Reliability Analysis Of Cyber-Physical Power System In V2G Mode

The rapid development of economy has led to the rapid growth of power load,which is followed by worsening energy situation and increasingly prominent environmental problems.The intelligentialize of power grid has become a trend.The key point is to combine the cutting-edge computing,communication and sensing technologies with the power system to promote grid interconnection and optimal scheduling of new loads such as electric vehicles and intermittent renewable energy.Cyber-physical power system is extended from the cyber-physical system(CPS),which is applied to the energy field.CPS is a next-generation embedded system.Its core is the close integration of information system and physical system,and has implanted some non functional characteristics in system design,such as isomerism,autonomy,real-time and so on.The network technology of the cyber-physical power system is different from the existing communication network,which requires a higher level of real-time and reliability.The increasing scope of information collection aggravates the burden of communication network in cyber-physical power system.The problems such as communication delay,data packet loss and network congestion during the transmission process will affect the real-time monitoring and control of power system.Therefore,the reliability of the communication network of the information system must be analyzed while considering the fusion of the information system and the power system.We introduce the cyber-physical power system briefly at first,and then considers the impact of electric vehicle to grid(V2G)technology on it,and summarizes the interactive mode of information system and power system.Then we analyze the technology of the V2 G and the structure of cyber-physical power system communication in V2 G mode.We use genetic algorithm to construct a multi-objective optimization mathematical model of communication network and the expression of satisfaction function of cost and reliability of multi-objective optimization function is given synthetically.Finally,based on the previous research results,we analyze the specific implementation process based on genetic algorithm guided by multi-objective optimization design principles and methods,and provide reference for further work.