電廠投資為耗時、金額大之工程，且除建造電廠外，亦須興建大量電
網、變電所，決策者若在期初設置的機組太多、擴展規模過快，而未來電力需求卻不如預期時，電廠成本勢必上升，由此可見需求變化對電力系統規劃之影響性。以往電力系統規劃模型，將未來電力負載需求視為給定值，但在現今多變的社會中，顯然無法滿足此假設。基此，本研究旨在建構考量電力負載需求不確定性下之電力系統規劃模型。
兩階段隨機規劃法（Two-Stage Stochastic Programming）適合用來處理不確定性問題，因此本研究以此方法將模型分為兩個階段，以考量在第二階段之需求不確定性發生時，在第一階段之發電機組做最有效率的配置。然此法雖可將不確定性納入模型中，但亦造成模型過於龐大無法計算，故本研究利用決策樹模擬方式，將未來可能發生之電力負載需求簡化為各個節點，並以蒙地卡羅法模擬各節點值與計算其路徑機率。
模擬結果顯示，兩階段隨機電力規劃模型可將電力負載需求之不確定
性納入考量，故電力規劃者可參考此方法，以確保未來電力系統規劃之穩健性。而在模型演算過程發現，在隨機參數具有常態分配之性質下，利用蒙地卡羅法模擬未來電力負載需求並計算其路徑機率值，會比直接給予路徑機率值來得更為客觀並符合學理依據。
本研究在「核能機組情境」中模擬結果顯示，至2025年完全無核能
發電之目標下，燃煤機組占比顯著增加，將由2010年36.85%上升至2025年50.4%。而在「二氧化碳減量情境」下，燃煤機組占比則顯著減少，轉由燃料成本較高之燃氣機組取代，且再生能源機組占比大幅上升(風力機組將達到發展上限3,000 MW)，但亦大量增加總期望成本現值。

Power plant investments are generally irreversible, lumpy, capital intensive, long lived, and have significant lead times. Investments are also necessarily undertaken in the context of projections of future electricity demand. However, future electricity demand is subjected to a range of uncertainties, particularly the economic growth and patterns of development. Overestimating long-term electricity demand can lead to overinvestment in generation assets and significantly higher industry costs. On the other hand, underestimating electricity demand can lead to insufficient investment in generation capacity, resulting in unserved demand and potentially significant adverse impacts on economic progress. Traditional electricity supply planning model regards the electricity demand as deterministic parameter (i.e. the electricity demand is given exogenously) to select power generation technologies. But in today’s world, the energy planners are facing tremendously complex environments full of uncertainty and risks, and electricity industry is also in an uncertain situation, where the assumption of certain electricity demand is apparently unreasonable. Therefore, the research aims to establish an electricity supply planning model incorporating electricity demand uncertainty.
The two-stage stochastic programming methodology is applied in the research to deal with the uncertainty in electricity demand. The model also combines decision tree and Monte Carlo methodology to reduce possible nodes and determine the future electricity demand values and probabilities. Using the model, we implement some simulation scenarios to evaluate the impact on the portfolio of power generation technologies and generating cost.
The simulation results obtained by applying the model to Taiwan's electricity sector indicate that the two-stage stochastic programming methodology can explicitly address the electricity demand uncertainty. Monte Carlo methodology can characterize uncertainty by assigning a normal distribution to uncertain electricity demand, which can be estimated from historical data. Consequently, the probabilities of future electricity demand can be determined endogenously instead of given exogenously.
In the nuclear-free scenario, the simulation shows that the share of coal-fired power plants will grow from 36.85% in 2010 to 50.4% in 2025 as all nuclear power plants decommission in 2025. In the reduction of carbon dioxide emissions scenario, the proportion of coal-fired power plants reduces and is replaced by LNG-fired and renewable energy power plants. Hence, this contributes significantly to the increase in total generating cost.

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