ELEKTRİKLİ ARAÇ ŞARJ İSTASYONLARININ ENERJİ DAĞITIM HATLARINA OPTİMUM ŞEKİLDE KONUMLANDIRILMASI

Murat Çıkan; Nisa Nacar Çıkan

doi:10.17780/ksujes.1365209

TR EN

ELEKTRİKLİ ARAÇ ŞARJ İSTASYONLARININ ENERJİ DAĞITIM HATLARINA OPTİMUM ŞEKİLDE KONUMLANDIRILMASI

Öz

Elektrikli araçların (EA) güç sistemlerine büyük ölçekli entegrasyonu elektrik şebekesinin güç kalitesinin düşmesine, hatlardaki aktif güç kayıplarının artmasına ve dağıtım hattının güvenirlilik indeks değerlerinin azalmasına neden olur. Bu tarz problemler elektrikli araç şarj istasyonlarının (EAŞİ) şebekeye optimal şekilde konumlandırılması ile minimize edilebilir. Bu çalışmada, sürüş eğitimi-temelli optimizasyon (DTBO) algoritması kullanılarak 200 baralı test sisteminde optimal yük akışı ve EAŞİ'lerin optimum noktalara konumlandırılması eş zamanlı olarak gerçekleştirilmiştir. Çalışmada, üç farklı senaryo ve her bir senaryoya ait üç farklı durum göz önüne alınmıştır. Birinci senaryoda EAŞİ’lerin hatta bulunmadığı durumda optimal güç akışının gerçekleştirilmesi, ikinci senaryoda EAŞİ’lerin rastgele baralara konumlandırılarak, üreteçlerin aktif/reaktif güç çıkışlarının optimize edilmesi ve üçüncü senaryoda ise EAŞİ’lerin DTBO algoritması ile optimum noktalara yerleştirilerek ve üreteçlerin çıkış güçlerinin birlikte optimize edilmesi olarak özetlenebilir. Her senaryoda üç farklı durum (serbest çalışma, deterministik ve meta-sezgisel metot yaklaşımları) göz önüne alınarak sistemdeki aktif güç kayıpları minimize edilmiştir. Elde edilen sonuçlar, DTBO algoritmasının kullanılmasıyla EAŞİ’lerin optimal noktalara konumlandırılması sonucunda aktif güç kayıplarının %32 oranında azaldığı göstermektedir.

Anahtar Kelimeler

OPTIMAL ALLOCATION OF ELECTRIC VEHICLE CHARGING STATIONS ON ENERGY DISTRIBUTION NETWORKS

Abstract

The large-scale integration of Electric Vehicles (EVs) into the power systems causes a decrease in the power quality of the electrical grid, an increase in active power losses in the lines, and a reduction in the reliability index values of the distribution network. Such problems can be minimized by the optimal positioning of electric vehicle charging stations (EVCSs) on the grid. This study uses a driving training-based optimization (DTBO) algorithm to simultaneously determine the optimal load flow and the optimal positioning of the EASIs in a 200-bus test system. In the study, three different scenarios and three different cases for each scenario have been considered. The study can be summarized as follows: In the first scenario, the power flow is optimized without the presence of EVCSs on the grid. In the second scenario, EVCSs are randomly placed on buses and generators' active/reactive power outputs are optimized. In the third scenario, simultaneously EVCSs at optimal locations are allocated using the DTBO algorithm and the generator output powers are optimized. The active power losses in the system have been minimized in each scenario by considering three different cases (unrestricted operation, deterministic, and meta-heuristic method approaches). The obtained results demonstrate that using the DTBO algorithm for the optimal placement of EVCSs has resulted in a 32% reduction in active power losses.

Keywords

References

Adnan, N., Md Nordin, S., bin Bahruddin, M. A., & Ali, M. (2018). How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle. Transportation Research Part A: Policy and Practice, 118, 819–836. https://doi.org/https://doi.org/10.1016/j.tra.2018.10.019
Ahmad, F., Iqbal, A., Ashraf, I., Marzband, M., & khan, I. (2022). Optimal location of electric vehicle charging station and its impact on distribution network: A review. Energy Reports, 8, 2314–2333. https://doi.org/https://doi.org/10.1016/j.egyr.2022.01.180
Birchfield, A. B., Xu, T., Gegner, K. M., Shetye, K. S., & Overbye, T. J. (2017). Grid Structural Characteristics as Validation Criteria for Synthetic Networks. IEEE Transactions on Power Systems, 32(4), 3258–3265. https://doi.org/10.1109/TPWRS.2016.2616385
Cikan, M., & Cikan, N. N. (2023). Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system. International Journal of Electrical Power and Energy Systems, 144. https://doi.org/10.1016/j.ijepes.2022.108564
Cikan, M., & Kekezoglu, B. (2022). Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration. Alexandria Engineering Journal, 61(2), 991–1031. https://doi.org/https://doi.org/10.1016/j.aej.2021.06.079
Dehghani, M., Trojovská, E., & Trojovský, P. (2022). A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Scientific Reports, 12(1), 9924. https://doi.org/10.1038/s41598-022-14225-7
Ge, S., Feng, L., & Liu, H. (2011). The planning of electric vehicle charging station based on Grid partition method. 2011 International Conference on Electrical and Control Engineering, 2726–2730. https://doi.org/10.1109/ICECENG.2011.6057636
Glover, J. D., Sarma, M. S., & Overbye, T. J. (2016). Power_system_analysis_and_design_5th. Cengage Learning.

