Browsing by Author "Lin, Lee-Cheng"

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A hierarchical cost learning model for developing wind energy infrastructures
(2013-12-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Liu, Penny H. Y.
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Lin, Lee-Cheng
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Ou, Jerry J. R.
Renewable energy has been increasingly promoted and used to substitute non-renewable fossil-fuels, which cause negative effects on the environment. The Taiwan Statute for Renewable Energy Development has regulated and promoted renewable energy since 2009. A feed-in tariff (FIT) for renewable energy is one of the incentives that the government uses to promote the installation of green power generation facilities. The price of the electricity feed-in tariff is based on the current and future costs of renewable energy generation. When analyzing cost trends for renewable energy installation, many researchers use a single factor cost learning curve model. However, past studies indicate that there are multiple factors affecting the overall cost of installing renewable energy. Hence, this research develops a hierarchical installation cost learning model which considers multiple factors to accurately model and forecast wind energy development. This research uses wind power development data from Taiwan as a case study. We identify the cost factors, evaluate the learning effects, and compare the hierarchical learning curve model to the basic (non-hierarchical) learning curve model. The research results show an improved fit between the hierarchical model and the actual data when compared to the basic learning model. The study also provides new insights between the wind power learning progression of Taiwan and three countries in Europe. (C) 2013 Elsevier B.V. All rights reserved.
Constructing a Hierarchical Learning Cost Curve for Photovotaic System
(2013)
Trappey, Amy J. C.
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Trappey, Charles V.
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Liu, Penny H. Y.
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Lin, Lee-Cheng
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Ou, Jerry J. R.
Developing renewable energy is an effective approach to reduce global warming and satisfies increasing energy demands. Among all of the renewable energy sources, solar power has the greatest potential because it is derived naturally from the sun. Photovoltaic methods convert solar radiation into direct current electricity via semiconductors. Although the energy coming from the sun is abundant, the cost of generating electricity using photovoltaic methods remains higher than conventional means of generating electricity. Often, expensive manufacturing and installation costs reduce the benefits of photovoltaic systems. In order to encourage the usage of photovoltaic systems, several countries have used a feed-in tariff calculated based on the installation costs as an incentive. Considering this important incentive, a learning curve model is constructed to model the installation costs. The cost and benefit model helps evaluate profitability as well as determine the feasible buyback prices and future installation plans. There are many factors that influence the costs. Therefore, this study uses Taiwan as case study and develops a hierarchical installation cost learning curve model for photovoltaic facility construction. Finally, the progression rate, which represents the cost trend, is calculated using case data.
The determinants of photovoltaic system costs: an evaluation using a hierarchical learning curve model
(2016-01-20)
Trappey, Amy J. C.
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Trappey, Charles V.
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Tan, Hao
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Liu, Penny H. Y.
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Li, Shin-Je
The uptake of solar power globally as an important alternative energy source to fossil fuels, together with a rapid fall in the cost of photovoltaic (PV) systems, has been phenomenal during the past decade. This trend is widely anticipated to continue for the years to come. The decline in PV installation costs, like many other new technologies through history, has been largely driven by the learning curve effect. However, it is suggested that other factors, such as costs of key production inputs and prices of competing technologies, also impact the costs of PV systems. In this study, we construct a hierarchical learning curve model to quantify the effects that various factors have on installation costs of PV systems based on empirical data from Taiwan. The results show that, in addition to the learning curve effect as underpinned by an increase of cumulative PV capacity, reductions to silicon price have significantly contributed to the decline of the final installation costs of PV systems in Taiwan. By quantifying the effects of critical cost factors, the learning curve effects on PV installation costs in Taiwan are defined which enable a more accurate projection of PV installation costs for governments, PV producers, operators and users. (C) 2015 Published by Elsevier Ltd.