Aggregated Representation of Electric Vehicles Population on Charging Points for Demand Response Scheduling

Charging electric vehicles (EVs), whose number is increasing, is a great challenge for the power grid due to the charging load variability. Coordinated charging and schedule optimization with seized demand response opportunities are well-known conceptual solutions to that. Still, the main challenge is to adequately predict availability and parameters of electric vehicles which is crucial for determining the charging schedule and the demand response potential. We propose a method to represent a population of electric vehicles that on the one hand enables prediction via machine learning and on the other it enables an accurate optimization of the charging schedule and demand response ability. The method essence is to use five discrete-time signals spanned over a prediction horizon period which are related to envelopes of feasible charging power and charging states for the EV population on that horizon. We also introduce a robust conversion of any sequence of these signals into individual EVs data. It enables to pose and solve the optimization problem of charging scheduling with included demand response for a predicted population in the introduced representation. The proposed method is validated by schedule optimization using first the original data and then using reconstructed population data. The validation results show that the proposed EV population representation method preserves the valuable information needed for the charging schedule optimization and demand response.

Automated summary

Charging electric vehicles poses a significant challenge to the power grid due to the variability in charging load. Coordinated charging and schedule optimization with demand response opportunities are well-known solutions. However, accurately predicting electric vehicle availability and parameters is crucial for determining the charging schedule and demand response potential. We propose a method using discrete-time signals that represent the EV population and enable prediction and optimization. This method preserves valuable information for charging schedule optimization and demand response.