VPP Controller ML Energy Management
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Solution Overview
Problem
Existing intelligent energy systems face challenges in efficiently managing variable power sources like solar and wind, and ensuring reliable power supply to consumers, due to the lack of direct control over power production and storage technologies.
Innovation Solution
A virtual power plant (VPP) controller that uses machine learning (ML) to manage power flow between power consumers, battery storage systems, and independent power plants, by determining energy management system (EMS) policies based on time-series data and power availability information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If variable independent power sources (solar, wind) are utilized to meet power demand, then power supply reliability is improved, but power production cannot be directly controlled due to weather conditions and time dependencies
Solution Approach 1:
A virtual power plant controller is introduced as an intermediary system that coordinates between independent power sources, battery storage systems, and power consumers. The controller receives data from weather sensors and other sources, processes this information, and generates control commands to optimize power distribution without directly controlling the variable power sources themselves.
Solution Approach 2:
The system implements continuous feedback loops where the controller monitors power production from independent sources, battery charge levels, power demand from consumers, and weather conditions. Based on this feedback, the controller dynamically adjusts battery charging/discharging and power distribution to maintain reliability despite the uncontrollable nature of variable power sources.
2Productivity
If battery storage systems are used to manage power flow, then power distribution optimization is improved, but battery lifetime is reduced due to charge-discharge cycles
Solution Approach 1:
The controller dynamically adjusts battery operation based on real-time conditions. Instead of fixed charging/discharging schedules, the system adaptively determines when to charge or discharge batteries based on power availability, demand patterns, and battery state of charge, optimizing the balance between power distribution and battery preservation.
Solution Approach 2:
The system changes operational parameters such as charge/discharge rates and state of charge thresholds based on current conditions. By adjusting these parameters dynamically, the controller optimizes power distribution while minimizing stress on the battery system and extending its operational lifetime.
3Measurement precision
If machine learning models are trained on historical time-series data to predict power demand, then power management accuracy is improved, but data processing time and computational resources are increased
Solution Approach 1:
Machine learning models are trained in advance on historical time-series data to learn patterns in power demand, weather conditions, and power production. This preliminary training enables the models to make rapid predictions during operation without requiring extensive real-time computation, reducing data processing time while maintaining high accuracy.
Data Source
AI summary
A method of operating a virtual power plant controller, that manages a power consumer connected to a power grid, a battery storage system, and an independent power plant, includes: obtaining a first data set including time-series information for each of power usage of the power consumer, power output of the independent power plant, power output capacity of the power grid, and state of charge of the battery storage system; training a machine learning (ML) model based on the time-series information using a ML algorithm, the ML model determines one or more parameters of an energy management system (EMS) policy to satisfy a power demand of the power consumer over a predetermined horizon; obtaining a second data set including power availability information; determining the one or more parameters of the EMS policy by inputting the second data set into the ML model; transmitting a command based on the EMS policy.