再生能源應用發展係台灣改善能源自主性及降低溫室氣體排放的重要選項之一。由於再生能源價格目前在能源市場上尚無法與化石燃料競爭，各國政府紛紛提供協助，希望鼓勵再生能源應用。台灣亦訂定再生能源政策與相關鼓勵措施，然而，目前對政策合理性相關分析仍相對缺乏，造成社會大眾對政府推廣再生能源的決心有所疑慮。有鑑於此，探討再生能源發展的可能源與推廣政策之適當性，是當前重要且緊急的議題。
首先，本研究應用技術經濟評估細緻地探討各項再生能源技術的成本效益分析。此方法能具體提供各項再生能源推廣規劃建議，審慎檢視再生能源的發展限制。只是評估各項再生能源技術，須考量其不同成本特徵來架構分析模式。目前，本研究選擇纖維酒精與太陽光電作為探討範圍，分別為生質燃料和再生電能的重要代表技術。
發展一纖維酒精的最適生產模型，探討如何策略性分配農產廢棄物至酒精廠生產纖維酒精，以及生產達政府目標量的可行性研究。文中討論了模型需要的酒精轉換率、料源供給量、潛在廠房位置和運輸網絡等參數。考量不同供給目標量和廠房最大生產規模的情境模擬，此模型為一非線性規劃模式。目標函數為成本極小化。模擬結果說明既定技術下，酒精生產成本介於每公升35-38元間；與進口酒精成本或汽油售價相比，仍缺乏價格競爭性。敏感性分析結果則顯示技術進步是纖維酒精商業化的關鍵因素。另外，以農業廢棄物90%利用率估算，得知國內可利用其生產41萬公秉纖維酒精，足以提供台灣40%的運輸需求。
政府為鼓勵太陽光電電能應用，訂定了推廣目標及躉購費率機制，然而，目前高成本特徵導致社會大眾對政府推廣決心有所疑慮。有鑑於此，本文從技術經濟分析的角度，建立台灣的太陽光電供給曲線，探討台灣未來的太陽光電開發潛力、太陽光電發電成本具經濟性的條件，以及現行太陽光電政策之適當性。結果顯示屋頂型光電可裝置潛力約有3,758MWp，發電成本介於5.7210-15.3226NT$/kWh。與燃氣發電成本相較，光電明顯沒有市場競爭力。敏感性分析則顯示光電發電要具備競爭力需依賴技術進步。以及，本研究亦提供一分析工具連結技術進步、推廣目標量及躉購費率的關係。此分析工具乃是根據光電供給曲線而建立，用以探討技術進步對推廣目標量及躉購費率的影響，另一方面，也可以探討推廣目標量及躉購費率對技術進步的要求。
惟技術經濟評估偏屬於靜態分析架構，乃利用特定的技術規劃下進行，無法呈現技術學習之動態效果，也缺乏再生能源發展可能對經濟面、能源面、環境面的整合影響。因此，為提供決策者一政策評估綜合架構，觀察再生能源政策(如：躉購費率、能源稅)之實施，對經濟發展、能源結構及二氧化碳減量長期效果。最後，本研究應用系統動態模型發展評估架構，對再生能源政策進行模擬分析。
本研究利用系統動態方法建立台灣能源-環境-經濟 (3E) 的整合性架構。此整合性架構之優勢在於能描術能源、環境與經濟的複雜回饋關係，於是，本研究設計相關再生能源政策，數值模擬再生能源政策執行對能源發展、溫室氣體減量和經濟發展的長期影響，進行再生能源政策意涵之探討與從中提供未來政策建議。研究成果顯示，現行躉購費率機制對鼓勵再生能源使用雖有些許成效，但無助於二氧化碳減量目標。若單以躉購費率機制要達成政府回到2000年二氧化碳排放水準，躉購費率至少需要提高27倍，這會造成政府嚴重財政負擔。以能源稅來看，碳稅是以二氧化碳含量來課徵，對碳減排有直接影響，故以碳稅進行最適化模擬，得到要達到政府二氧化碳減排量目標的最適碳稅高達393$/TonCO2(12,000NT$/TonCO2)。這些證據躉購費率機制或是能源稅能源稅均難以達到低碳社會目標。

Taiwan, being highly dependent on imported fossil fuels, needs to find more reliable energy sources locally. For this reason, it is important to explore the feasibility of renewable energy deployment. This dissertation did a detailed exploration of Taiwan’s PV and cellulosic ethanol policies at county level in view of techno-economic modeling. System dynamics modeling is applied to comprehensively demonstrate the 3E effects of renewable energy policies at national level.
A bioethanol production optimization model is developed and implemented to assess the feasibility of producing Taiwan’s target volume of cellulosic ethanol. The most strategic placement of biorefineries based on feedstock supply is also explored to optimize the allocation of agricultural wastes. In order to simulate the different supply targets and the varying maximum biorefinery capacities, the nonlinear planning model utilizes the following information: ethanol yield, quantities of feedstock materials, potential refinery locations, and transportation networks. This simulation is undertaken to satisfy a minimum cost objective function and find out the most cost-effective factors of production. Sensitivity analysis explores the possible effects of technological advancements in cellulose conversion. This study estimates that a usage rate of 90% on two million metric tons of agricultural waste would produce 410 million liters of bioethanol, enough to meet 40% of Taiwan's transportation fuel demand.
It is widely recognized that solar energy, a major renewable energy source, can strengthen a country's energy security and reduce CO2 emissions. For this reason, Taiwan aims to develop its solar power by promoting photovoltaic (PV) applications. In order to meet its PV installation targets, the government considers adopting Feed-in Tariffs (FITs), offering subsidies on capital, and funding research and development. At present, there is a wide gap between the country's installed capacity and the long-term government targets. Therefore, this study uses a PV supply curve to demonstrate the potential contribution of PV power to Taiwan's electricity requirements. Based on this curve, an assessment tool is developed to show the relationship between PV installed capacity and cost reduction under an FIT scheme. Using this assessment model, policymakers can simulate the adoption of PV projects at county level and anticipate possible challenges to the project. Furthermore, it is also developed to measure the required cost reduction for PV technology in order to reach specific targets under the FIT scheme.
Typically, these renewable energy policies have complex energy and economic feedback loops, which may cause economic vulnerability. Therefore, it is important that it fully recognizes energy, environment and economic (3E) effects of renewable energy policies. A system dynamics model was used in this paper to assess the policy implications. The numerical results showed that the current FIT price (50$/GJ) does little help to stimulate renewable energy technology. The huge FIT price (average 1400$/GJ) could lead to a dramatic increase in renewable energy use, but also severely damage the economy. Similarly, up to 393$/TonCO2 (12,000NT$/TonCO2) of carbon tax needs to be imposed in order to achieve the target reduction of CO2 emissions. These evidences suggest that both the FIT and energy tax regime are not sufficient to advance a low-carbon energy future.