Research On Energy Economic Scheduling Of Combined Heat And Power Microgrid Based On Source-load Probability Prediction

There are a lot of wind and solar energy in northeast,northern and northwestern China,Wind power generation and photovoltaic power generation have the advantages of green environmental protection,non-pollution,and the conflict between electric heating during winter heating period,leading to energy utilization and scenery level Lower.Thermoelectric network micro-network integrated energy system is one of the effective ways to solve energy utilization efficiency and difficulty in renewable energy.By coordinating various units to optimize the system,the system operating cost can be reduced to a certain extent,and the renewable energy power generation is enhanced.However,solar and wind energy have strong random volatility,and due to the diversity of the load,the randomness of the load is further increased,and the difficulty of scheduling decision is increased.(1)Introduction and modeling of combined heat and power microgrid.In view of the modeling problem of combined heat and powe microgrid system,this paper establishes a comprehensive hybrid logic dynamic model of the system.According to the fuel consumption,maintenance and energy interaction with the main power grid,the daily operation cost function is established,and the economic optimization is carried out with the goal of minimizing the function value.The operation mode and optimal scheduling of combined heat and powe microgrid system are classified and summarized.It is determined that the operation of cogeneration microgrid system in this paper adopts the top-level cycle mode,and the optimal scheduling is only for active power.(2)Probability prediction analysis of renewable energy and load multivariate parameters.Aiming at the problem that the existing source load probability modeling needs to assume parameter distribution and can not fully consider the influence of various random factors,this paper proposes a probability prediction model of renewable energy output power and load based on nonparametric kernel density estimation algorithm,and gives an optimal bandwidth selection model that does not depend on the overall real distribution.Aiming at the serious skew characteristics of wind power density,a nonparametric kernel density estimation method based on logarithmic transformation is proposed,which improves the quality of wind power probability prediction results,and gives a comprehensive test index to verify the effectiveness of the proposed method.(3)Energy economic schedule based on model predictive control.In view of the energy economic scheduling problem,this paper constructs a model predictive control(MPC)algorithm based on if-then-else rule,and uses a two-stage optimal scheduling model to schedule the energy economy of the system.The first stage is the day ahead economic optimal scheduling,and the second stage is the real-time optimal scheduling in the prediction time domain,so that the real-time optimal control variables are as close as possible to the day ahead planned value,The short-sighted effect in the process of real-time optimization is effectively solved.In view of the high computational complexity and long computational time of mixed integer linear programming,this paper proposes to use if-then-else rule to assign the binary decision variables in the comprehensive mixed logic dynamic model of CHP microgrid system based on the renewable energy and load prediction results in the optimization process,and transform the mixed integer linear programming problem(MILP)into linear programming(LP).Finally,through simulation verification and comparative analysis,compared with before using if-then-else rule,this method can effectively reduce the computational complexity,significantly reduce the computational time with almost no performance loss,and the more the number of binary logic variables,the more the computational time is saved.