Research On Optimization Method For Coordinated Operation Of Smart Energy System

The purpose of the smart energy system is to realize the coupling and sharing of electricity,gas,cold and hot energy.The day-ahead optimization scheduling of the smart energy system can achieve the goals of multi-energy complementization,clean and efficient.With the large-scale grid connection of distributed energy and the application of vehicle to grid(V2G)technology,the operational cost and flexibility of the day-ahead scheduling of the smart energy system have been further optimized.Due to the instability of clean energy outputs and the income of electric vehicle owners,the day-ahead scheduling of thesmart energy system will be affected to some extent.Therefore,in the day-ahead scheduling of the smart energy system,how to minimize the impact of the fluctuation of clean energy and maximize the benefits of electric vehicle owners are the hot issues that need to be solved urgently for thesmart energy system at present.The coordinated and optimized operation method of the smart energy system taking the uncertainty of clean energy output into account is studied in this paper.The main research contents are as follows:First,Monte Carlo method is used to predict the output power of wind power and photovoltaic power of the smart energy system,and the errors between the predicted value and the actual value are explained.The framework of energy interaction and sharing between intelligent energy system,energy supply system and terminal energy users is established,and the energy interaction mode among them is illustrated.The day-ahead scheduling plan of the smart energy system is divided into three types:day-ahead electricity purchase and sale plan for power grid,day-ahead optimization plan for the operation status of internal energy conversion equipment,day-ahead scheduling plan of electric vehicles for terminal energy users.The impact of wind-wind output uncertainty on the scheduling plan and the risks it brings are analyzed.Second,risk factors are introduced into traditional master-slave game theory,and the feasibility of improving the master-slave game model is proved theoretically.With intelligent energy system as the leader and terminal energy users as the followers,the optimal scheduling model of the smart energy system based on the master-slave game model of introducing risk factors is established,and the feasible implementation strategies of the smart energy system and terminal energy users are given.Then,the Conditional Value at Risk(CVaR)theory in economics is used to quantitatively analyze the day-ahead scheduling risk cost of the smart energy system caused by the uncertainty of clean energy outputs.Taking the risk cost as a risk factor,a two-level optimal scheduling model for the smart energy system and terminal energy users based on the master-slave game model of introducing risk factors is established.The nonlinear factors of the two-level optimal scheduling model are analyzed,and the nonlinear condition linear relaxation,equivalent KKT condition and duality theory are used to transform the master-slave game model of introducing risk factors nonlinear model into linear model,and the Yalmip algorithm is used to solve the problem.Finally,through the simulation of the operation data of a smart energy system in China,the coordination and optimization control effect of the smart energy system under different scenarios is analyzed.The simulation results show that the model established in this paper can effectively reduce the risk caused by the uncertainty of clean energy outputs,achieve a win-win situation between the smart energy system and electric vehicle owners,and has good economy and feasibility.