Virtual Energy Storage Modeling And Collaborative Optimization Control Method For Electric Vehicle

In recent years,China’s energy revolution is in the strategic transformation period of accelerating the construction of a clean,low-carbon,safe and efficient energy system.The proposal of V2 G technology can give full play to the role of mobile energy storage of electric vehicles,and use the energy storage of large-scale electric vehicles to interact with the power grid for energy and information,which provides a new solution to alleviate the energy pressure.Therefore,to deeply tap the potential of virtual energy storage of electric vehicles to participate in the regulation and operation of power grid and determine a reasonable and effective optimal control strategy of virtual energy storage of electric vehicles plays an important role in meeting the needs of multi-type regulation of power grid and ensuring the stability of power grid operation.The main work of this paper is as follows:(1)In order to deeply tap the schedulable potential of virtual energy storage of electric vehicles in different parking scenarios,an aggregation model of virtual energy storage of electric vehicles based on travel chain is constructed.Based on the travel chain theory,the 2017 national family travel data(NHTS)is used to fit various parameters required by the travel chain model.Considering the closed-loop and non closed-loop behavior characteristics,the travel chain model is established and the effectiveness of the model is verified;The differences between conventional energy storage and virtual energy storage of electric vehicles are analyzed.Based on the travel chain,the virtual energy storage aggregation model of electric vehicles is established,and the virtual energy storage capacity,external charge and discharge power boundary and charge state of energy storage of cluster electric vehicles under different charging scenarios at different times are evaluated.This model is the basis of the subsequent in-depth study of the optimal control strategy of electric vehicles.(2)In order to give full play to the scheduling potential of virtual energy storage of electric vehicles,a collaborative optimization decomposition model of virtual energy storage scheduling command objectives of electric vehicles is proposed.Based on the hierarchical energy management method,a hierarchical architecture of "grid layer operator layer response control layer" cluster electric vehicle virtual energy storage participating in regional power grid dispatching is established,and the functions of each level and the operation principle and cooperation mode of the whole hierarchical architecture are analyzed.Based on the layered architecture,the operator level optimization model is established with the goal of minimizing the response deviation of power grid dispatching command and optimizing the economy.In order to ensure the travel demand of users and respond to the guidance power command,the response control layer energy management control strategy is proposed.The example results show the advantages of the virtual energy storage aggregation model of electric vehicle and the effectiveness of the proposed optimization model and energy management control strategy.(3)In order to reduce the power deviation that the actual power can not meet the dispatching power command due to the randomness of electric vehicle travel during power grid dispatching,a control strategy for the execution deviation of electric vehicle virtual energy storage dispatching command is proposed.Firstly,according to the actual number of electric vehicles on the network and corresponding parameters,a virtual energy storage deviation management and control capability model of electric vehicles is established to evaluate the management and control capability of virtual energy storage of local cluster electric vehicles to the deviated power;Then,in order to give full play to the virtual energy storage deviation control ability of local electric vehicles,a local deviation control strategy is proposed;Finally,aiming at the deviation that still exists after the implementation of the local deviation control strategy,a cross cluster collaborative deviation control strategy is proposed to give full play to the virtual energy storage deviation control ability of electric vehicles in other clusters and further reduce the local power deviation.The example results show that the proposed scheduling instruction execution deviation control strategy is effective in reducing the deviation from the scheduling power instruction and improving the operation economy.