Multi-Domain Modeling and Optimization of Integrated Renewable Energy and Urban Electric Vehicle Systems

• Applicants: Professor Dr.-Ing. Dietmar Göhlich; Professor Dr. Volker Markl; Professor Dr.-Ing. Kai Strunz
• Subject Area: Electrical Energy Systems, Power Management, Power Electronics, Electrical Machines and Drives ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 410830482

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.

• DFG Programme: Research Grants
• International Connection: China
• Partner Organisation: National Natural Science Foundation of China
• Cooperation Partners: Professor Dr. Zechun Hu; Professor Dr. Yuan Zou