Wind Power Prediction Based on the Pricing Strategy of Electric Vehicle Charging

Authors

Shurong PENG, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China
Shijun HUANG, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China
Bin LI, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China
Junzhe PENG, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China
Fulu XU, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China
Liangyuan SHI, School of Electrical and Information Engineering, Changsha University of Science and Technology，Changsha 410114 , China

Keywords

deep learning, lstm neural network, twoobjective optimization model, wind power prediction, price adjustment strategy

Abstract

In order to improve the power network stability involving the wind power, a twostage price adjustment strategy is proposed for electric vehicles based on the wind power prediction. This strategy promotes the wind power accommodation by predicting wind power and then regulating the price of electric vehicles. In the prediction stage, the LSTM neural network with a memory ability of time series is utilized to predict the wind power. At the pricing stage, an optimization model of price adjustment is established with an objective function of the high similarity between the predicted wind power curve and the charging load curve, and the small charging cost. The price is set based on the forecast wind power and then it is utilized to adjust the load so that the charging load is close to the wind power over time. Finally, a simulation is included to verify the effectiveness of the strategy. The charging load before and after optimization is compared. It is shown that the latter is closer to the prediction of wind power.

DOI

10.19781/j.issn.16739140.2020.03.015

First Page

114

Last Page

119

Recommended Citation

PENG, Shurong; HUANG, Shijun; LI, Bin; PENG, Junzhe; XU, Fulu; and SHI, Liangyuan (2020) "Wind Power Prediction Based on the Pricing Strategy of Electric Vehicle Charging," Journal of Electric Power Science and Technology: Vol. 35: Iss. 3, Article 15.
DOI: 10.19781/j.issn.16739140.2020.03.015
Available at: https://jepst.researchcommons.org/journal/vol35/iss3/15