Systems for machine learning, optimising and managing local multi-asset flexibility of distributed energy storage resources

Abstract

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.

Description

technical field [0001] The present invention relates to managing groups of distributed energy storage resources, such as batteries and electric vehicles, through machine learning and other optimization methods to facilitate power system balancing and local network constraint management, and to maximize the performance of multiple energy system stakeholders. Background technique [0002] Energy storage represents a growing asset class in the energy system and an opportunity to help manage and shift the supply of low-carbon generation sources such as wind and solar, and to help manage the shape of the energy demand map and power system management. Management challenges increase when there are abundant energy storage and flexibility resources on the grid, especially as the adoption of electric vehicles increases and the pressure on local networks to accommodate large fluctuations in electricity consumption, such as increasing electric vehicle charging rates, increases. higher. ...

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