A Mathematical Programming Technique for Multi-footprint Constrained Energy Planning

• Main Authors: Han-Fu Lin, 林翰甫
• Other Authors: 李瑞元
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9cek9h

碩士 === 國立臺北科技大學 === 化學工程與生物科技系化學工程碩士班 === 106 === Since the Industrial Revolution, fossil fuels (coal, oil, and natural gas) have been heavily exploited for technology development, civilization, industrialization, and modernization. However, the resulting excess CO2 emissions have generally been regarded as the main cause of global warming and climate change. Key strategies for mitigating CO2 emissions include improving energy efficiency, expanding low-carbon energy use, and deploying carbon capture and storage (CCS). According to the International Energy Agency (IEA) assessment, CCS and renewables would contribute more than 50% of the cumulative emissions reductions by 2050. Based on the 2017-2031 long-term load forecast and the 2016 long-term power development plan of the Taiwan Power Company, this work analyzes the feasibility of Taiwan’s energy sector in meeting the energy demand in 2025 while achieving the emissions reduction target, and provides optimal energy planning for various scenarios. In this study, the Integrated Environmental Control Model (IECM) was used to evaluate the performance of thermal power plants and various carbon capture technologies. Plant simulation was performed to determine the net power output, carbon emissions, water consumption and power generation cost of each unit. Constraints and limits on energy planning were also identified according to the current energy policies, regulations and environmental conditions. This work applies mathematical programming and presents a generic energy sector planning model considering multiple footprint constraints. A case study of Taiwan’s 2025 energy mix is presented to demonstrate the complex decision-making and cost trade-offs in the deployment of CCS and renewables through the analysis and comparison of six different scenarios.