Distributed Energy Flexibility Orchestration for Local Grid Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies fail to effectively manage and optimize groups of distributed energy storage resources, such as batteries and electric vehicles, to balance electrical systems and manage local network constraints, particularly in islanded energy systems or those with limited interconnection.
Innovation Solution
A management and optimization system that uses software protocols and connectivity to gather data, monitor usage, and process external signals, enabling the coordination of flexibility in distributed energy resources to achieve specific performance objectives over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large numbers of energy storage and flexibility resources are deployed on the grid, then the ability to manage and shift supply from low carbon generation resources improves, but the management complexity and infrastructure challenges increase significantly
Solution Approach 1:
The system segments the management of distributed energy resources by implementing a hierarchical architecture that divides control functions between local edge devices and central cloud platform. This segmentation allows individual resources to be managed independently while maintaining collective optimization, reducing overall system complexity despite large numbers of deployed assets.
Solution Approach 2:
The patent introduces an intermediary management platform that acts as a mediator between distributed energy resources and the grid. This intermediary handles the complexity of coordinating numerous resources by providing standardized interfaces, aggregation functions, and intelligent control algorithms, thereby simplifying the management burden while maintaining high productivity.
2Adaptability or versatility
If electric vehicle adoption increases with higher rate charging, then mobility flexibility improves, but the pressure on local networks to accommodate power consumption increases
Solution Approach 1:
The system implements preliminary action by pre-scheduling EV charging during periods of low grid demand or high renewable generation. The management platform forecasts charging needs and automatically schedules charging sessions to occur when grid stress is minimal, thereby maintaining mobility flexibility while preventing network overload.
Solution Approach 2:
The patent applies periodic action through time-varying charging rates that respond to grid conditions. Instead of continuous high-rate charging, the system implements periodic charging cycles that adjust power delivery based on real-time grid status, renewable availability, and demand patterns, reducing peak network pressure while maintaining overall charging throughput.
3Reliability
If energy systems are islanded or have limited interconnection, then energy independence improves, but the ability to manage flexibility within the energy system becomes more challenging
Solution Approach 1:
The patent applies merging by combining multiple distributed energy resources (solar, wind, battery storage, EVs) into a unified virtual power plant managed by the cloud platform. This consolidation allows the islanded system to manage flexibility collectively, where the combined capacity of diverse resources provides the adaptability needed for energy independence without requiring complex individual resource management.
Solution Approach 2:
The system implements parameter changes by dynamically adjusting operational parameters of energy resources based on local conditions. The management platform modifies charging/discharging rates, renewable curtailment levels, and load shifting strategies in response to changing weather patterns, storage state-of-charge, and demand conditions, enabling flexible management within the constraints of an islanded system.
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.