Power System Reliability Assessment With Large Scale Renewable Energy

With the decrease of fossil energy,the global power demand is difficult to meet.How to further use renewable energy to contribute to the power system has become the focus of research.Due to the fluctuation and randomness of their own output,wind energy and photovoltaic energy will cause greater disturbance when they are incorporated into the grid,so it is particularly important to evaluate the reliability of the power system connected to renewable energy.In this paper,the reliability of the system after wind and photovoltaic energy are connected to the power grid is studied.In this study,the output power characteristic model of the power system with renewable energy is established,which lays the foundation for evaluating the reliability of the power system.Firstly,the wind speed of wind farm is predicted by time series method.In order to solve the problem of low accuracy and large deviation in the prediction process,this paper proposes to use Elman neural network algorithm to optimize the time series method,i.e.Arima Elman combined prediction model,which effectively improves the prediction accuracy of wind speed;at the same time,considering the particularity of wind speed in the wind farm,considering the wake effect and outage effect of wind speed,the power output model of the wind farm is further established.For the photovoltaic power station,the solar irradiance is obtained by calculating the solar terrestrial astronomical relationship,the effective irradiance received on the ground is decomposed,and the randomness of light intensity change is expressed by the clear sky index.Further,the photovoltaic power generation model is established,and the time series model of photovoltaic power output is obtained according to the energy conversion characteristics of photovoltaic panels.Secondly,aiming at the low efficiency of the traditional Monte Carlo simulation method in the system state sampling,this paper proposes to use the important sampling method to sample the system operation state,and select the appropriate important probability function to calculate the optimized system state probability distribution.After the wind farm is connected to the international reliability test system(IEEE-RTS),the improved Monte Carlo simulation method of importance sampling method is used to evaluate the reliability of the system.Through the simulation experiment,the reliability index of the system at this time is analyzed,which proves that the improved evaluation algorithm is more efficient.Finally,the power system reliability evaluation index system including renewable energy is established,and the evaluation process is clarified.Wind energy and photovoltaic energy are added to the reliability evaluation system of the original system,and the system reliability changes after connecting different capacity wind energy,photovoltaic energy and hybrid energy through different nodes are analyzed.Based on the experimental simulation of a regional power grid,the simulation results under different conditions are analyzed,and the influence of renewable energy on the system reliability is obtained.At the same time,the method to identify the relatively weak links of the system is proposed,and the corresponding improvement measures are proposed for the weak areas of the power system.