南方能源建设 (Nov 2024)
Application of Monte Carlo Simulation for Calculating Power Generation with Exceeding Probability in Wind Farm
Abstract
[Introduction] The uncertainty of wind farm brings probability distribution to power generation. The objective and scientific evaluation of uncertainty factors is an important prerequisite for calculating power generation with exceeding probability in wind farm. At present, the industry generally adopts reduction factos to estimate power generation to achieve risk control. Alternatively, uncertain factors are analyzed using probabilistic methods to evaluate the exceedance probability of power generation in wind farms. In view of the simple reduction or neglect of the nature of the uncertain factors, this paper proposes a scientific method to calculate the power generation with exceeding probability in wind farm following the nature of the uncertain factors. [Method] In this paper, the Monte Carlo simulation method was used to construct the normal probability distribution model of uncertainty factors, to simulate and obtain the statistical results for uncertainty. The wind speed-power generation senitivity factor was applied to determin the uncertainty in wind farm power generation, and finally, the exceeding probability of wind farm power generation was calculate. [Result] The results of many random simulations by Monte Carlo are concrete functions obeying normal distribution. In this paper, the results of three cases, the standard deviation of uncertainty factor is 12.0%, 14.0%, 16.0% of the average, are simulated. The distribution range of 95% wind speed uncertainty in specific confidence interval is 7.08%～8.56%, 6.97%～8.71% and 6.88%～8.84%, and the distribution range of total uncertainty of power generation is 13.36%～15.92%, 13.17%～16.18% and 13.01%～16.41% respectively. The distribution range of power generation with exceeding probability is 96.06～101.52 GWh, 95.5～101.92 GWh and 95.01～102.26 GWh respectively. [Conclusion] It is helpful for decision-making to be established on a correct and reliable basis to evaluate the investment risk of wind farm based on the power generation with exceeding probability. The Monte Carlo simulation method is scientific and efficient, and the results obtained have statistical significance.
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