Battery Frequency Response Control for SOC and Degradation Balance
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Solution Overview
Problem
Determining optimal adjustments for battery storage controllers to participate in frequency response programs while managing energy and demand charges is challenging, as existing systems struggle to balance revenue generation and battery degradation costs.
Innovation Solution
A frequency response optimization system that includes a battery, a power inverter, and a frequency response controller, which receives regulation signals, determines optimal power setpoints, and estimates battery degradation to maximize revenue while minimizing costs through a high-level and low-level controller framework, using statistics and battery life models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the battery storage controller actively participates in frequency response programs by adjusting power setpoints, then frequency response revenue is generated, but battery degradation increases and energy/demand charges increase
Solution Approach 1:
The system dynamically adjusts the midpoint parameter around which frequency response regulation occurs. By optimizing this midpoint parameter, the controller balances the trade-off between generating frequency response revenue and minimizing battery degradation, allowing the battery to operate in a state that maximizes economic return while preserving battery life
Solution Approach 2:
The system implements dynamic control by continuously adjusting power setpoints based on real-time conditions including regulation signals, state of charge, and predicted energy/demand charges. This dynamic adaptation allows the controller to respond optimally to changing grid conditions while managing battery stress and degradation over time
2Productivity
If the battery storage controller adjusts power setpoints to maximize frequency response revenue, then revenue increases, but energy and demand charges incurred increase
Solution Approach 1:
The system performs preliminary optimization by determining the optimal midpoint before frequency response events occur. This advance planning allows the controller to prepare appropriate power setpoints that account for predicted energy and demand charges, ensuring that frequency response participation does not result in excessive energy costs
Solution Approach 2:
The system uses feedback from regulation signals and monitoring of state of charge to continuously adjust power setpoints. This feedback mechanism ensures that the controller can adapt to actual grid conditions and battery state, optimizing the balance between revenue generation and energy cost management in real-time
Data Source
AI summary
A frequency response optimization system includes a battery configured to store and discharge electric power, a power inverter configured to control an amount of the electric power stored or discharged from the battery at each of a plurality of time steps during a frequency response period, and a frequency response controller. The frequency response controller is configured to receive a regulation signal from an incentive provider, determine statistics of the regulation signal, use the statistics of the regulation signal to generate an optimal frequency response midpoint that achieves a desired change in a state-of-charge (SOC) of the battery while participating in a frequency response program, and use the midpoints to determine optimal battery power setpoints for the power inverter. The power inverter is configured to use the optimal battery power setpoints to control the amount of the electric power stored or discharged from the battery.