| Summary: | 碩士 === 國立清華大學 === 工業工程與工程管理學系 === 103 === Due to rapid industrial development and economic growth, the demand for energy consumption has increased gradually. Fossil fuels (coal, natural gas, and petroleum) account for 90% of non-renewable energy source. However, the combustion of fossil fuels is the major source of pollutants which will result in greenhouse gas emissions and global warming. Taiwan government has enacted the Statute for Renewable Energy Development, which guarantees that the public who own renewable electricity generation facilities, such as roof-top solar photovoltaic (PV) systems, will receive a fixed price for all of the electricity they generate for a contract term of twenty years. For government, how to set the appropriate feed-in tariff (FIT) rates is the major issue because the low FIT rates will make it unattractive to invest in renewable energy but the high FIT rate will cause government's financial burden.
Therefore, in order to investigate if the current renewable energy promotion policies in Taiwan can make energy demand, environmental concerns and economic considerations (E3) reach balance, this research adopts system dynamics (SD) approach to develop an analysis model to evaluate FIT policy. The impacts of FIT on solar PV installed capacities, carbon dioxide emissions and energy dependency on imports are all considered. There are lots of energy-related platforms providing various energy data with different storage format. Therefore, with the concept of information integration, this research establishes a data warehouse (DW) to store and manage historical energy data collected from global energy platform. However, different decision-making models require various data dimensions, so the multi-dimensional data cubes should be built according to analysis model. In this research, the DW is built based on star-type data cube design, which provides comprehensive view of information. The data can be queried and then used as input data to plot the data trend using line graphs. It is necessary to understand and compare the data from different perceptions before analysis. Then, the data can be used as the input data of SD model with SQL syntax.
The SD model simulates the scenarios of policies for promoting renewable energy which reference the FIT rate of Germany and Spain. The simulation results show that the adoption of Germany FIT policy (where FIT declines at 1% annually) will effectively reduce the cost of carbon reduction and energy dependency on imports. Therefore, a gradual decline in FIT rates would be a better choice. The SD simulation results are valuable reference for governments to adjust the appropriate FIT rates.
|
|---|
