Optimization Management And Benefit Analysis Of Electric Vehicles And Battery Energy Storage System

Electric vehicles (EVs) have been gaining intensive attention by governments in many countries, energy companies and power grid companies. The popularity of EVs, on one hand, could efficiently relieve the challenges arised from the energy crisis and environmental pollutions, but on the other hand may impose significant impacts on security, economics and reliability of the power system concerned. Therefore, how to elaborately design the EV charging strategy so as to lower costs of users and improve the security and economics of the power system and how to reasonably evaluate the comprehensive benefit of implementing smart charging strategies are important issues to be addressed.Given this background, some relevant topics have been preliminarily investigated and the main focuses of this thesis are summarized as follows:(1) An overview of the EV charging load modeling and smart charging management. First, the affecting factors of EV charging load modeling are elaborately analysed, and then the calculation methods of EV charging load are summarized. Subsequently, the interactions between EV charging and the distribution power system are carefully outlined and the smart charging strategies are classified in term of the optimization objective. Finally, the economic value of the EV are systematically investigated from three different aspects, namely user, power system and the society, respectively.(2) A decentralized smart charging strategy is proposed for EVs to lower the charing costs of users. First, the analysis about the charging behaviors of five types of EVs is conducted based on the survey results. Secondly, the decentralized smart charging strategy which not only takes into account the user behaviors but also considers the real-time electricity price, is presented to regulate the charging behaviors of EVs by economic incentives. Furthermore, the robust optimization model for EVs with smart charging is developed while with the uncertainties of the charge price considered, and AMPL/CPLEX, a highly efficient commercial solver, is employed to obtain the optimal charging plan. Finally, a typical Hangzhou distribution system is employed to illustrate the efficiency of the proposed model. Simulation result shows the availablility and advantage of the decentralized smart charging strategy in reducing the users’charging costs and improve the operation economics of the power system.(3) A comprehensive benefit analysis is conducted for EVs to evaluate the effect of implementing the proposed coordinated charging management. First, a coordinated EV charging optimization model is proposed with an objective of minimizing the system load variance for a given time period so as to obtain the system load profile. Subsequently, methods are developed for evaluating the potential economic benefits of coordinated EV charging management from four aspects, including no extra generation and transmission investment, reduced cost of distribution system capacity reinforcement, reduced cost of energy losses, reduced cost of purchasing ancillary services, with the free charging scenario as the datum. Meanwhile, a method for comprehensive benefit analysis of coordinated EV charging management is presented. Finally, a 54-bus sample distribution system is employed to demonstrate the proposed method, and it is shown by simulation results that remarkable economic benefits can be achieved by implementing coordinated EV charging management.(4) Two control strategies of battery energy storage system (BESS) is presented for smoothing wind power fluctuations. The mathematical model of the BESS is first introduced based on the structure and characteristic of the cogeneration system. Afterwards, an improved control strategy which takes the interaction between the current charging/discharging of BESS and the SOC (State Of Charge) constraints at next time slot into consideration is proposed so as to make the most use of the BESS capacity. Additionally, an optimal control strategy is presented with an objective of minimizing the over-limit probability of combined output power fluctuations from the cogeneration system. Finally, a cogeneration system of a large wind farm and the BESS is employed to demonstrate the proposed strategies. It is shown by simulation results that both the improved control strategy and the optimal control strategy for the BESS could effectively improve the combined output power fluctuations characteristic of the cogeneration system.Finally, the research outcomes of this thesis are summarized and possible future research topics indicated.