the EVCS model includes dynamic operational aspects such as mean queue lengths, waiting times, and demand response (DR) load control. The methodology is validated using both a practical Indian 28-bus RDN and the large-scale IEEE 118-bus system to assess scalability and generalizability. Simulation results confirm the superior performance of QRSMA in improving voltage profiles, reducing power losses, and achieving better computational efficiency compared to conventional optimization algorithms. Sensitivity analyses further demonstrate the robustness of QRSMA under varying objective priorities. Moreover, the economic assessment using the levelized cost of energy (LCOE) indicates strong financial viability for real-world implementation. This work underscores the importance of coordinated planning of SDGs and SCs to mitigate EVCS-induced challenges and provides a scalable, efficient solution for modern, EV-integrated smart grids.http://www.sciencedirect.com/science/article/pii/S2590123025028683Electrical vehicleQuasi-reflection-based slime mould algorithmSolar distributed generationShunt capacitorLevelized cost of energyMulti-objective function |
| spellingShingle | T. Yuvaraj M. Thirumalai T.D. Suresh Sudhakar Babu Thanikanti Mohammad Khishe Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network Results in Engineering Electrical vehicle Quasi-reflection-based slime mould algorithm Solar distributed generation Shunt capacitor Levelized cost of energy Multi-objective function |
| title | Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network |
| title_full | Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network |
| title_fullStr | Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network |
| title_full_unstemmed | Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network |
| title_short | Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network |
| title_sort | dynamic optimization of solar dg and shunt capacitor placement to mitigate the impact of ev charging stations on power distribution network |
| topic | Electrical vehicle Quasi-reflection-based slime mould algorithm Solar distributed generation Shunt capacitor Levelized cost of energy Multi-objective function |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025028683 |
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