| Smart energy will promote the deep fission of the energy revolution,and the establishment of a corresponding energy supply system according to energy demand is the basis of the smart energy microgrid.Smart energy is the development direction of smart grid and an important development direction of aerospace energy.The establishment of a distributed system close to the user end is a main construction method of smart energy,which can not only effectively realize the cascade utilization of energy,but also improve the efficiency of energy utilization.The optimal scheduling of microsources is an effective method to reduce the operating cost of energy systems,and is of great significance to the stable and efficient operation of smart energy micro-grids.Data attacks in smart energy microgrids may cause the microgrid control center to make wrong judgments,which in turn may cause serious losses to the grid.Compared with traditional attack methods,false data injection attacks are more deceptive and purposeful,and are difficult to be detected and defended by the system.Therefore,combined with relevant network security and communication security technologies,the possible data vulnerabilities in the system are detected and studied.Algorithms and strategies to resist false data injection attacks are particularly important.In order to verify the importance of attack detection and study the economic losses caused by attacks occurring at different moments in the process of optimizing configuration,thesis introduces virtual data injection attacks into smart energy systems containing wind power and photovoltaic generators.First,build a smart energy microgrid system based on edge computing,establish distributed energy sources such as wind power generation,photovoltaic power generation,and micro diesel engines,and fill the energy gap through energy storage equipment and interaction with the main grid.According to the characteristics of energy,a corresponding mathematical model is established,and the optimal dispatching model with the lowest total system operation cost is established with the constraints of various distributed energy power balance,rated power limit,and self-power supply capacity indicators in the system.Secondly,simulate a smart energy microgrid system in a certain area,formulate operation control scheduling strategies according to its load usage and power consumption characteristics,as well as the output of various distributed energy sources,and combine prediction algorithms and optimization algorithms to control the system.Each part of the system is scheduled before the day,and the optimal configuration of each distributed energy source and the optimal system cost are obtained.Finally,by injecting false data injection attacks at different times in the process of scheduling the day before,the economic losses caused by false data injection attacks under different prediction and optimization algorithms are simulated and compared,and the optimal algorithm combination strategy is proposed.The actual built smart energy system in the edge computing environment verifies the economics of energy optimal scheduling and the importance of attack detection. |
