Battery Frequency Response Optimization via Dynamic Midpoint Control
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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 with battery degradation and operational costs.
Innovation Solution
A frequency response optimization system that includes a battery, a power inverter, and a frequency response controller, which uses regulation signals to determine optimal battery power setpoints, considering statistics, filter parameters, and objective functions that account for revenue, degradation, and operational constraints to maintain a constant battery state-of-charge and optimize energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the battery storage controller adjusts the midpoint to participate in frequency response programs, then revenue generation is improved, but battery degradation increases and operational costs increase
Solution Approach 1:
The system dynamically changes the midpoint parameter around which the battery operates during frequency response events. By adjusting the midpoint based on predicted regulation signals and battery state, the controller optimizes the balance between revenue generation and battery degradation, allowing the battery to participate in frequency response programs while managing its operational lifespan.
Solution Approach 2:
The system performs preliminary calculations of the optimal midpoint adjustment before frequency response events occur. By predicting regulation signals and calculating the optimal midpoint in advance, the controller prepares the battery to participate in frequency response programs while pre-planning to minimize degradation and operational costs.
2Productivity
If the battery storage controller adjusts the midpoint to participate in frequency response programs, then revenue generation is improved, but operational costs increase
Solution Approach 1:
The system dynamically adjusts the midpoint parameter to optimize the balance between revenue generation and operational costs. By changing the operational midpoint based on predicted regulation signals and battery state, the controller minimizes energy losses and operational costs while maintaining frequency response revenue generation.
3Reliability
If the battery maintains constant state-of-charge at beginning and end of frequency response period, then battery health is improved, but flexibility in power setpoint adjustment is constrained
Solution Approach 1:
The system dynamically determines power setpoints during the frequency response period while maintaining constant state-of-charge at the beginning and end. By allowing dynamic adjustment of power setpoints within the constraints of constant SOC, the controller achieves both battery health preservation and operational flexibility.
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, and a frequency response controller. The frequency response controller includes receiving a regulation signal from an incentive provider, determining statistics of the regulation signal, using the statistics of the regulation signal to generate a frequency response midpoint, and using the frequency response midpoint 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.