Islam, M., Shareef, H., & Mohamed, A. (2015). A Review of Techniques for Optimal Placement and Sizing of Electric Vehicle Charging Stations. Przegląd Elektrotechniczny, 91, 122–126. https://api.semanticscholar.org/CorpusID:113450868
Doğanşahin, K., & Cikan, M. (2023). A new line stability index for voltage stability analysis based on line loading. Journal, 1(1), 23–30.
Kathiravan, K., & Rajnarayanan, P. N. (2023). Application of AOA algorithm for optimal placement of electric vehicle charging station to minimize line losses. Electric Power Systems Research, 214, 108868. https://doi.org/https://doi.org/10.1016/j.epsr.2022.108868
Mozafar, M. R., Moradi, M. H., & Amini, M. H. (2017). A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm. Sustainable Cities and Society, 32, 627–637. https://doi.org/https://doi.org/10.1016/j.scs.2017.05.007
Nacar Cikan, N., & Cikan, M. (2024). Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss. International Journal of Electrical Power & Energy Systems, 157, 109796. https://doi.org/https://doi.org/10.1016/j.ijepes.2024.109796
Nurmuhammed, M., & Karadağ, T. (2021). Elektrikli Araç Şarj İstasyonlarının Konumlandırılması ve Enerji Şebekesi Üzerine Etkisi Konulu Derleme Çalışması. In Gazi University Journal of Science Part A: Engineering and Innovation (Vol. 8, Issue 2, pp. 218–233). Gazi University.
Pal, A., Bhattacharya, A., & Chakraborty, A. K. (2021). Allocation of electric vehicle charging station considering uncertainties. Sustainable Energy, Grids and Networks, 25, 100422. https://doi.org/https://doi.org/10.1016/j.segan.2020.100422
Parker, N., Breetz, H. L., Salon, D., Conway, M. W., Williams, J., & Patterson, M. (2021). Who saves money buying electric vehicles? Heterogeneity in total cost of ownership. Transportation Research Part D: Transport and Environment, 96, 102893. https://doi.org/https://doi.org/10.1016/j.trd.2021.102893
Reddy, M. S. K., & Selvajyothi, K. (2020). Optimal placement of electric vehicle charging station for unbalanced radial distribution systems. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 0(0), 1–15. https://doi.org/10.1080/15567036.2020.1731017
Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Pub.
Tuan, L. A. (2017). Impacts of fast charging of electric buses on electrical distribution systems. CIRED - Open Access Proceedings Journal, 2017(1), 2350-2353(3). https://digital-library.theiet.org/content/journals/10.1049/oap-cired.2017.0802
Yuvaraj, T., Devabalaji, K. R., Kumar, J. A., Thanikanti, S. B., & Nwulu, N. I. (2024). A Comprehensive Review and Analysis of the Allocation of Electric Vehicle Charging Stations in Distribution Networks. IEEE Access, 12, 5404–5461. https://doi.org/10.1109/ACCESS.2023.3349274
Zhou, M., Long, P., Kong, N., Zhao, L., Jia, F., & Campy, K. S. (2021). Characterizing the motivational mechanism behind taxi driver’s adoption of electric vehicles for living: Insights from China. Transportation Research Part A: Policy and Practice, 144, 134–152. https://doi.org/https://doi.org/10.1016/j.tra.2021.01.001
Zimmerman, R. D., Murillo-Sánchez, C. E., & Thomas, R. J. (2011). MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 26(1), 12–19. https://doi.org/10.1109/TPWRS.2010.2051168

Details

Primary Language

Turkish

Subjects

Power Plants , Electrical Engineering (Other)

Journal Section

Research Article

Authors

Murat Çıkan ^*
0000-0001-6723-5769
Türkiye

Nisa Nacar Çıkan
0000-0002-9641-4616
Türkiye

Publication Date

June 3, 2024

Submission Date

September 23, 2023

Acceptance Date

May 13, 2024

Published in Issue

Year 2024 Volume: 27 Number: 2

DOI

https://doi.org/10.17780/ksujes.1365209

IZ

https://izlik.org/JA49UH86AG

Cite

RIS / Bibtex

APA

Çıkan, M., & Nacar Çıkan, N. (2024). ELEKTRİKLİ ARAÇ ŞARJ İSTASYONLARININ ENERJİ DAĞITIM HATLARINA OPTİMUM ŞEKİLDE KONUMLANDIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 340-363. https://doi.org/10.17780/ksujes.1365209

Cited By

Distribution Network Reconfiguration for Voltage Profile Enhancement and Power Loss Reduction Under Hourly Energy Consumption Using Quantum Approximate Optimization Algorithm

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

https://doi.org/10.21605/cukurovaumfd.1665857

IEEE 13-Baralı Dengesiz Üç Fazlı Güç Dağıtım Sistemlerinde Yenilenebilir Enerji Kaynaklarının Optimum Konumlandırılması ve Boyutlandırılması

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

https://doi.org/10.21605/cukurovaumfd.1665868

Cost optimization in microgrids: A scenario-based analysis by using polar the fox optimization algorithm

Journal of Scientific Reports-A

https://doi.org/10.59313/jsr-a.1606950

Multi-Objective Approaches for Optimizing 37-Bus Power Distribution Systems with Reconfiguration Technique: From Unbalance Current & Voltage Factor to Reliability Indices

Computer Modeling in Engineering & Sciences

https://doi.org/10.32604/cmes.2025.061699