Learning Assisted Demand Charge Mitigation for Workplace Electric Vehicle Charging

Abstract

Uncontrolled electric vehicle (EV) charging loads at a workplace could result in hefty demand charges. Several studies in the literature have focused on mitigating these demand charges at workplaces using aggregator-based centralized control and consensus-based distributed control. Though each model has successfully mitigated the demand charges they all show some limitations. Aggregator-based centralized solver is computation and time intensive while consensus-based distributed control is communication intensive. Other works with algorithms exploring demand charge mitigation also often rely on large amounts communications. These algorithms include water filling algorithms, multiobjective optimization, and a fuzzy logic controller. The machine learning-based (ML) approach investigated in this study seeks to eliminate both drawbacks using the model trained on optimization results obtained with existing methods. The investigation of ML-based controller in this study is not only found to be superior in performance due to speed and accuracy metrics compared to existing control approaches but has also provided the promising resilient approach to deal with communication failure and cybersecurity threats.