風場風力發電機之可靠配置模擬統計模式

隨著科技不斷的發展，能源的需求也逐漸提高，但是石化能源總有耗盡的一天。發展再生能源勢必為未來的趨勢，近幾年，已有許多風能的相關研究，像是如何有效安置風力發電機的位置，以達到獲取最佳風能的效益等等。風力發電有著許多不穩定因素，相較於石化能源無法穩定的供給，過去已有許多的研究探討相關議題，但是大部分的研究仍以定量模式的風向風速分佈作為評估產生發電量的依據，可能會導致結果與真實情況產生很大的誤差，造成無法有效地模擬實際風場配置情況。本研究提出模擬最佳化的方式，期望解決或降低模擬結果與真實情況所產生的誤差，透過機率分佈的方式呈現求解風力發電機配置問題的結果，並且結合風險管理，定義四個統計模式，透過改良二元基因演算法，先以過去文獻的問題進行實驗比較，證明演算法的有效性，再使用本研究的問題進行求解，並採用真實的風力觀測資料，最後針對實驗結果進行決策以及相關的分析，對於決策者而言期望能更加符合真實的需求，協助規劃風力發電廠的建置。

關鍵字

風力發電機配置；模擬最佳化；風險管理；二元基因演算法；假設分析

並列摘要

Along with the continuous development of science and technology, power demand is also increasing. However, the fossil fuel is non-recyclable and not sustainable, renewable energy is thus bound to the future trend. Recently, there exist many wind energy researches, such as the effective placement of wind turbines for optimal wind energy capture. Compared to fossil energy, wind power has many uncertainties and cannot supply stable energy. Many researchers tackle this problem by quantitative models to gauge the wind speeds and directions, which may be highly deviated from the situation observed from real wind farms. In order to alleviate the discrepancy between the simulated and real wind farms, this study presents four simulation-based statistical modeling concepts for reliable micro-siting. An improved binary genetic algorithm (BGA) is proposed and compared to previous research. It shows our BGA is effective on the benchmark dataset. The BGA is then experimented according to our statistical models with a real wind-farm dataset. The what-if analysis on the dataset is useful for the decision makers in planning the establishment of wind farms.

並列關鍵字

Wind Turbine Micro-Siting ； Simulation Optimization ； Risk Management ； Binary Genetic Algorithm ； What-If Analysis

參考文獻

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風場風力發電機之可靠配置模擬統計模式

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