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研究生:楊逸亞
研究生(外文):Yi-YaYang
論文名稱:因應台灣能源轉型之儲能系統建置評估
論文名稱(外文):Assessment of Energy Storage System Deployment for Energy Transition in Taiwan
指導教授:吳榮華吳榮華引用關係黃韻勳黃韻勳引用關係
指導教授(外文):Jung-Hua WuYun-Hsun Huang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資源工程學系
學門:工程學門
學類:材料工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:113
中文關鍵詞:能源轉型鴨子曲線間歇性再生能源儲能系統
外文關鍵詞:Energy TransitionDuck CurveVariable Renewable Energy (VRE)Energy Storage System (ESS)
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考量到台灣為一孤島國家,化石燃料資源稀少,對於進口能源依賴度高達98%,同時為了達成巴黎氣候協議,政府提出了能源轉型政策以提升國家能源自主率,將再生能源占發電量之占比提升至20%,並於2025年達成非核家園之願景。在高占比的再生能源發電下,其所擁有的間歇特性將可能對供電造成不穩定的風險,因此未來的電力供應規劃將面臨挑戰,而建置儲能系統於台灣各區域,將大幅被應用於供電的調節。
本研究中以線性規劃方式建置了台灣2025年達成非核家園後的短期電力模型。由於台灣地處亞熱帶,氣候南北有別,本研究特別著重各地區的間歇性再生能源(Variable Renewable Energy)發電特性,並將模型尺度切為每月的24小時,用以觀察各區域的再生能源與儲能需求量。模型中將2025的再生能源建置目標裝置容量納入考量,包含20GW太陽能、5,738MW離岸風力與1,200MW陸域風力。以總發電成本最小化的目標下,模擬2025電力供應情形。所有能源的成本資料被分為「固定成本」與「變動成本」。固定成本包含期初投資的建置成本,並攤提至其使用年限;變動成本則包含機組發電的燃料費用、固定運轉維護費用與輸配電路系統的維護費用。
研究結果顯示,在未來政策目標達成情況下,由於傍晚太陽能出力快速下降,傳統能源供應需求上升,將出現鴨子曲線 (Duck Curve),傳統能源需求比例超過90%,需搭配可快速調升之燃氣機組以因應傍晚的電力緊缺。研究中亦發現,以現有的抽蓄水力機組搭配風力發電,一年可調節842~847百萬度的電力,藉以調整再生能源的供電時段與需求相符。而安裝544~620MW之太陽能儲能系統,並設置太陽能支援傍晚鴨子曲線出現時,一年可儲存330~453百萬度之電力。安裝儲能系統也將造成總發電成本上升約20.3% ~ 20.4%。研究建議未來應考量傍晚電力緊缺問題,使傳統能源機組於傍晚待命;並確保天然氣安全存量,以因應2025年的燃氣發電50%的供應占比。
Owing to the scarcity of indigenous energy resources, Taiwan relies on imports for 98% of its energy requirements, which is highly dependent on fossil fuels. Considering issues of energy security, and in response to the Paris Agreement, the Taiwanese government has initiated planning for Energy Transition. It aimed to increase renewable energy to 20% of total power generation and achieve the nuclear-free homeland by 2025. Energy Storage System (ESS) is an important option for facilitating the integration of variable renewable energy (VRE). Therefore, it is crucial to assess regional ESS requirements during Energy Transition in Taiwan.
This paper develops a short-term power dispatch model based on linear programming framework to incorporate the highly resolved generation data. Our model differs from previous applications of linear programming to the electricity supply planning. We explicitly address the issue of the generation technology portfolios and ESS selection from a regional renewable energy generating perspective in linear programming frameworks, and extend the formulation to reflect regional ESS requirements. The model is utilized to simulate the daily power generation performance of multiple energies in 12 months, and simulates the charging and discharging operation of ESS. In other words, our model can explicitly account for ESS specification and its detailed operation in each region based on hourly temporal resolutions as well as economic and operation performances.
The model simulates hourly operation of all energies in Taiwan in 2025, including solar power (20GW), offshore wind (5.738GW), and inland wind (1.2GW). The model minimizes the total cost of satisfying regional electricity demand in every hour of 12 months. The costs consist of capital costs and operation and maintenance (O&M) costs of each energy, which also takes the future fuel price and VRE installation fee prediction into consideration. The model constraints include regional balanced electrical power load, the production constraint of the generators in different regions, as well as policy and environmental restrictions, and the energy storage technology restrictions.
