Aggregating Small-Scale Energy Storage for Grid Balance
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Solution Overview
Problem
The integration of renewable energy sources into electrical networks introduces uncertainty in energy supply, making it challenging to maintain a balance between supply and demand, and existing energy storage solutions are often costly and inefficient.
Innovation Solution
A system and method for optimal aggregation of small-scale energy storage, which involves generating predicted energy consumption and generation data, determining dispatchable energy storage capacity, and dispatching energy storage devices to balance supply and demand in real-time, utilizing a processor and memory to manage and control energy storage devices such as mobile phones, electric vehicles, and home appliances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated energy storage solutions are installed to balance supply and demand, then supply-demand balance is improved, but installation and maintenance costs increase
Solution Approach 1:
The patent aggregates multiple small-scale energy storage devices (batteries, electric vehicles, home appliances) into a coordinated network system. By merging these distributed resources under centralized control, the system achieves grid-scale energy storage functionality without requiring dedicated large-scale storage infrastructure, thereby reducing installation and maintenance costs while maintaining supply-demand balance.
Solution Approach 2:
The system enables small-scale energy storage devices to serve multiple functions: balancing supply and demand, protecting network components from peak loads, and providing dispatchable capacity. This multi-functionality reduces the need for specialized dedicated storage installations, lowering overall system costs while achieving reliable grid management.
2Object-affected harmful factors
If renewable generation is increased to reduce emissions, then environmental impact is improved, but supply uncertainty increases
Solution Approach 1:
The system continuously monitors predicted energy consumption and generation data, comparing actual renewable supply with demand requirements. This feedback mechanism enables real-time adjustments to dispatch small-scale energy storage devices, compensating for renewable variability and maintaining supply reliability while preserving the environmental benefits of increased renewable generation.
Solution Approach 2:
The system generates predicted energy consumption and generation data in advance, allowing proactive planning of energy storage dispatch. By anticipating renewable generation patterns and demand fluctuations, the system can pre-position energy storage resources to compensate for supply uncertainty, ensuring reliable delivery while maintaining high renewable penetration.
3Reliability
If peak load capacity is increased to prevent blackouts, then network reliability is improved, but component sizing and cost increase
Solution Approach 1:
Instead of sizing components for static peak load capacity, the system dynamically deploys small-scale energy storage devices only when needed. The dispatchable energy storage capacity is activated during peak demand periods or contingency events, allowing network components to be sized for average rather than peak conditions, thereby reducing component sizing and associated costs while maintaining network reliability.
Data Source
AI summary
A system for optimal aggregation of small-scale energy storage capacity includes a processor operatively coupled to memory. The processor is configured to implement the steps of: generating predicted energy consumption data based on a model of expected energy usage within a given electrical network; generating predicted energy generation data based on a model of expected energy generation for the given electrical network; generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network; determining a set of energy storage devices that need to be dispatched for the given electrical network; comparing the predicted energy consumption data with the predicted energy generation data; and dispatching the set of energy storage devices for the given electrical network.