CV Algorithm for Energy Storage Peak Shaving
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Solution Overview
Problem
Existing peak shaving algorithms for energy storage systems are computationally expensive and unstable, leading to inefficient reduction of peak power demand, particularly in commercial and industrial settings where high peak demand charges are a significant economic burden.
Innovation Solution
A novel algorithm, referred to as the CV algorithm, which incorporates features such as ratcheting, dead band, roll-off, and dispatch calculation to regulate energy storage system discharging and charging, allowing for effective peak shaving based on current and short-term historical power usage without requiring predictive foresight, and can be implemented on low-cost hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex predictive techniques (e.g., machine learning, neural networks) are used for peak shaving algorithms, then peak power demand reduction effectiveness is improved, but computational cost and device complexity increase significantly
Solution Approach 1:
The patent replaces expensive, complex predictive algorithms with a simple, computationally inexpensive algorithm that can be executed on low-cost hardware. The CV algorithm uses basic arithmetic operations and conditional logic instead of machine learning models, effectively substituting expensive computational resources with cheap, simple processing that achieves comparable or superior peak shaving results.
Solution Approach 2:
The patent substitutes complex computational systems (machine learning, neural networks) with a simpler algorithmic approach based on basic mathematical operations. The CV algorithm replaces the need for expensive computational hardware and complex software systems with a lightweight solution that operates efficiently on standard microcontrollers or simple processors.
2Reliability
If certain well-tested peak shaving algorithms are used in specific scenarios, then effective peak reduction is achieved, but the algorithms become unstable and perform poorly in general use cases
Solution Approach 1:
The CV algorithm is designed to be universally applicable across diverse peak shaving scenarios without requiring scenario-specific tuning or multiple different algorithms. It handles various load profiles, energy storage capacities, and utility rate structures through a single unified approach, making it both stable and versatile across different use cases.
Solution Approach 2:
The algorithm dynamically adjusts its behavior based on real-time conditions such as current power demand, energy storage state of charge, and utility pricing signals. This dynamic adaptation allows the same algorithm to effectively handle varying scenarios while maintaining stability, as it responds to changing conditions rather than relying on fixed parameters tuned for specific scenarios.
Data Source
AI summary
Techniques for controlling an energy storage device to reduce peak power demand at a site are provided. In one embodiment, instantaneous power usage at the site can be monitored, where the instantaneous power usage corresponds to power that is instantaneously imported or exported at a point of common coupling (PCC) between the site and a utility-managed energy grid. A historical power usage value for the site can then be calculated based on the monitored instantaneous power usage, and the historical power usage value can be compared with a target peak value plus a buffer value. If the historical power usage value exceeds the target peak value plus the buffer value, the target peak value can be set to the historical power usage value.