Energy Storage Flow Scheduling Under Grid Threshold Limits
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Solution Overview
Problem
The electrical grid infrastructure components can be overloaded or damaged when energy storage devices, such as electric vehicle batteries, consume or provide energy beyond their threshold limits, especially during periods of high demand or variability in energy consumption.
Innovation Solution
A system predicts energy consumption and availability of energy storage devices using historical data and infrastructure thresholds to generate optimized energy flow schedules, ensuring devices consume energy within safe limits and reducing the risk of overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy storage devices consume or provide energy without optimization, then energy availability and responsiveness are improved, but infrastructure components may be overloaded or damaged
Solution Approach 1:
The system performs preliminary actions by predicting future energy consumption and availability of energy storage devices before actual energy transactions occur. The optimization system uses historical data and machine learning models to forecast energy needs and generate optimized energy flow schedules in advance, preventing infrastructure overload before it happens rather than reacting after damage occurs.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy consumption and availability data, comparing it against predicted values, and using this information to refine future predictions and optimizations. The system receives feedback from infrastructure components about their status and uses this to adjust energy flow schedules, creating a closed-loop control system that improves reliability while maintaining productivity.
2Reliability
If energy flow is optimized using predictions and schedules, then infrastructure component safety is improved, but system complexity increases
Solution Approach 1:
The optimization system performs multiple functions using a unified approach: it predicts energy consumption, predicts energy availability, generates optimized schedules, and monitors infrastructure status all through the same machine learning models and data processing pipelines. This multi-functionality reduces the need for separate specialized systems for each task, managing complexity while comprehensively improving infrastructure safety.
Solution Approach 2:
The system enables energy storage devices to essentially self-regulate their energy flows by providing them with optimized schedules and instructions. The devices autonomously adjust their charging and discharging behaviors based on the generated schedules, reducing the need for complex external control mechanisms and simplifying the overall system architecture while maintaining safety.
3Reliability
If energy consumption is restricted to scheduled time steps, then infrastructure overload is reduced, but energy access flexibility decreases
Solution Approach 1:
The energy flow schedules generated by the system are dynamic rather than static. The optimization models continuously adapt schedules based on changing conditions including updated predictions of energy availability, changing infrastructure status, and evolving energy needs. This allows the system to maintain reliability through scheduled restrictions while preserving flexibility through real-time adaptations to new information.
Solution Approach 2:
The system changes key parameters of energy flow schedules based on predicted conditions and actual performance. It adjusts timing, duration, and intensity of energy consumption periods dynamically, transforming rigid scheduled restrictions into flexible parameter-based guidelines that maintain infrastructure protection while adapting to varying energy needs and availability conditions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing energy storage energy flow schedules. One of the methods includes predicting a quantity of energy storage devices from a plurality of energy storage devices that will likely consume energy during a time period; for at least some devices from a plurality of energy storage devices, predicting a state of charge for the respective device; using the predicted quantity and the predicted states of charge, predicting an amount of energy that will be needed by devices from the plurality of energy storage devices during the time period; generating, using the predicted amount of energy, an energy flow schedule for one or more devices from the plurality of energy storage devices; and providing, to at least some of the one or more devices, instructions to cause the respective device to execute a respective energy flow schedule.