The research results show that when the future policy goals are achieved, due to the rapid decline in solar energy output in the evening and the rising demand for traditional energy supply, a duck curve will appear. The proportion of traditional energy demand will exceed 90%. This highlights the importance of the adjustment ability of the traditional power units.
In this study, we paired the wind power with the existing facility of PSH, which can store 842 to 847 GWh of electricity in a year, and achieve the purpose of deploying wind power generation to match the demand at the same time. After installing 544~620MW ESS to store solar power, they can store 330~453 GWh electricity to support the energy shortage caused by the rapid reduction of solar energy in the evening.
The installation of energy storage systems will also increase the total power generation cost by approximately 20.3% ~ 20.4%, compared to the generation cost in 2019. We suggest that in the future, the issue of power shortage in the evening should be considered, so that traditional energy generating units should be on standby in the evening. Also, a safe stock of natural gas should be ensured to meet the 50% supply of gas-fired power generation in 2025.
中文摘要 I
英文摘要 III
致謝 V
Table of Contents VI
List of Figures IX
List of Tables XII
Acronym XIV
Nomenclature XV
Chapter 1. Introduction 1
1.1 Backgrounds and Motivation 1
1.1.1 Energy Policies in Taiwan 1
Solar power 2
1.1.2 Global Energy Outlook 3
1.1.3 Taiwan’s Energy Supply Status 10
1.2 Research Objectives 14
1.3 Research Overall Framework 16
1.4 Limitations 19
1.5 Summary 20
Chapter 2. Literature Review 21
2.1 Power Planning on Linear Programming Model 21
2.1.1 Domestic Research 21
2.1.2 Foreign Research 26
2.2 Economics of Energy Storage Systems 28
2.3 Summary 30
Chapter 3. Model Establishment 31
3.1 Objective Function 34
3.2 Constraint Functions 35
3.2.1 Constraint: Satisfying electricity power demand 35
3.2.2 Constraint: Policies 37
3.2.3 Constraint: The production constraints of the generator set 38
3.2.4 Constraint: Energy storage settings 39
3.2.5 Constraint: Nonnegative and integer restrictions 45
Chapter 4. Data Collection and Processing 46
4.1 Installed Capacity 46
4.1.1 Data Processing of Variable Renewable Energy 48
4.1.2 Data Processing of Traditional Generators 51
4.2 Capacity Factor Settings 55
4.2.1 The Capacity Factor of Traditional Energy 57
4.2.2 The Capacity Factor of Renewable Energy 59
4.3 Costs of Energy and Energy Storage Systems 67
4.3.1 Costs of Traditional Energy 67
4.3.2 Costs of Renewable Energy 70
4.3.3 Costs of Energy Storage Systems 72
4.4 Demand Data 74
Chapter 5. Simulation Results and Discussions 76
5.1 Model Validation 77
5.2 Basic Scenario: None ESS Installed 81
5.3 Scenario I: PV Distribution Deferral 86
5.4 Scenario II: PV Peak-Cutting 92
5.5 Comparison 98
Chapter 6. Conclusion and Policy Implications 100
6.1 Conclusion 100
6.2 Further Research and Policy Implications 101
6.2.1 Further Research Implications 101
6.2.2 Policy Implications 101
References 102
Appendix A: The Cost Data of ESS (Monte Carlo Simulation Result) 107
Appendix B: Simulation Results of ESS Deployment Model 108
1.Baldinelli, A., Barelli, L., Bidini, G., & Discepoli, G. (2020). Economics of innovative high capacity-to-power energy storage technologies pointing at 100% renewable micro-grids. Journal of Energy Storage, 28, 101198. Retrieved from http://www.sciencedirect.com/science/article/pii/S2352152X19308977. doi:https://doi.org/10.1016/j.est.2020.101198
2.BP. (2020). BP Statistical Review of World Energy 2019. Retrieved from https://www.bp.com/en/global/corporate/energy-economics/energy-outlook.html
3.Bureau of Energy, MOEA. (2019a). Energy Indicators of Taiwan. Retrieved from https://www.moeaboe.gov.tw/ECW/english/content/ContentDesc.aspx?menu_id=1552
4.Bureau of Energy, MOEA. (2019b). Introduction of Taiwan's Energy Policy. Retrieved from https://tpludemo.files.wordpress.com/2019/01/1080124_%E6%88%91%E5%9C%8B%E8%83%BD%E6%BA%90%E6%94%BF%E7%AD%96%E4%BB%8B%E7%B4%B9_%E7%B6%93%E6%BF%9F%E9%83%A8%E8%83%BD%E6%BA%90%E5%B1%80%E6%9D%8E%E5%89%AF%E5%B1%80%E9%95%B7%E5%90%9B.pdf
5.Bureau of Energy, MOEA. (2020). Energy Statistics Query System. Retrieved from https://www.moeaboe.gov.tw/wesnq/
6.Joint Research Centre. (2018). Cost development of low carbon energy technologies. Retrieved from https://ec.europa.eu/jrc/en/publication/cost-development-low-carbon-energy-technologies-scenario-based-cost-trajectories-2050-2017-edition
7.Chang, T.-C. (2019). Techno-economic Analysis of Large-scale Energy Storage System in Taiwan. (Master), National Cheng Kung University Tainan.
