AI Virtual Power Plant Control for Dynamic Distributed Energy Dispatch
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Solution Overview
Problem
Existing energy storage systems are not safe, not efficient, or impractical, and fail to effectively integrate geographically dispersed, small-scale power generators and storage units into a unified system that can dynamically respond to power supply and demand changes.
Innovation Solution
A virtual power plant system that includes a network of sense and control devices communicatively coupled with a control center, optimizing energy generation, storage, and transmission through real-time monitoring and dynamic dispatching, integrating small-scale power generators and storage units, and utilizing artificial intelligence for predictive control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If small-scale power generators and storage units are integrated into a unified system, then grid stability and flexibility are enhanced, but device complexity increases
Solution Approach 1:
The patent combines multiple small-scale power generators and storage units into a unified virtual power plant system that functions as a single coordinated entity. The control center aggregates control signals and coordinates operation across all components, achieving grid stability equivalent to large centralized plants while maintaining the benefits of distributed small-scale units.
Solution Approach 2:
The control center serves multiple functions simultaneously: it coordinates power generation, manages energy storage, optimizes resource scheduling, and provides real-time monitoring. This multi-functional approach consolidates control capabilities into a single universal system that manages the entire virtual power plant without requiring separate specialized systems for each function.
2Productivity
If real-time monitoring and dynamic dispatching are implemented, then resource scheduling is optimized, but device complexity increases
Solution Approach 1:
The system implements continuous real-time monitoring that feeds operational data back to the control center. This feedback loop enables dynamic dispatching decisions based on current system state, optimizing resource scheduling while maintaining automated control that prevents the complexity from becoming unmanageable.
Solution Approach 2:
The control system operates autonomously using automated algorithms for resource scheduling and dynamic dispatching. The system self-regulates by processing monitoring data and executing control decisions without requiring constant human intervention, which optimizes productivity while containing operational complexity through automation.
3Adaptability or versatility
If artificial intelligence is used for predictive control, then system response to power supply and demand changes is improved, but device complexity increases
Solution Approach 1:
The artificial intelligence system performs predictive control by analyzing historical and real-time data to forecast future power supply and demand conditions. This preliminary action allows the system to pre-position resources and prepare control strategies in advance, improving adaptability and response time while the automated predictive algorithms manage the computational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides real-time monitoring, dynamic dispatching, and self-regulation, enhancing grid stability and flexibility by aggregating resources, optimizing resource scheduling, and dynamically responding to power supply and demand changes.
Implementation Method 1
pumping a corresponding amount of fluid to increase a gas pressure inside the energy storage apparatus
Implementation Method 2
pushing out a portion or all of the corresponding amount of fluid to generate an amount of released electricity by driving a hydrogenerator
Data Source
AI summary
A virtual power plant system including a network of sense and control devices communicatively coupled with a virtual power plant control center that receives data about distributed power generating facilities, power storage apparatuses, power transmission nodes and/or power consumption entities from the devices and optimizes the process of energy generation, storage, re-generation, transmission and/or consumption throughout the network based on the received data.