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Electric Fleets with On-site Renewable Energy Sources (EFORES): Data-driven Dynamic Dispatching and Charging under Uncertainties

现场可再生能源电动车队（EFORES）：不确定性下数据驱动的动态调度和充电

基本信息

批准号：
EP/W028727/1
负责人：
Xuewu Dai
金额：
$ 6.42万
依托单位：
Northumbria University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FW028727%2F1
关键词：
Electric Fleets site Renewable Energy

项目摘要

In line with the UK's target to reach net zero by 2050, Electrical Vehicles (EV) charged by renewable energy are one of the solutions towards carbon-neutral road transport, which is the 2nd largest carbon emission both nationally in the UK and locally in Newcastle city (it contributed about 33% of total emission in 2020). The electrification of business fleets (either commercial or for public service) has recently emerged as one the key factors in reducing transportation related CO2 emissions. However, according to the Global Covenant of Mayors's (GCoM) guidance the electrification of fleets leads to the reduction of direction emission, it does not imply reduction of overall emission nationally or globally if the electricity charged for EV is still sourced from the fossil fuels (see also the NU et al.'s recent policy report: https://www.seev4-city.eu/wp-content/uploads/2020/09/SEEV4-City-Policy-Recommendations-and-Roadmap-1.pdf).A recent trend in Renewable Energy Sources (RES) is an increasing amount of small-scale RES installed on-site , referred to as ORES. For instance, in March 2021, Newcastle City Council announced a £27M plan to install solar panels, energy storage etc. at schools, leisure centres, cultural venues, depots and offices to decarbonise public buildings and transport. Likewise, Gateshead Council has approved in Nov. 2020 plans to develop two significant-scale urban solar farms, and furthermore installing solar PV canopies above car parking bays in sites like Gateshead Civic Centre, and furthermore are including rooftop solar PV on new developments such as the Gateshead Quay Arena and the proposed Gateshead Quays multi-storey carpark (construction of both commencing in 2021). This provides good opportunities for EV to use more on-site generation renewable electricity to actually reduce the overall emission for road transport. The key issue is the efficient use of ORES.Using battery as a electricity storage can alleviate this, but at significant investment and operation cost. V2G is proposed to reduce static battery storage, but causes battery degradation. And smart charging is needed to avoid or reduce the operation cost of battery degradation. Most existing EV smart charging studies focus on the EV charging only to reduce charging cost and/or peak-shaving, under the assumption of EVs' electracy demand are given and non-adjustable (either constant or statistical model, e.g. Poisson distribution). This is reasonable for non-collaborative individual EVs. However, for a electric fleet (EF) consisting of collaborative EVs, in addition to the optimal EV charging, the electricity demand can be optimized by EF dispatching, i.e. adjusting EF's travel plan by assigning the right EV to the right service to maintain the right state of charge of the battery, and allocating to the right charging station at a right time window, such that a better marginal benefits can be achieved in terms of better efficiency and utilization of on-site renewable energy. However, the power generation of ORES is highly variable - resulting in an undesired fluctuation at the supply side. On the demand side, EVs' charging demand also comes with uncertainties, to meet various tasks with dynamic travelling and charging demands. In shifting EV energy from less variable fossil electricity (imported from the grid) to high variable on-site ORES, the main challenge is the charging strategy of maximizing self-consumption of own ORES under uncertainties, whilst meeting the variable EV demands, at minimized cost in energy storage and less impact on grid's peak load. This project is to investigate the possibility to intelligently integrate the dynamic charging demand of electric fleets with the high variable on-site renewable energy by developing a data-driven reinforcement learning (RL) decision support tool.

根据英国到2050年实现净零排放的目标，可再生能源充电的电动汽车（EV）是实现碳中和道路运输的解决方案之一，这是英国全国和纽卡斯尔市当地的第二大碳排放量（2020年占总排放量约33%）。商业车队（无论是商业还是公共服务）的电气化最近已成为减少运输相关二氧化碳排放的关键因素之一。然而，根据全球市长盟约（GCoM）的指导，车队的电气化导致方向排放的减少，如果EV充电的电力仍然来源于化石燃料，则这并不意味着国家或全球总体排放的减少（也参见NU et al.最近的政策报告：https：//www.seev4-city.eu/wp-content/uploads/2020/09/SEEV4-City-Policy-Recommendations-and-Roadmap-1.pdf）。可再生能源（RES）的最近趋势是现场安装的小规模RES（称为ORES）数量不断增加。例如，2021年3月，纽卡斯尔市理事会宣布了一项2700万英镑的计划，在学校、休闲中心、文化场所、仓库和办公室安装太阳能电池板、储能等，以实现公共建筑和交通的脱碳。同样，Gateshead理事会已于2020年11月批准计划开发两个大规模的城市太阳能发电场，并在Gateshead Civic Centre等地点的停车场上方安装太阳能光伏檐篷，此外还将在Gateshead Quay竞技场和拟议的Gateshead Quays多层停车场等新开发项目上安装屋顶太阳能光伏（两者均于2021年开始建设）。这为电动汽车提供了良好的机会，可以使用更多的现场发电可再生电力，以实际减少道路运输的整体排放。关键问题是如何有效利用ORES。使用电池作为电力储存可以缓解这一问题，但投资和运营成本很高。V2 G旨在减少静态电池存储，但会导致电池退化。并且需要智能充电来避免或减少电池退化的操作成本。现有的电动汽车智能充电研究大多只关注电动汽车充电以降低充电成本和/或调峰，假设电动汽车的电量需求是给定的且不可调节的（恒定或统计模型，例如泊松分布）。这对于非协作的个体EV是合理的。然而，对于由协作EV组成的电动车队（EF），除了最优EV充电之外，还可以通过EF调度来优化电力需求，即通过将正确的EV分配给正确的服务以保持电池的正确荷电状态来调整EF的出行计划，并在正确的时间窗口分配给正确的充电站，这样就可以在现场可再生能源的更好效率和利用方面实现更好的边际效益。然而，ORES的发电量是高度可变的-导致供应侧的不期望的波动。在需求方面，电动汽车的充电需求也具有不确定性，以满足各种具有动态出行和充电需求的任务。在将电动汽车能源从变化较小的化石电力（从电网进口）转移到变化较大的现场ORES时，主要挑战是在不确定性下最大化自身ORES的自消耗的充电策略，同时满足可变的电动汽车需求，以最小的能量存储成本和对电网峰值负荷的影响较小。该项目旨在通过开发数据驱动的强化学习（RL）决策支持工具，研究将电动车队的动态充电需求与高可变现场可再生能源智能整合的可能性。