Virtual Power Plant Scheduling for Battery-Aware Power Balancing
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Solution Overview
Problem
Conventional power systems face challenges in managing power distribution efficiently, particularly with variable power sources like solar and wind, which are influenced by environmental factors, leading to inefficiencies and increased maintenance costs due to peak demand mismatch.
Innovation Solution
A virtual power plant (VPP) system that uses a machine learning-based simulation model to optimize power distribution between a power grid, battery storage systems, electric vehicle charging stations, and power plants, by training on data sets to generate schedules that control power exports and imports, thereby minimizing strain on production facilities and extending equipment lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional power systems manage power distribution without machine learning optimization, then system simplicity is maintained, but power distribution efficiency deteriorates due to inability to handle variable power sources effectively
Solution Approach 1:
A virtual power plant simulation model acts as an intermediary between variable power sources and the power distribution system. The model receives data from power sources, consumers, and storage systems, processes this information through machine learning algorithms, and generates optimized power schedules that coordinate all components effectively.
Solution Approach 2:
The system performs preliminary actions by training the simulation model on historical data sets before actual power distribution decisions are made. This pre-training phase enables the model to learn optimal power management strategies and predict future power conditions, allowing proactive rather than reactive power distribution management.
2Productivity
If machine learning-based VPP simulation model is implemented, then power distribution efficiency is improved, but data processing requirements and computational complexity increase
Solution Approach 1:
The power distribution system is segmented into distinct controllable components: power sources, power consumers, and power storage systems. The simulation model processes data for each segment separately and generates coordinated control schedules, making the overall data processing task more manageable and efficient.
Solution Approach 2:
The system changes parameters by using the simulation model to predict future power conditions and adjust operational parameters dynamically. The model transforms raw data into optimized control parameters for power exports and imports, converting large volumes of raw data into actionable scheduling decisions.
3Reliability
If optimized power schedules are generated to control power exports and imports, then economic performance is improved, but control system complexity increases
Solution Approach 1:
The virtual power plant simulation model serves multiple functions simultaneously: it predicts power conditions, generates optimized schedules, coordinates power sources and consumers, and manages storage systems. This multi-functionality consolidates what would otherwise require multiple separate control systems into a single unified platform.
Solution Approach 2:
The system implements feedback mechanisms where the simulation model continuously receives updated data from the power system, compares actual performance against predicted performance, and adjusts future power schedules accordingly. This closed-loop control ensures economic optimization while maintaining system reliability.
4Duration of action of stationary object
If battery storage systems are managed to minimize deep discharge events, then equipment lifespan is extended, but power management complexity increases
Solution Approach 1:
The simulation model performs preliminary analysis of power conditions and battery state to predict optimal charge-discharge schedules before actual operations occur. By pre-planning battery operations based on forecasted power availability and demand, the system extends battery lifespan without requiring complex real-time decision-making hardware.
Data Source
AI summary
A method of managing a virtual power plant (VPP) and power distribution between a power grid, a battery storage system, an electric vehicle (EV) charging station, and a power plant, includes: obtaining a first data set including information from each of the power grid, the battery storage system, the EV charging station, and the power plant; training a VPP simulation model based on the first data set using a machine learning algorithm; obtaining a second data set including information from each of the power grid, the battery storage system, the EV charging station, and the power plant; determining power condition information based on the second data set and VPP simulation model; generating a power schedule, based on the VPP simulation model and the power condition information; and transmitting a command based on the power schedule.