Understanding controlled EV charging impacts using scenario-based forecasting models: Poster

Rahul Roy, Trivikram Dokka, David Ellis, Esther Dudek, Paul Barnfather

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

1 Citation (SciVal)


Smart or controlled charging is widely seen as a potential solution to alleviate stress caused by mass uptake of electric vehicles on existing networks. While analyses with completely uncontrolled charging result in unrealistically amplified estimates of EV load, assuming a completely coordinated controlled charging also oversimplifies the estimates of impact due to mass EV uptake. Learning from the recent EV trials in the UK and elsewhere we take into account two key aspects which are largely ignored in current research: EVs actually charging at any given time and wide range of EV types, especially battery capacity-wise. Taking a minimalistic scenario-based approach, we study forecasting models for mean number of active chargers and mean EV consumption for distinct scenarios. Focusing on residential charging the models we consider range from simple regression models to more advanced machine and deep learning models such as XGBoost and LSTMs. We then use these models to evaluate the impacts of different levels of future EV penetration on a specimen distribution transformer that captures typical real-world scenarios. In doing so, we also initiate the study of different types of controlled charging when fully controlled charging is not possible. This aligns with the outcomes from recent trials which show that a sizeable proportion of EV owners may not prefer fully controlled centralized charging. We study two possible control regimes and show that one is more beneficial from load-on-transformer point of view, while the other may be preferred for other objectives. We show that a minimum of 60% control is required to ensure that transformers are not overloaded during peak hours.
Original languageEnglish
Title of host publicatione-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems
Place of PublicationU. S. A.
PublisherAssociation for Computing Machinery
Number of pages2
VolumeJune 2021
ISBN (Electronic)9781450383332
ISBN (Print)9781450383332
Publication statusPublished - 22 Jun 2021

Publication series

Namee-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems


  • Controlled Charging
  • Electric Vehicle (EV)
  • Forecasting
  • LSTMs
  • Time Series
  • XGB

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Fuel Technology


Dive into the research topics of 'Understanding controlled EV charging impacts using scenario-based forecasting models: Poster'. Together they form a unique fingerprint.

Cite this