Research On Source-storage Optimized Planning For Distribution Network Under The Scenario Of Diversified Electric Source-load Spatiotemporal Correlations

In recent years,Distributed Generator(DG)has developed from marginal to a mainstream factor in the power grid.However,the randomness and correlation of intermittent DG make the supply side complex and changeable.China’s distribution network must provide quantitative and qualitative power demand to achieve the satisfaction of demand side users.Meanwhile,the continuous improvement of DG penetration rate and the significant advancement of energy storage system technology both assume the functional role of multiple drivers under the evolution of the distribution network.Both the source-storage interaction and the spatiotemporal correlation of renewable output have brought new thinking and new challenges to the coordination and planning of the distribution network.Because traditional fossil energy mostly follows the bottom-up planning mode,and can not be copied on the renewables side where the constituent elements and operation structure are constantly changing,new theories and methods need to be proposed in the planning and operation links.This thesis has carried out the following work:(1)Aiming at the shortcomings of the existing probabilistic load flow calculation methods that do not consider the prior distribution of state variables,based on the thinking of Bayesian theory,a new method based on improved approximate bayesian calculation is designed from the prior information of state variables.While simulating observation samples of state variables and processing the correlation of random variables,the calculation of digital features such as the probability density of output random variables is completed.Furthermore,the probabilistic load flow calculation problem is described as the uncertainty problem of observation samples and simulation samples,which can be applied to the system analysis and evaluation of highpermeability DG connected to the grid.(2)Research of the spatiotemporal correlation for wind-photovoltaic-load.Starting from the perspective of multi-dimensional data mining,a new method of Bayesian network structure learning is proposed.Then through the complex evolution of multi-scenario technology,the correlation characteristics of wind power,photovoltaic output and load demand are mapped.Finally,the MD-K2 bayesian network model is calculated to verify its structural accuracy and the rationality of wind-photovoltaic-load joint scene.(3)Research of the DG multi-objective planning model considering the spatiotemporal correlation of wind-photovoltaic-load.The optimal DG configuration depends on the economics of investment and operation,and the risks associated with the system operation.Considering the uncertainty of renewables output and load fluctuation,combined with the collaborative implementation of various active management measures to achieve the goal of maximum annual return and minimum operation risk.The optimal configuration of DG distribution and capacity is determined,which provides a new solution for high permeability DG access to the grid.(4)Research of the joint planning model for DG and generalized energy storage considering the spatiotemporal correlation of wind-photovoltaic-load.From the three perspectives of power supply,load,and energy storage,the DG configuration and generalized energy storage double-layer optimization planning method in the multi-energy complementary power generation system is constructed.The upper level considers the comprehensive cost,DG carrying capacity and the system’s comprehensive operating risks,and the lower level considers the operating cost to balance the charge and discharge power of each energy storage system.The collaborative optimization of DG and ges is realized under the multi data drive,which can effectively deal with the problem of how to closely couple the operation and planning,and formulate a joint planning scheme within the safe operation boundary of the system.