A system for optimising and managing distributed energy storage resources gathers data and monitors usage of end devices and resources at remote sites in a network, and determines a battery charging plan for charging/discharging batteries at the remote sites, where the batteries may be electric vehicle (EV) batteries. End devices at the remote sites control charging in accordance with their charging plans, but also implement a charging protocol to respond to local constraints, congestion, or local limits, to optimise energy transmission. The charging protocol begins charging/discharging at an initial rate, periodically increments the rate towards a target rate according to the charging plan, determines that a local limit has been reached, and in response reduces the rate. The local limit may be determined by monitoring network voltage or frequency. The battery charging plan may be adjusted for market and tariff signals, weather data, location constraints, and energy use by the building or vehicle at the remote location, or predictions and models of usage patterns and network performance. Also claimed are a system for classification of events in an energy system using a recurrent neural network, and a method of recording energy charging events in a mesh-chain.


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    Title :

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


    Contributors:

    Publication date :

    2020-04-15


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    H02J CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER , Schaltungsanordnungen oder Systeme für die Abgabe oder Verteilung elektrischer Leistung / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen



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