Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure

Abstract

This paper proposes a novel combined event-based simulation model for assessing the explicit demand response potential of electric vehicle (EV) charging networks. The model utilizes different multivariate copulas in generation of realistic artificial charging events that effectively retain the complex dependency structures and parameter distributions of real data important for accurate demand response simulation. A deterministic model is used to estimate the maximal explicit demand response potential of individual charging events based on technical requirements of the frequency containment reserve for disturbance situations (FCR-D) market. The proposed model achieved a mean absolute percentage error (MAPE) of 3.27% when considering averaged daily dispatchable FCR-D potentials, and a MAPE of 4.65% in prediction of dispatchable FCR-D potential with one workweek of data. The results and methodology have been verified and validated with real life data and through comparison with a previous non-copula application for EV FCR profile estimation which it outperformed. The combined event-based simulation model can boost active participation of EVs in power network balancing and is suitable for use in various practical and theoretical applications.