Demand response based zonal load forecasting for electric vehicle charging stations

Huimin Wang

doi:10.1117/12.2635068

25 April 2022 Demand response based zonal load forecasting for electric vehicle charging stations

Huimin Wang

Proceedings Volume 12244, 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022); 122442S (2022) https://doi.org/10.1117/12.2635068
Event: 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022), 2022, Guilin, China

Abstract

With the access to broad-based demand response (DR) resources such as large-scale electric vehicles (EVs), customers are responding to demand under the influence of various incentives in the electricity market, which will change load characteristics and affect load forecasting. This paper proposes a zonal load forecasting for EVs based on DR, using wavelet decomposition(WD) to form seasonal base load and DR dominant part load for similar days, dividing the area where EV charging stations are located into residential, work and public zones, using support vector machine regression methods to forecast for residential and work zones, and using time series models to forecast for seasonal For the public area, a time series model is used to forecast the seasonal base load, and a grey forecasting model is used to forecast the dominant part of the DR, and Markov correction is applied to the forecast results. By comparing with the traditional method, the proposed method proves its effectiveness and accuracy in forecasting the load of EV charging stations with demand response.

Citation Download Citation

Huimin Wang "Demand response based zonal load forecasting for electric vehicle charging stations", Proc. SPIE 12244, 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022), 122442S (25 April 2022); https://doi.org/10.1117/12.2635068

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