Research On Dynamic Aggregation Decision Of Demand Side Energy Resources In Urban Power Grid And Its Information System

At present,the problems of energy and environment are becoming increasingly severe.The distributed generation resources such as wind power and PV have the advantages of short construction period,less investment,close to the load center,easy to eliminate,and flexible control,and have been developed rapidly.With the development of smart grid and electricity market,the resources of distributed energy storage,electric vehicle and demand side response load increase gradually.In this paper,the resources on the load side of the power grid with certain regulation and responsiveness are collectively referred to as demand side energy resources.These demand side energy resources have different characteristics,different scale and distribution.How to fully excavate these resources and utilize them efficiently is of great significance to the economic security and stability of the power grid,especially in the urban power grid with high load density and big difference between peak and valley.Aiming at the problem of efficient utilization of DSER in urban power grid,a comprehensive set of decision-making system for the assessment,planning,comprehensive development and utilization of DSER is set up based on multiple time scale,comprehensive application of comprehensive evaluation,optimization theory,prediction theory and investment portfolio theory.The system focuses on solving the problems of DSER resources evaluation and planning,DSER's comprehensive utilization of energy efficiency and the efficiency guarantee of DSER operators,so as to provide a scientific theory and method basis for the management and decision of the demand side energy resources.The main research work of this paper is as follows:(1)To accurately assess the status and development potential of demand side energy resources,a demand side energy resource evaluation model based on GIS is proposed.Focusing on the five demand side resources of wind power,photovoltaic,electric vehicle,energy storage and flexibly load,the basic index of the benefit and potential of resources development and the characteristics of resources and cost characteristics are put forward on the basis of the analysis of the characteristics of various resources.Then the analytic hierarchy process(AHP)and entropy weight method are used to determine the weight of each index under the basic index and the characteristic index.Finally,according to the weight and quantized value of each index,the comprehensive score of demand side resources in this area is obtained.Through an example,it is proved that the evaluation model can effectively evaluate the demand side resources on the urban grid block,and provide support for the development planning of urban demand side resources.(2)Aiming at the problem of efficient utilization of DSER resources,an optimization aggregation model of virtual power plant based on geographical decision partitioning,in which the complementary characteristics of demand side resources are fully considered,is proposed.First,on the basis of the evaluation results of block DSER,the detailed division criterion of virtual power plant decision area is put forward,and the decision variables of various resources in the representative area are determined.Then,in order to obtain virtual power plant with low daily average cost,good load characteristics and high degree of polymerization,a multi-objective virtual power plant optimization aggregation model is established,and the improved bat algorithm based on priority degree is applied to solve the model.Finally,the correctness and efficiency of the model are verified by an example.(3)In view of the uncertainty of demand side resources,a short-term prediction model of demand side energy resources is proposed,including short-term forecasting of wind power and photovoltaic power and short-term forecasting of electricity price,which will provide support for the optimized operation decision of DSER.First,wavelet neural network algorithm is applied to predict wind power and photovoltaic output.Combined with error analysis,wind power error probability density function based on similarity of wind is established,and PV error probability density function based on season and weather type is established.Then the probability density function is sampled,and the sampling value of the error is obtained.The error of each prediction point fitting value is obtained by volatility analysis.The prediction accuracy is improved by the superposition of the predicted value and the fitting value.The main idea of the electricity price prediction is to decompose the original data of the electricity price curve into multiple sequences with different details through the wavelet decomposition.Then,the ARIMA prediction is carried out for different sequences.Finally,the final prediction results are obtained by reconstructing.(4)Aiming at the profit demand of DSER operators,a dynamic optimization operation decision model of demand side energy resources is put forward.First,the profit model is analyzed for the virtual power plant which is optimized by the wind power,photovoltaic,energy storage and other resources,and the dynamic optimization operation model of the virtual power plant in the day market and the auxiliary service market is established.And the example proves that the model can effectively improve the profit of the operator by rationally distributing the output of the virtual power plant.Then,based on the DSER resources which are not involved in the virtual power plant,taking the photovoltaic power station and energy storage power station as the research object,the dynamic joint operation decision model of PV power station and energy storage power station is established.According to the change of external conditions,whether to implement joint operation is dynamically decided so as to achieve the optimal joint operation strategy of increasing the benefits of PV power station and energy storage system.(5)The demand side energy resource evaluation and operation decision support system design scheme based on the driving model is put forward.Firstly,the functional requirement of four subsystems including DSER evaluation system,DSER optimal polymerization system,DSER short-term forecasting system and DSER optimizing operation strategye system are designed.Secondly,the analysis and design of system requirements,the composition and function of the system structure,the design of system interface and result display are elaborated.Finally,combined with the construction plan of DSER evaluation and operation decision support system,a case is developed and analyzed.