Local Flexibility Control for Distributed Energy Storage Clusters
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Solution Overview
Problem
Existing technologies fail to effectively manage and optimize groups of diverse energy storage resources like batteries and electric vehicles, neglecting the need for adaptive solutions that combine machine learning and optimization to balance individual and collective interests, manage financial transactions, and minimize operational costs in dynamic energy systems.
Innovation Solution
A management and optimization system using software systems, connectivity protocols, and data exchange to coordinate and schedule energy resources, enabling real-time control and self-regulation across distributed assets, including batteries, electric vehicles, and other devices, to achieve balanced energy supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large numbers of energy storage and flexibility resources are deployed on the grid, then energy system capacity and flexibility improve, but management complexity and infrastructure challenges increase
Solution Approach 1:
The patent combines multiple types of energy storage resources (batteries, electric vehicles, home storage) into a single aggregated management system. This merging approach allows diverse resources to be managed collectively through unified software platforms and communication protocols, reducing the overall management complexity despite the large number of individual assets.
Solution Approach 2:
The patent introduces intermediary software systems, communication protocols, and aggregation layers that mediate between individual energy storage resources and the grid operator. These intermediaries handle the complexity of managing large numbers of resources by providing standardized interfaces, data aggregation, and coordinated control mechanisms.
2Reliability
If real-time control and self-regulation are implemented across distributed assets, then energy system resilience and optimization improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent implements self-service mechanisms where distributed energy storage assets automatically adjust their operation based on pre-configured rules, market signals, and coordination protocols. Individual assets perform self-regulation and real-time control actions without requiring constant centralized intervention, thereby improving resilience while limiting the growth of system complexity.
Solution Approach 2:
The patent employs dynamic control strategies where the level of centralization and automation adapts based on system conditions, asset types, and operational requirements. This dynamic approach allows real-time control to be implemented efficiently by adjusting the complexity of control algorithms and communication requirements to match actual system needs.
3Productivity
If adaptive solutions combining machine learning and optimization are used, then asset performance and financial returns are maximized, but computational requirements and data processing needs increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models and optimizing control algorithms during off-peak periods or using historical data. This allows complex computational tasks to be performed in advance, reducing real-time computational requirements while maintaining high asset performance through pre-computed optimization strategies and predictive models.
Solution Approach 2:
The patent implements partial optimization strategies where machine learning and optimization algorithms are applied selectively to the most critical assets or time periods rather than uniformly across all resources. This partial action approach maximizes performance benefits for key assets while limiting overall computational requirements to manageable levels.
Data Source
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AI summary
Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.