10078489Using AI to optimise energy flow management in urban electric vehicle chargingClosedCollaborative R&DInnovate UKIn this project Lesla Ltd and Cranfield University will carry out a feasibility study to determine the merits and viability of using Artificial Intelligence solutions to optimise energy flow management of numerous Lesla on-street electric vehicle chargers forming energy microgrids. Urban EV charging is essential to reach UK Net-Zero legislative targets. More than 80% of electric vehicles charge at home, but 38% of households in UK don't have access to off-street parking, and they would depend on public on-street charging, which currently is critically underserved - less than 2% of EVs rely on on-street parking now. Lesla kerb charger system solves this problem. It encourages electric cars to be plugged in all the time while parked overnight, taking advantage of best energy pricing as well as income from advanced grid services, making energy more affordable, which lately has been particularly relevant for many households in UK. Optimising energy flow management for many urban chargers involves multifaceted relationships between many energy market players, each with their own, sometimes contradictory requirements, which not always can be reliably predicted. This project will research the feasibility of using Artificial Intelligence to forecast and optimise the energy flows within these microgrids formed by electric vehicles and multi-dweller urban buildings: * to ensure that the electric cars can be charged with the lowest-priced energy, incentivising to charge when renewable energy is abundant, and * shifting the EV charging away from periods of high grid demand, to ensure that existing grid connections can be used without increasing the overall neighbourhood grid connection capacity requirements. * to enable community to earn from advanced grid services and load balancing tools. Artificial intelligence solution should be capable of providing a self-adapting energy flow management system, which would continually learn from the behaviours of buildings' residents and electric vehicle drivers, to be suitable for deployment in new neighbourhoods, and adding new charger devices for mass deployment in the future. The feasibility study will: * provide an assessment of technical, economic, financial, legal, and environmental considerations for AI enabled Lesla EV kerb charger management within urban microgrid. * engage with stakeholders, - to substantiate user needs and their responsiveness to energy use incentivisation, - to establish predictable charging profiles from empirical data. * determine the likelihood of success before conducting a subsequent larger project.