Frequency response optimization based on a change in battery state-of-charge during a frequency response period
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Solution Overview
Problem
Determining optimal adjustments for battery storage controllers to actively participate in frequency response markets while minimizing 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 receives regulation signals, determines optimal power setpoints, and manages battery state-of-charge to maximize revenue while minimizing degradation costs, using high and low-level controllers and battery life models to filter signals and constrain objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the battery storage controller adjusts the midpoint to actively participate in frequency response markets, then frequency response revenue is improved, but battery degradation and operational costs increase
Solution Approach 1:
The system dynamically adjusts the midpoint parameter based on the state of charge (SOC) of the battery. When SOC is high, the midpoint is shifted to allow more discharge; when SOC is low, the midpoint is shifted to allow more charge. This parameter change enables the system to maximize frequency response revenue while preventing excessive battery degradation by adapting to real-time battery conditions.
Solution Approach 2:
The controller continuously monitors the state of charge (SOC) of the battery and uses this feedback to adjust the midpoint dynamically. This closed-loop feedback mechanism ensures that the system responds to changing battery conditions in real-time, optimizing the balance between revenue generation and battery preservation without requiring complex predictive models.
2Ease of operation
If the battery storage controller maintains a fixed midpoint for frequency response, then operational simplicity is improved, but the ability to optimize revenue while managing battery degradation is reduced
Solution Approach 1:
The system transitions from a static fixed midpoint approach to a dynamic midpoint adjustment mechanism. The midpoint is no longer a constant value but changes dynamically based on the battery's state of charge. This dynamic approach maintains operational simplicity from the user perspective while internally optimizing revenue and managing battery degradation through automated SOC-based adjustments.
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.