Adaptive Energy Storage Control for Predictive Co-Optimization
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Solution Overview
Problem
Current energy automation control systems lack advanced tools for predictive analytics, dynamic aggregation, and co-optimization of energy storage systems, leading to inefficiencies and manual intervention by operators.
Innovation Solution
An adaptive energy operating system that integrates predictive analytics, energy asset modeling, and revenue generation modeling to optimize energy storage system performance and economic efficiency, using a modular software architecture with drivers, libraries, and applications to manage and communicate with energy storage devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation and control is used for energy storage systems, then operational flexibility is maintained, but productivity and energy efficiency deteriorate due to manual intervention requirements
Solution Approach 1:
The energy storage system incorporates an automated control system that enables self-service operation. The system automatically monitors its own status, manages charge/discharge cycles, and optimizes performance without requiring manual intervention, thereby improving productivity while maintaining operational flexibility through programmable parameters.
Solution Approach 2:
The patent replaces manual mechanical control operations with an automated electronic control system. The control system uses sensors, processors, and communication interfaces to automatically manage energy storage operations, substituting human operators with automated mechanisms that enhance efficiency and reduce manual intervention.
2Measurement precision
If simple control systems are used for energy storage, then device complexity is reduced, but measurement precision and predictive analytics capability deteriorate
Solution Approach 1:
The control system is segmented into modular functional components including sensing modules, processing modules, communication modules, and actuation modules. Each module performs a specific function with high precision, allowing the system to achieve accurate measurement and predictive analytics while managing complexity through modular architecture where each segment can be independently optimized and maintained.
3Loss of information
If energy storage systems operate without integrated management, then device complexity is minimized, but loss of information and economic optimization capability worsen
Solution Approach 1:
The integrated management system serves multiple functions simultaneously: it monitors energy storage status, tracks operational data, performs predictive analytics, optimizes economic performance, and communicates with external systems. This multi-functional approach reduces information loss by centralizing data collection and processing while managing complexity through a unified system architecture that handles diverse tasks through standardized interfaces and protocols.
Data Source
AI summary
The present disclosure provides an adaptive energy storage operating system that is programmed or otherwise configured to operate and optimize various types of energy storage devices.