Hybrid Energy Storage Modeling for Predictive Grid Management
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Solution Overview
Problem
Current energy management solutions lack a simple and reliable method to dynamically consider energy storage availability and consumer interaction with the electricity grid, particularly in demand response programs.
Innovation Solution
A method using computational agents to represent and simulate physical and potential energy storage elements, forming a hybrid assembly that determines energy charging or discharging based on generation and load requirements, incorporating real-time simulation and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational agents are used to represent and simulate energy storage elements, then predictive management capability is improved, but system complexity increases
Solution Approach 1:
The patent creates virtual copies of physical energy storage elements through computational agents. Each physical energy storage element is represented by a computational agent that simulates its behavior, allowing predictive management without directly controlling physical systems. This copying approach enables safe testing and modeling while reducing risks to actual equipment.
Solution Approach 2:
Computational agents serve as intermediaries between the control system and physical energy storage elements. The hybrid assembly of computational and physical agents acts as a mediator layer that enables predictive management while isolating the control system from direct interaction with physical equipment, thus managing complexity through abstraction.
2Adaptability or versatility
If hybrid assembly with simulated elements is used, then energy management flexibility is improved, but computational resource requirements increase
Solution Approach 1:
The system implements a hybrid assembly that includes only necessary simulated elements rather than fully simulating all energy storage elements. By selectively creating computational agents only where needed for predictive management and testing, the system achieves flexibility while avoiding excessive computational resource consumption.
3Extent of automation
If real-time simulation is implemented, then demand response automation is improved, but processing time requirements increase
Solution Approach 1:
The computational agents perform predictive simulations and evaluate energy management strategies in advance before actual demand response events occur. By pre-computing scenarios and preparing predictive models ahead of time, the system enables automated real-time decision-making without excessive processing delays during critical events.
Data Source
Figure 1
AI summary
The present disclosure is within the area of electrical energy grid management, especially referring to the management of several energy resources with focus on energy storage. Although there exist numerous solutions addressing energy management from energy providers which include storage, these solutions are missing a simple and reliable way to dynamically consider availability for the energy management, considering energy storage. The solution of the present disclosure further allows to test different energy availabilities and model a consumer interaction with the electricity grid. Moreover, an actuation in energy storage allows to fully automate a process on behalf of the consumer, namely in the context of demand response programs.