Using AI to optimise energy flow management in urban electric vehicle charging

利用人工智能优化城市电动汽车充电能量流管理

• 批准号: 10078489
• 金额: $ 6.36万
• 依托单位: LESLA LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10078489
• 关键词: Using; AI; optimise; energy; flow

In 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.

在这个项目中，Lesla有限公司和克兰菲尔德大学将进行一项可行性研究，以确定使用人工智能解决方案的优点和可行性，以优化众多Lesla街头电动汽车充电器形成能源微电网的能量流管理。城市电动汽车充电是实现英国净零立法目标的关键。超过80%的电动汽车在家中充电，但英国38%的家庭没有路外停车位，他们将依赖公共道路充电，目前服务严重不足-现在只有不到2%的电动汽车依赖道路停车。Lesla路边充电器系统解决了这个问题。它鼓励电动汽车在夜间停车时一直插上电源，利用最佳能源定价以及先进电网服务的收入，使能源更实惠，最近对英国的许多家庭特别重要。优化许多城市充电器的能量流管理涉及许多能源市场参与者之间的多方面关系，每个参与者都有自己的，有时相互矛盾的要求，这并不总是能够可靠地预测。该项目将研究使用人工智能来预测和优化由电动汽车和多居民城市建筑形成的微电网内的能量流的可行性：* 确保电动汽车可以用最低价格的能源充电，激励充电时可再生能源丰富，* 将电动汽车充电从高电网需求时期转移，以确保现有的电网连接可以使用而不增加整体社区电网连接容量要求。让社区能从先进的电网服务和负载平衡工具中获益。人工智能解决方案应能提供一个自适应的能量流管理系统，该系统将不断从建筑物居民和电动汽车司机的行为中学习，以适合在新的社区部署，并增加新的充电器设备，以便将来大规模部署。可行性研究将：* 为城市微电网内支持AI的Lesla EV路边充电器管理提供技术、经济、财务、法律的和环境考虑因素评估。*与利益相关者合作，-证实用户需求及其对能源使用激励措施的反应，-根据经验数据建立可预测的收费模式。在进行后续更大的项目之前确定成功的可能性。