Adaptive Energy Storage Operating System for Multi-Service Coordination
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Solution Overview
Problem
Current energy storage systems lack an efficient and adaptive software solution to optimize and manage multiple value streams, failing to effectively coordinate components and integrate with various energy services and devices.
Innovation Solution
A modular software operating system that includes adaptive energy computing modules, device drivers, libraries, and applications to optimize energy storage systems based on external signals and operator preferences, enabling communication, data transformation, and control across energy storage devices and networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a modular software operating system with adaptive energy computing modules is implemented, then the system can optimize and manage multiple value streams and coordinate components effectively, but the device complexity increases due to the modular architecture including drivers, libraries, and applications
Solution Approach 1:
The software operating system is divided into distinct modular components including energy computing modules, device drivers, libraries, and applications. Each module performs a specific function and can be independently developed, deployed, and maintained. This segmentation allows the system to manage complexity through organized modularity while maintaining the ability to optimize multiple value streams through coordinated module interactions.
Solution Approach 2:
The energy operating system is designed as a universal platform that can manage multiple energy storage devices and coordinate various energy services through a common architecture. The modular design with standardized interfaces enables the system to handle diverse energy storage technologies and multiple value streams (economic services) without requiring separate specialized systems for each function.
2Ease of operation
If the operating system integrates with various energy storage devices and services, then the ease of operation improves through automated management, but the device complexity increases due to integration requirements
Solution Approach 1:
The energy operating system acts as an intermediary layer between energy storage devices and the management interface. Device drivers serve as specific intermediaries that translate between device-specific protocols and the universal operating system interface. This intermediary architecture enables automated management and coordination of multiple devices and services while encapsulating integration complexity within the driver layer, protecting the upper-level applications from device-specific complexities.
Solution Approach 2:
The system incorporates automated rule-based algorithms that enable self-service optimization of energy storage operations. The energy computing modules automatically analyze pricing signals, communications signals, rate structures, and system status to generate optimal operational decisions without requiring constant manual intervention. This automation improves ease of operation while the modular architecture manages the underlying complexity.
3Productivity
If adaptive rules and algorithms are used to optimize energy storage operation based on external signals, then the productivity improves through optimized energy management, but the device complexity increases due to the computational requirements
Solution Approach 1:
The system employs pre-configured rule-based algorithms that are established in advance for optimizing energy storage operations. These algorithms incorporate predetermined logic for responding to pricing signals, rate structures, and system conditions. By having rules prepared beforehand rather than requiring complex real-time computation for every decision, the system achieves productive optimization while managing computational complexity through pre-planned decision frameworks.
Solution Approach 2:
The energy operating system continuously monitors system status, pricing signals, and operational outcomes, using this feedback to adjust and optimize energy storage operations. The adaptive rules incorporate feedback loops where system performance is constantly evaluated and operational parameters are adjusted accordingly. This feedback mechanism enables productive optimization through data-driven decisions while the modular rule-based architecture manages the complexity of computational requirements.
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.