本論文應用累積元及基因演算法求解不定因素下之獨立電力系統電源規劃問題。本論文將具有間歇特性的風速、太陽日照以及負載變動等不確定因素,利用其資料型態選用韋伯分佈及高斯分佈特性建立不確定模型,得到其隨機分佈之八級累積元,再運用Gram-Charlier級數展開式係數加上Hermite多項式的前八項多項式,求得機率密度函數及累積機率。本論文利用上述隨機模型,使用基因演算法在考慮二氧化碳排碳限制及失載機率可靠度限制下,求解最佳風機、太陽光伏、儲能電池及柴油機組合的個數。本論文最後探討一個150kW的獨立電力系統之最佳電源規劃,並比較不同二氧化碳排放及失載機率限制對規劃結果之影響。
The thesis uses cumulant and Genetic Algorithm (GA) to study the generation expansion considering uncertainty in an isolated power system. This thesis uses the historical data with characteristics of intermittence to develop uncertainty model using Weibull distribution and Gaussian distribution. This step can obtain cumulants of random variables. With the cumulant effect, the Gram-Charlier series expansion and Hermite polynomial are utilized to gain the probability density function and cumulative probability. The cumulative probabilities at any level of confidence can be estimated for the random variables. This thesis utilizes these probability models and uses GA to find the optimal the numbers of wind turbines, photovoltaic arrays, battery banks and diesel generators while satisfying the requirements of CO2 and reliability limits. Finally this thesis illustrates the results from an example of a 150kW isolated power system. The comparative studies among different CO2 emission constraints and Lose of Load Probability (LOLP) constraints are given.