TY  - JOUR
T1  - ELEKTRİKLİ ARAÇ ŞARJ İSTASYONLARININ ENERJİ DAĞITIM HATLARINA OPTİMUM ŞEKİLDE KONUMLANDIRILMASI
TT  - OPTIMAL ALLOCATION OF ELECTRIC VEHICLE CHARGING STATIONS ON ENERGY DISTRIBUTION NETWORKS
AU  - Çıkan, Murat
AU  - Nacar Çıkan, Nisa
PY  - 2024
DA  - June
DO  - 10.17780/ksujes.1365209
JF  - Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi
JO  - KSU J. Eng. Sci.
PB  - Kahramanmaras Sutcu Imam University
WT  - DergiPark
SN  - 1309-1751
SP  - 340
EP  - 363
VL  - 27
IS  - 2
LA  - tr
AB  - 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.
KW  - Elektrikli araç şarj istasyonu
KW  - konumlandırma
KW  - optimizasyon
KW  - meta-sezgisel algoritma
N2  - 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.
CR  - 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
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CR  - 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
CR  - 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
CR  - 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
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CR  - 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
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CR  - 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
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UR  - https://doi.org/10.17780/ksujes.1365209
L1  - https://dergipark.org.tr/en/download/article-file/3428808
ER  -