Customer-Specific Control Model for Energy Storage Optimization
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Solution Overview
Problem
Current energy storage systems are not economically attractive due to their inability to effectively manage and optimize the benefits of energy production and storage, particularly with intermittent renewable sources, leading to challenges in grid stability and efficiency.
Innovation Solution
A method and system that utilize time-series data and customer-specific data to develop and deploy a customer-specific control model for energy storage systems, enabling them to determine optimal modes of operation, such as charging, discharging, or doing nothing, to maximize financial returns and improve grid stability by managing real and reactive power flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy storage systems are installed to store energy from intermittent renewable sources, then energy availability is improved, but economic attractiveness deteriorates due to inability to optimize benefits
Solution Approach 1:
The system changes operational parameters dynamically by using machine learning models to determine optimal charge/discharge timing, state of charge levels, and power output based on real-time and historical data from multiple sources including market prices, weather forecasts, and grid conditions
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring actual performance against predictions, using machine learning models that learn from historical operational data and market outcomes to improve future control decisions, creating a self-optimizing system
2Ease of operation
If energy storage systems operate without optimized control, then operational simplicity is maintained, but productivity deteriorates due to inability to maximize financial returns
Solution Approach 1:
The system enables self-service operation by implementing autonomous control where the energy storage system automatically makes operational decisions using embedded machine learning models, eliminating the need for manual intervention while maximizing financial returns through optimized charge/discharge cycling
Solution Approach 2:
The system dynamically adjusts operational parameters including state of charge, power output, and cycling frequency based on real-time market conditions, weather forecasts, and grid requirements, allowing the system to adapt to changing conditions without manual control
3Adaptability or versatility
If distributed energy production is increased, then energy independence is improved, but grid stability deteriorates due to intermittency of renewable sources
Solution Approach 1:
The system implements real-time feedback control by monitoring grid frequency, voltage, and power flow conditions, using machine learning models to predict optimal response actions that maintain grid stability while maximizing local energy production from intermittent renewable sources
Solution Approach 2:
The system performs preliminary actions by using weather forecasts and market predictions to pre-charge or pre-discharge energy storage systems in anticipation of future grid conditions, renewable generation availability, and price signals, thereby proactively maintaining stability
Data Source
AI summary
A method for controlling an energy storage system which includes receiving time-series data and customer specific data and developing one or more customer specific control models based, at least in part, on the time-series data and the customer specific data. After developing one or more customer specific control models, the method proceeds by training the customer specific control models and then deploying the customer specific control model to the customer for use by the customer to determine which of a plurality of modes the energy storage system should be in. The method may include the development, deployment and/or execution of one or more centralized control models for controlling a network of any combination of common and/or different customer control models.