Planning Research For Grid-connection Of Distributed Generations Considering The Sequential Feature And Correlation In Active Distribution Network

With the increasing shortage of energy and environmental pollution,intermittent power generation such as wind power and solar energy has developed rapidly,distributed generation technology will become the main form of intermittent energy.In order to effectively solve the large-scale grid-connected consumption problem of distributed power,the active distribution network will become a major development form of the future intelligent distribution network,which has the characteristics of high permeability and high control requirements.However,intermittent distributed power output power is easily affected by the external environment,with obvious randomness and timing,and there is a correlation between distributed power output,distributed power supply and load output,and its large-scale grid connection will affect the planning and operation of active distribution networks.At the same time,since the distributed power supply and demand response load are important interactive resources in the active distribution network mode,if the distributed generation and load sequence and correlation are taken into account,the demand response mechanism can be incorporated into the distributed generation grid-connected planning process.Considering the time-series characteristics of the load demand variation in the demand response mechanism,the collaborative planning and management of the two can be performed.Optimize the load power mode and improve the active distribution network operation level.Firstly,establish a probabilistic model of distributed power supply and load output and introduce random variables.For the characteristics of correlation between distributed power sources,distributed power supply and load,and the characteristics of temporal distribution of probability distribution parameters,this paper uses the principle of equi-probability conversion and Cholesky decomposition to divide the time period and according to the specific probability distribution parameters in each time period.Combined with the improved correlation matrix method and Latin hypercube sampling technique,a time-correlated sample matrix of distributed power and load output in 24 periods is obtained.Secondly,a long and short term planning-running two-layer chance constrained optimization model based on time-series and correlation in active distribution network is established for grid connection of distributed power.The lower-level short-term model takes into account the optimization of active distribution network operations during each time period to determine the optimal active management strategy,the upper-level long-term planning is based on short-term optimization at the lower level,and considers that the overall economic cost of the active distribution network is optimal in the entire planning stage to determine the distribution power supply installation location and capacity.On this basis,the above model was solved by hybrid intelligent algorithm combining probability probabilistic power flow method with nested simulated annealing particle swarm algorithm,and the simulation results verify the rationality of the proposed method.Finally,Considering the distributed generation? load output correlation and timing,taking into account the timing characteristics of load demand changes in the demand response mechanism,a dual-layer chance constrained optimization model for joint planning of distributed power supply and demand-side response is proposed.The lower layer considers the optimization of the demand-side response of the active distribution network to determine the price of peak-to-peak level for each period of time,the upper layer plans to optimize the underlying layer and considers that the active distribution network has the largest net return to determine the installation location and capacity of the distributed power supply.Finally,a stochastic simulation technique was combined with the nested SAPSO hybrid intelligence algorithm to solve the above model,and an example was analyzed.