Echo State Network Energy Storage Controller for Grid Stability
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Solution Overview
Problem
The imbalance between energy demand and supply in power grids leads to inefficiencies, including energy waste and shortages, which can result in grid failures and power outages, due to the need for thermal power plants to operate at peak capacity constantly, and the variability of renewable energy sources like solar and wind.
Innovation Solution
An energy storage controller system that uses a battery, a charging circuit, and echo state networks to optimize energy storage and release, adjusting the charging rate based on control and model errors to balance demand and supply, and leverage FPGA acceleration for real-time computation to manage battery state of charge and remaining useful life effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal power plants operate at peak capacity constantly to meet peak demand, then power supply reliability is improved, but energy waste increases during off-peak hours
Solution Approach 1:
The energy storage system performs preliminary charging during off-peak hours when electricity is cheap and abundant, storing energy in advance for later use during peak demand periods. This allows the thermal power plant to operate at peak capacity without wasting energy, as the stored energy supplements supply during off-peak times.
Solution Approach 2:
The battery energy storage system acts as an intermediary between the thermal power plant and the grid, absorbing excess energy during off-peak hours and releasing it during peak hours. This mediator smooths the imbalance between supply and demand, allowing the power plant to maintain peak operation without causing energy waste.
2Reliability
If power plant output capacity is increased to meet peak demand, then power supply reliability is improved, but manufacturing cost increases
Solution Approach 1:
Instead of building additional peak-demand power plant capacity, the system preliminarily stores energy during off-peak hours in battery systems. This avoids the need for expensive infrastructure expansion while ensuring reliable power supply during peak demand periods.
Solution Approach 2:
The system changes the temporal distribution parameter of energy supply by storing energy when demand is low and releasing it when demand is high. This allows existing power plant capacity to serve peak demand without requiring capacity expansion, thereby reducing manufacturing costs.
3Productivity
If conventional control systems are used for energy storage management, then system complexity is reduced, but productivity and responsiveness decrease
Solution Approach 1:
The control system continuously monitors grid conditions, battery state of charge, and pricing signals, using this feedback to dynamically adjust charging and discharging rates. This feedback mechanism enables real-time optimization of energy storage operations, improving productivity while managing complexity through automated control algorithms.
Solution Approach 2:
The control system transitions from static to dynamic operation by continuously adapting charging/discharging rates based on real-time conditions. This dynamic control optimizes energy storage productivity by responding to changing grid demands and pricing, while the modular architecture manages system complexity.
Data Source
AI summary
A system, method, and non-transitory computer readable medium provide for an energy storage controller. The system includes battery, a charging circuit, and an energy storage controller (ESC) coupled to the battery and the charging circuit. The battery stores and expends energy with an electrical grid. The charging circuit regulates a rate of energy stored in the battery. The ESC receives controller inputs; determines a control error of the battery based the controller inputs used in a charging controller echo state network (ESN); determines a model error of the battery based on the controller input used in a battery model ESN; and adjusts the regulated rate of energy that the charging circuit stores in the battery based on the control error and the model error.