Reliability Evaluation Of Power Generation System With High-Proportion Renewable Energy

With the rapid growth of human energy demand,renewable energy has gradually become one of the important energy sources.The deteriorating environment and the non-renewability of traditional fossil energy have promoted the continuous and rapid development of renewable energy,such as wind and solar energy.With the increase in the proportion of renewable energy,the intermittent and random nature of new energy generation forms has resulted in unstable output characteristics and uncontrollable source power,which has a negative impact on the safe and stable operation of power systems.With the increase in the scale of renewable energy access,the traditional reliability assessment models and assessment methods are no longer applicable,and the reliability assessment process becomes complicated.Therefore,it is necessary to integrate the system reliability models and Evaluation methods,and further the study.First,this paper establishes a time series output model of wind farms and photovoltaic power plants considering the correlation of different kinds of renewable energy.Affected by the geographical environment and climatic conditions,the output of adjacent wind farms and photovoltaic power plants has a strong correlation.Therefore,when large-scale wind and solar power is connected to the grid,it is necessary to use simulated data for system reliability evaluation,which contain the relevance of wind farms and photovoltaic power plants.Simulating the actual operating state of the system is conducive to improving the accuracy and practicability of reliability assessment.Based on the Copula function and Markov process related theory,a time series output model that takes into account the correlation for reliability evaluation is established.The above model was verified by the actual data in Walong,Belgium.Secondly,the reliability model of the components is studied,and a time-varying failure rate model based on the condition cloud is established.The x-condition cloud model is used to describe the relationship between power output and weather conditions,and the y-condition cloud model is used to complete the conversion of weather conditions to weather impact factors.Based on the conditional cloud model and the uncertainty reasoning rules of the cloud model,the weather impact factor is obtained from the power output of the wind farm and photovoltaic power plants.For traditional power equipment,only the aging characteristic is considered.The double Weibull distribution is used to characterize the characteristics of time-varying failure rate.For the components installed in the renewable energy power plant,both weather condition and aging characteristic are considered in process of the reliability evaluation.For power generation systems with a high percentage of renewable energy sources,the reliability model of wind farms and photovoltaic power plants consist of the time series outputmodel and operating state modle.In the study of reliability evaluation methods,the Sequential Monte Carlo Simulation is adapted.Based on the time-varying failure rate model,the state duration of each element is obtained by random sparse sampling,and the system's time series opreating state sequence is obtained by combing the state of different components.The reliability of the power generation system is evaluated in accordance with the load at the corresponding moment.Finally,the aforementioned wind and solar output model,time-varying failure rate model,and evaluation algorithm were established and programmed on the Matlab platform.The output model was constructed based on the data of wind farms and photovoltaic power stations from Walong,and the simulated data were tested.The cloud model was established based on the data from Walong area.Based on the above model,the reliability evaluation of power generation system was performed through the RTS-79 test system,the influence of wind and solar penetration and weather conditions on power system reliability was analyzed.