Day-ahead Optimal Scheduling Strategy Of Electricity-heat Integrated Energy System For New Energy Accommodation

China's northeast,north and northwest regions have a single power supply structure,most of which are dominated by thermal power units.At the same time,a large amount of wind power is connected to the grid,making it difficult to adjust the peak power grid.The thermal power generating unit is constrained by the "power subject to heat" operating conditions,lacking other flexible energy-regulating equipment,and wind power has intermittent,random and peak-valley characteristics opposite to the electrical load.These factors make it difficult to adjust the peak wind power peak period The phenomenon is more serious.Taking into account the dynamic characteristics of heat energy transmission in the heat network and the heat-storage device's heat-storage device,the electricity-heat Integrated energy system couples the power system and the heating system together to form a multi-dimensional energy interconnected energy form.Among them,it is of great significance to the constraint of decoupling thermal power units to"power subject to heat" and promote the efficient development of clean energy.At first,electrical energy and thermal energy,as the main energy output of the electricity-heat Integrated energy system,are affected by various factors and present a certain degree of volatility and randomness.In order to analyze the changes in the electric heating load and accurately predict the electric heating load,this paper proposes a Electric heating load forecast based on GA-BP neural network algorithm.First of all,considering the fluctuation of the data of various influencing factors and the difficulty of obtaining data,the input and output variables of the electric heating load are determined.On this basis,the BP neural network model is constructed and the composition and design of the genetic algorithm are described.Through simulation examples,the traditional The electric heating load forecast of BP neural network algorithm is compared and analyzed to verify the accuracy and credibility of the GA-BP neural network algorithm for forecasting electric heating load.Secondly,on the basis of realizing the electric heating load forecast based on GA-BP neural network algorithm,the typical structure of the heating system is introduced,and the heating network model is established on the basis of analyzing the actual heating network structure and operating characteristics.It focuses on the analysis of the node method to describe the dynamic process of thermal energy transmission in the heating pipe network,and establishes a thermal energy transmission dynamic model that takes into account thermal transmission delay and thermal energy loss.According to the heat network model,modeled on the power system power flow calculation,an example is set to simulate the typical pipe outlet temperature of the heating primary network and the return water temperature of the first heat exchange station.Comparing the predicted heat load values proves the correctness and feasibility of building a dynamic model of thermal energy transmission,and analyzes and studies the thermal energy transfer characteristics and heat storage can make thermal energy transfer across time periods.Finally,using the heat network as a schedulable resource,four types of optimal scheduling schemes for the electricity-heat Integrated energy system that take into account the dynamic characteristics of heat energy transmission in the heat network and the heat storage of the heat storage device are proposed to realize the scheduling and utilization of the virtual energy storage of the heat network.Among them,through the optimal allocation of energy equipment output,the total operating cost is the lowest and the wind curtailment penalty is added as the objective function.The actual electricity-heat Integrated energy system is used as an example to analyze the calculation example,compare the predicted value of the electric heating load and the predicted amount of wind power based on the GA-BP neural network algorithm,and analyze and calculate the wind power brought by different optimized dispatching schemes by changing the output of each energy equipment Results of consumption rate and economy.