以結合蒙特卡羅模擬的二項式選擇權定價模型評價離岸風力發電開發專案

隨著環境變化和能源短缺的現象，加速了再生能源之開發與利用。目前台灣陸域風電發展已漸趨緩，近年亦開始朝離岸風力發展。傳統評估方法對於投入成本大、不確定性高、生命週期長之離岸風電專案並不適用，因此有必要引入實質選擇權評價法（ROA）進行評估。現有實質選擇權評價法假設專案效益為一個期望值不具趨勢的隨機變數，專案投資與營運支出則被視為一個常數。上述假設無法適用於離岸風電專案，因此本研究假設：（1）專案效益為一個期望值具趨勢的隨機變數。（2）專案投資與營運支出為一個依照學習曲線降低成本之期望值具趨勢的隨機變數。（3）前述兩個隨機變數之間具有相關性。為了求解此一模型，本文提出一種結合模擬法的二項式選擇權定價法，並對離岸風電專案進行敏感性分析。研究結果顯示：（1）當總效益趨勢係數s、一年的投資人的資金成本率r、投資機會總效益之現值S、可供投資人延遲決策的期間T越大時，擴張NPV皆越大；（2）當總支出趨勢係數k、投資機會總支出之現值K、相關係數𝜌越大時，擴張NPV皆越小。（3）在S與K正相關的情況下，當一年的投資機會總效益之現值的波動率𝜎_𝑆越大時，擴張NPV越大；而在負相關下，關係不明顯。（4）在S與K正相關的情況下，當一年的投資機會總支出之現值的波動率𝜎_𝐾越大時，擴張NPV越小；而在負相關下，𝜎_𝐾越大時，擴張NPV越大。（5）總效益趨勢係數s、總支出趨勢係數k、相關係數𝜌、一年的投資機會總效益之現值的波動率𝜎_𝑆、一年的投資機會總支出之現值的波動率𝜎_𝐾等是對選擇權價值影響最大的因子。

關鍵字

離岸風力發電開發；實質選擇權；二項式選擇權定價模型；蒙特卡羅模擬

並列摘要

With environmental changes and energy shortages, the development and utilization of renewable energy has been accelerated. At present, the development of onshore wind power in Taiwan has gradually slowed down, and the development of offshore wind power has also begun in recent years. Traditional evaluation methods are not suitable for offshore wind power projects with high input costs, high uncertainty, and long life cycles. Therefore, it is necessary to introduce the real option approach (ROA) for evaluation. The existing ROA assumes that the project benefit is a random variable with no trend in expected value, and the project investment and operating expenditure are regarded as a constant. The above assumptions cannot be applied to offshore wind power projects. Therefore, the work assumes the following assumptions. (1) Wind power benefit is a random variable with an expected trend. (2) The reducing cost of investment and operation expenditures with learning curve is a random variable with an expected trend. (3) There is correlation between the two random variables above. The three assumptions above are different from the traditional real options to defer; hence, it's necessary to propose new models and solutions. We propose a new hybrid approach, Binomial Options Pricing Model Combined with Monte Carlo Simulation (BOPM-MCS), which combines Binomial Options Pricing Model (BOPM) and Monte Carlo Simulation (MCS) to combine their advantages. The sensitivity analysis is also conducted to find the key factors influencing the expected value of the option value. The results show that (1) when the total benefit trend coefficient, the investor's capital cost rate, the present value of the total benefit of investment opportunities (S), and the period for investors to delay decision-making are greater, the extended NPV will be greater; (2) The larger the total expenditure trend coefficient, the present value of the total investment opportunity expenditure (K), and the correlation coefficient, the smaller the extended NPV. (3) In the case of a positive correlation between S and K, when the volatility of S is greater, the extended NPV is greater; while in the negative correlation, the relationship is not obvious. (4) In the case of a positive correlation between S and K, when the volatility of K is larger, the extended NPV is smaller; while in the negative correlation, when larger, the extended NPV is greater. (5) The total benefit trend coefficient, the total expenditure trend coefficient, the correlation coefficient, the volatility of S, the volatility of K are the factors that have the greatest impacts on the value of the option.

並列關鍵字

Off-shore wind power generation ； Real options ； Learning curve ； Monte Carlo Simulation ； Binomial Options Pricing Model (BOPM)

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