Optimal Sizing and Placement of Electric Vehicle Charging Stations Based on MOMA Algorithm

The continuous growth in the number of domestic EVs has led to a large number of electric vehicles (EVCSs) being connected to the distribution network to meet the increasing charging demands of electric vehicles (EVs). This situation has brought unprecedented challenges to the stability, security, a...

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Vydáno v:2025 7th Asia Energy and Electrical Engineering Symposium (AEEES) s. 957 - 962
Hlavní autoři: Cheng, Min, Pan, Zhen, Huang, Feipeng, Li, Kui, Wei, Lishan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.03.2025
Témata:
charging demand forecasting
Costs
Dispatching
Distribution networks
electric vehicle
energy storage system
Fluctuations
MOMA algorithm
Planning
Power grids
Power system stability
site selection and capacity determination
Stability analysis
State of charge
Voltage fluctuations
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Shrnutí:The continuous growth in the number of domestic EVs has led to a large number of electric vehicles (EVCSs) being connected to the distribution network to meet the increasing charging demands of electric vehicles (EVs). This situation has brought unprecedented challenges to the stability, security, and economy of the distribution network. In order to reduce the impact of EVCSs on the distribution network while protecting the interests of investors and EV users, this paper constructs a multi-objective planning model for the combination of EVCSs and battery energy storage systems (BESSs) considering the behavior characteristics of EV users. This model aims to minimize the comprehensive costs of EVCSs and BESSs, the waiting time of users, and the system voltage fluctuation, and achieve the optimal trade-off between economy and stability through the planning of EVCSs and BESSs. In addition, the multi-objective mayfly algorithm (MOMA) algorithm is used for verification in the extended IEEE-33-node test system and Chenggong District University Town in Kunming. Simulation results show that in the IEEE-33-node test system, compared with the situation using sequential preference optimization (SPO), the cost is reduced by 15.49%, the system network loss is reduced by 1.97 \text{MW} \cdot \mathrm{h} (40.6 % ), and the voltage fluctuation is reduced by 0.29 p.u. (41.1%). The stability and economy of the distribution network are effectively improved.
DOI:10.1109/AEEES64634.2025.11019024