Multi-Domain Modeling and Optimization of Integrated Renewable Energy and Urban Electric Vehicle Systems
集成可再生能源和城市电动汽车系统的多域建模和优化
基本信息
- 批准号:410830482
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2019
- 资助国家:德国
- 起止时间:2018-12-31 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Germany and China, as well as other countries, have set ambitious objectives for strongly increasing the number of electric vehicles (EVs) and the share of renewable energies in the electric power supply over the next years. Without the development of strategies for a coordinated sustainable power system integration of renewables and EVs, this rapid growth is set to cause serious stress to the infrastructure – as already experienced today during peak hours of power demand. The proposed project addresses the necessary integration stages based on a multi-domain methodology comprising big data based analysis and the modelling and optimization of integrated renewable energy and electric vehicle fleets. Inner-city reference districts of Chinese and German cities serve as benchmarks.The majority of relevant scientific references describe the driver behaviour by means of stochastic models, travel surveys or simulation data. In contrast, in this work real-world recorded data of the movement and the charging process for several thousand EVs in Beijing are used. This is possible as information obtained from the Big Data Monitor Center of the Chinese MIIT is being integrated. Extensive data for buses and urban commercial vehicles are evaluated as well. A comprehensive analysis of such a large data set of EVs has not been carried out so far and the development of suitable big data methods is necessary, applying parallel processing and machine learning on heterogeneous hardware.Energy consumptions records will be classified into several patterns to develop a transport and energy demand prediction model for an urban reference district. This includes the topology, different vehicle types, and their travel behaviour. Based on this analysis, load profiles can be generated to provide a foundation for modelling the grid integration of EV fleets. Power system reference models for different charging patterns will be developed, power quality issues will be analysed based on these reference models and mitigation solutions will be derived. The energy market integration of EV fleets is addressed by the creation of an energy management system. So-called “nanogrids” are investigated as solution for locations where the electric network is weak and prone to heavy voltage fluctuations. The performance of EVs is highly determined by its batteries and battery degradation has a serious impact on the vehicle range and life cycle. The latter is also affected when vehicle-to-grid technology is applied. Therefore, the influence of battery aging and health on the optimization of EV fleet operation and integration will be examined.In a concluding stage of research an integrated multi-domain model is derived which facilitates an optimized charging management and overall cost minimization considering relevant short- and long-term effects. Recommendations for different operating modes to maximize the use of renewable energy or to minimize CO2 emissions will be given.
德国和中国以及其他国家都制定了雄心勃勃的目标,要在未来几年大力增加电动汽车(EVS)的数量和可再生能源在电力供应中的份额。如果不制定可再生能源和电动汽车协调可持续电力系统集成的战略,这种快速增长势必会给基础设施带来严重压力--就像今天电力需求高峰期已经经历的那样。拟议的项目涉及以多领域方法为基础的必要整合阶段,包括基于大数据的分析以及综合可再生能源和电动汽车车队的建模和优化。以中德两国城市内参考区为基准,相关的科学参考文献大多通过随机模型、出行调查或模拟数据来描述驾驶员的行为。相比之下,在这项工作中,使用了北京数千辆电动汽车的运动和充电过程的真实记录数据。这是可能的,因为从中国工信部大数据监测中心获得的信息正在被整合。对公交车和城市商用车的大量数据也进行了评估。到目前为止,还没有对如此庞大的电动汽车数据集进行全面的分析,需要开发合适的大数据方法,在不同的硬件上应用并行处理和机器学习。能源消耗记录将被归类为几种模式,以建立城市参考区的交通和能源需求预测模型。这包括拓扑结构、不同的车辆类型及其出行行为。在此分析的基础上,可以生成负荷分布,为电动汽车车队的电网一体化建模提供基础。将为不同的充电模式开发电力系统参考模型,并将基于这些参考模型分析电能质量问题,并得出缓解方案。电动汽车车队的能源市场一体化通过创建能源管理系统来解决。所谓的“纳米颗粒”被研究为解决电网薄弱和容易出现严重电压波动的地方的解决方案。电动汽车的性能在很大程度上取决于其电池,电池的退化严重影响了车辆的续航里程和生命周期。后者在应用车辆到电网技术时也会受到影响。因此,本文将研究电池老化和健康对电动汽车车队运营和集成优化的影响。在研究的总结阶段,推导出一个集成的多域模型,该模型有助于优化充电管理和考虑相关的短期和长期影响的总体成本最小化。将对不同的运行模式提出建议,以最大限度地利用可再生能源或最大限度地减少二氧化碳排放。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Professor Dr.-Ing. Dietmar Göhlich其他文献
Professor Dr.-Ing. Dietmar Göhlich的其他文献
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