Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons - one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.
EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging
2023-06-21
2262490 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Electric vehicle charging optimization method based on day-ahead load prediction value
Europäisches Patentamt | 2022
|IuD Bahn | 2012
|Intelligent line load charging method based on power grid load level
Europäisches Patentamt | 2023
|Charging pile charging load balance scheduling method
Europäisches Patentamt | 2024
|Charging management system based on charging station load and charging control method thereof
Europäisches Patentamt | 2022
|