| Along with the development of "3060” carbon peaking and carbon neutrality goals,flexible resources such as photovoltaic power,wind power,energy storage systems,flexible load,and electric vehicles will also develop rapidly.Due to the special operation mode of dispatchable flexible resources,their response to TOU price is different from that of general loads.With the continuous increase of the proportion of dispatchable flexible resources in the power grid,TOU price obtained by the traditional optimization method of TOU price will no longer be the optimal solution,and even play the opposite role.At the same time,the dispatchable flexible resources represented by photovoltaic and energy storage systems transform the traditional distribution network into an active distribution network.At the same time,the dual characteristic of "source-load" of the energy storage systems also puts forward new requirements for the planning of distribution network.Based on the problems raised above,the main contents of this paper are as follows:(1)The basic model of dispatchable flexible resources and the generation of typical curves.Firstly,photovoltaic,wind power,energy storage,and flexible load are modelled successively.Then,the typical load and output curves of photovoltaic and wind power are generated based on the K-means cluster algorithm.Finally,based on the probability distribution function of the daily driving range and return time of electric vehicles,the sampling simulation of electric vehicles is carried out one by one,and the load power curve of electric vehicles is obtained by superposition.(2)Study on TOU Optimization with the consideration of dispatchable flexible resources.Firstly,the typical load curve of the power grid is combined with the typical output curve of photovoltaic power and wind power to obtain the comprehensive load curve,and the TOU period is divided according to the comprehensive load curve of the power grid.Secondly,the response model of EV charging behavior under TOU is established based on Monte Carlo simulation,and the influence of the charging and discharging strategy of energy storage stations on TOU optimization is explored.Finally,the minimization of the peak-valley load difference and the minimization of the maximum load of the grid are taken as the objective function,and the particle swarm intelligent algorithm is used to solve the problem.The results show that the proposed optimization method can correctly reflect the real load of the power grid with dispatchable flexible resources,improve the rationality of time division and correctly guide the load to carry out demand-side response so as to minimize the peak-valley difference and peak load of the power grid.(3)Research on Distribution Network Planning with Dispatchable flexible resources.Firstly,the power curves of various flexible resources and load curves are combined to obtain the equivalent load curve,and the nodes containing flexible resources are equivalent to aggregate nodes externally.Secondly,the influence of dispatchable flexible resources is considered in the substation capacity and site selection planning.The substation constant capacity formula and the substation load distance formula are optimized and updated based on the substation capacity and site selection planning.The influence of dispatchable flexible resources on grid planning is considered by adding corresponding constraints to grid planning,and a method of grid cross-discrimination based on binary function is proposed.Then the influence of dispatchable flexible resources and TOU on grid planning is explored.Finally,a planning model with minimum network investment and operation cost as the objective function is established,and a 35 k V network planning example is analyzed and verified.The results show that the proposed method can reasonably plan the distribution network containing dispatchable flexible resources,and the response behavior of dispatchable flexible resources to TOU price is included in the distribution network planning stage,which can only improve the rationality and science of the distribution network planning scheme. |