Bi-directional Switch Algorithm for Energy Storage Arbitrage
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Solution Overview
Problem
Existing energy storage systems, like BESS, face challenges in efficiently regulating charge and discharge to maximize revenue while adhering to power purchase agreements, as they typically follow preset schedules or rely solely on frequency regulation, failing to capitalize on real-time electricity price fluctuations.
Innovation Solution
A smart control algorithm for bi-directional switches that uses both forecasted and real-time electricity pricing data to set a threshold, charging energy storage devices when prices are low and discharging when prices are high, with periodic threshold adjustments to capture the latest data, allowing for dynamic and revenue-maximizing operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset 24 hour schedule is followed for charging and discharging, then operation simplicity is maintained, but revenue optimization is limited
Solution Approach 1:
The system transitions from a static preset schedule to a dynamic control approach where charging and discharging decisions are adjusted in real-time based on electricity price signals. The controller continuously monitors price data and modifies operation parameters to capture revenue opportunities, thereby resolving the contradiction between operational simplicity and revenue optimization.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring electricity price signals and using this information to adjust charging and discharging operations. Price data serves as feedback that informs control decisions, enabling the system to optimize revenue while maintaining manageable complexity through automated price-responsive control.
2Reliability
If frequency regulation method is used for discharging, then grid stability is maintained, but market price fluctuations are not exploited
Solution Approach 1:
The energy storage system is designed to perform multiple functions: frequency regulation to maintain grid stability and price-responsive energy arbitrage to maximize revenue. The controller can switch between or combine these functions based on operating conditions, allowing the system to simultaneously address reliability and productivity requirements.
Solution Approach 2:
The system dynamically adjusts its operational mode based on real-time conditions. When frequency regulation is needed, it maintains grid stability; when price differentials are favorable, it transitions to charge/discharge operations for revenue optimization. This dynamic adaptability resolves the contradiction between maintaining grid stability and exploiting market opportunities.
3Productivity
If real-time price monitoring and dynamic threshold adjustment is implemented, then revenue optimization is improved, but system complexity increases
Solution Approach 1:
The system employs self-service control where the controller automatically monitors price signals, determines optimal charge/discharge timing, and executes operations without requiring complex external management. This automated self-regulation reduces operational complexity while maintaining revenue optimization benefits.
Solution Approach 2:
The system manages complexity by focusing on changing key parameters (charge/discharge state, price thresholds) rather than overhauling the entire system architecture. By dynamically adjusting these critical parameters in response to price signals, the system achieves revenue optimization with minimal added complexity.
Data Source
AI summary
A system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high. The invention uses two different types of pricing data: forecasted price data and real-time price data. The forecasted price data is used to set a threshold. When the real-time price data of electricity exceeds this threshold, the energy storage device is set to discharge and send power to the grid. Otherwise the energy storage device is set to charge. The threshold is set periodically, typically in 30 minute to several hour intervals to capture the latest data.