ML-Optimized VPP Controller for EV Charging Networks
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Solution Overview
Problem
Existing intelligent energy systems face challenges in efficiently managing variable power sources like solar and wind, which are dependent on weather conditions, leading to potential power demand failures in aggregated systems.
Innovation Solution
A method for operating a virtual power plant (VPP) controller that integrates machine learning (ML) algorithms to predict power output from independent power plants, manage electric vehicle (EV) charging stations, and optimize energy storage and grid power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If variable power sources (solar, wind) are used to maximize utilization, then energy efficiency is improved, but power supply reliability deteriorates due to weather dependency
Solution Approach 1:
The system performs preliminary actions by training machine learning models with historical power output data before actual operation. The ML models predict future power generation from variable sources, allowing the VPP controller to proactively plan power distribution and storage strategies, thereby maintaining reliability while maximizing variable power source utilization.
Solution Approach 2:
The system implements feedback mechanisms where the VPP controller continuously monitors actual power output from variable sources, compares it with ML predictions, and adjusts power distribution strategies in real-time. This closed-loop control ensures reliable power supply while maintaining high utilization of variable power sources.
2Productivity
If machine learning models are trained and deployed for power prediction, then power distribution optimization is improved, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between data collection and power distribution control. These ML models act as intelligent mediators that process historical power data and generate predictions, which then guide the VPP controller's decision-making, thereby optimizing power distribution while managing complexity through modular architecture.
3Reliability
If real-time power prediction and control are implemented, then power demand satisfaction is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary training of machine learning models during off-peak periods using historical data. Once trained, the models can quickly predict power output during real-time operation, reducing computational burden during critical power distribution decisions while maintaining high power demand satisfaction.
Data Source
AI summary
A method of operating a virtual power plant (VPP) controller, that manages an electric vehicle (EV) charging station connected to a power grid, a battery storage system, and an independent power plant, includes: obtaining a first data set including time-series information of power output from the independent power plant; training a machine learning (ML) model that predicts power output from the independent power plant over a predetermined prediction horizon; obtaining a second data set including time-series information of: power demand information from the EV charging station; and power availability information from each of the battery storage system, the power grid, and the independent power plant; generating a predicted schedule of power output from the independent power plant over the predetermined prediction horizon; generating a command by inputting the second data set and the predicted schedule into an energy management system (EMS) policy.