Local Energy Storage Orchestration for EV Charging and Grid Constraints
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Solution Overview
Problem
Existing technologies fail to effectively manage and optimize groups of diverse energy storage resources such as batteries and electric vehicles, particularly in islanded or low-interconnected networks, and do not address the need for adaptive, resilient, and financially optimal management across multiple stakeholders with changing energy systems and regulations.
Innovation Solution
A management and optimization system using software, connectivity, and protocols to coordinate and orchestrate energy storage resources, enabling real-time data processing, self-regulating control, and financial management to achieve balanced energy performance and minimize operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large numbers of energy storage and flexibility resources are deployed on the grid, then energy system flexibility and renewable integration improve, but network management complexity and infrastructure challenges increase
Solution Approach 1:
The patent combines multiple energy storage resources (batteries, electric vehicles, home storage) into a single coordinated system managed by central software and machine learning algorithms. This merging approach allows the system to handle diverse resources uniformly, reducing management complexity while maintaining flexibility. The coordinated control of aggregated resources enables efficient response to grid needs without requiring separate management systems for each resource type.
Solution Approach 2:
The patent introduces an intermediary management layer consisting of software systems and machine learning algorithms that mediate between the grid operator and distributed energy storage resources. This intermediary layer translates grid requirements into resource-specific control signals and aggregates resource responses, simplifying the interface between diverse resources and the grid while maintaining system flexibility.
2Productivity
If electric vehicle charging rates are increased to meet demand, then mobility service quality improves, but local network constraints and infrastructure pressure worsen
Solution Approach 1:
The patent uses machine learning algorithms to predict future energy demand patterns and pre-coordinate charging schedules for electric vehicles. By anticipating peak demand periods and preparing charging plans in advance, the system can smooth out charging loads and avoid sudden spikes that would strain the network, thereby maintaining high charging productivity without exceeding infrastructure constraints.
Solution Approach 2:
The patent implements dynamic charging rate adjustment based on real-time network conditions and predicted demand. The system continuously adapts charging rates for electric vehicles, increasing them when network capacity is available and reducing them when constraints are detected. This dynamic approach allows the system to maximize charging productivity while respecting infrastructure limits at any given moment.
3Reliability
If distributed energy resources are managed without centralized coordination, then system autonomy and resilience improve, but optimization efficiency and stakeholder performance worsen
Solution Approach 1:
The patent segments the energy management system into autonomous local control units at each resource location and a centralized software coordination layer. Each local unit maintains autonomy for immediate response and self-protection, while the centralized layer provides coordinated optimization across the entire system. This segmentation allows the system to simultaneously achieve high local reliability through autonomy and high overall efficiency through centralized optimization.
Solution Approach 2:
The patent implements different control strategies at different levels of the system. Local control units handle time-critical autonomy functions such as immediate safety responses and local balance maintenance, while the centralized software system handles strategic optimization functions such as multi-stakeholder performance maximization and long-term resource scheduling. This local quality differentiation ensures both autonomy and optimization efficiency are achieved in their respective domains.
Data Source
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.