Open Access Paper
11 September 2023 Charging station quantity planning model based on neural network and electric load forecasting model
Kai Kang, Qianlong Feng, Yue Zhou, Fan Zhang
Author Affiliations +
Proceedings Volume 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023); 127790J (2023) https://doi.org/10.1117/12.2689557
Event: Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 2023, Kunming, China
Abstract
A large quantity of Electric Vehicle (EV) charging station loads connected to the power grid will aggravate the peak-valley difference and reduce the stability of the power system and the economic benefits of operation. The connection of EV charging station loads will also inject a large quantity of harmonics into the power grid, further aggravating the risk of power supply equipment failure. EV can also be regarded as distributed energy storage devices. Under special circumstances, EV can feed back electric energy to the power grid through power conversion devices and participate in frequency modulation of the power grid. In order to reduce the idle rate of charging stations and promote the distributed photovoltaic absorption capacity of the system, this paper proposes an electric load forecasting model based on Particle Swarm Optimization Neural Network (PSO-NN). The charging and discharging power of EV cluster and energy storage equipment are solved in turn by PSO algorithm, and when the regulation capacity of EV cluster is insufficient or limited, it is supplemented by energy storage equipment. The simulation results show that with the increase of the quantity of experiments, the accuracy of this algorithm is stable at about 95%, and the real-time wavelength tends to be stable. Therefore, the scheduling strategy can effectively improve the safety and economic performance of power grid and the capacity of transportation system, which is conducive to improving the operational performance of power grid and transportation network and providing algorithm and technical support for the construction of charging station number planning model.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kai Kang, Qianlong Feng, Yue Zhou, and Fan Zhang "Charging station quantity planning model based on neural network and electric load forecasting model", Proc. SPIE 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 127790J (11 September 2023); https://doi.org/10.1117/12.2689557
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling

Power grids

Solar energy

Education and training

Neurons

Transportation

Industry

Back to Top