8.Chen, J.-S., Chang, Y.-J., & Cho, C.-H. (2019). Evaluation of Electrical Energy Storage Requirements for Renewable Energy Policy in Taiwan. Journal of Taiwan Energy, 6, 313-334.
9.Deane, J. P., Chiodi, A., Gargiulo, M., & Ó Gallachóir, B. P. (2012). Soft-linking of a power systems model to an energy systems model. Energy, 42(1), 303-312. Retrieved from http://www.sciencedirect.com/science/article/pii/S0360544212002551. doi:https://doi.org/10.1016/j.energy.2012.03.052
10.Denholm, P., O’Connell, M., Brinkman, G., & Jorgenson, J. (2015). Overgeneration from Solar Energy in California: A Field Guide to the Duck Chart. Retrieved from https://www.nrel.gov/docs/fy16osti/65023.pdf
11.Huang, H.-L. (2009). Combined MODM and System Dynamics for the Evaluation of Electricity Sector Pursuing National 3E Targets. (Master), National Taipei University, Taipei.
12.Huang, Y.-C., Chen, J.-J., & Ko, F.-K. (2017). Grid-Level Energy Storage System Analysis in Taiwan Simulation Using TIMES Model. Journal of Taiwan Energy, 4, 45-58.
13.IEA. (2018). Status of Power System Transformation 2018. Retrieved from https://webstore.iea.org/status-of-power-system-transformation-2018
14.IRENA. (2017). Electricity storage and renewables: Costs and markets to 2030. Retrieved from https://www.irena.org/publications/2017/Oct/Electricity-storage-and-renewables-costs-and-markets
15.IRENA. (2018). Power system flexibility for the energy transition. Retrieved from https://www.irena.org/publications/2018/Nov/Power-system-flexibility-for-the-energy-transition
16.Renewable Energy Development Act, (2019).
17.Ko, L., Chen, C.-Y., & Seow, V.-C. (2014). Electrical power planning and scheduling in Taiwan based on the simulation results of multi-objective planning model. International Journal of Electrical Power & Energy Systems, 55, 331-340. Retrieved from http://www.sciencedirect.com/science/article/pii/S0142061513004055. doi:https://doi.org/10.1016/j.ijepes.2013.09.023
18.Kuo, C.-W., Chou, Y.-F., Hung, M.-L., & Liu, T.-Y. (2015). Long-Term Electricity Supply-Demand Planning Simulation Using Taiwan TIMES Model. Journal of Taiwan Energy, 2, 363-382.
19.Lazar, J. (2014). Teaching the Duck fly. Retrieved from http://www.ripuc.ri.gov/eventsactions/docket/4443-EERMC-Presentation2_5-8-14.pdf
20.Lazard. (2018). Lazard's levelized cost of storage analysis-version 4.0. Retrieved from https://www.lazard.com/
21.Lee, K.-C. (2015). The Study of Long-run Optimal Electricity Portfolio Planning in Taiwan: Apply Portfolio Theory. (Master), National Taipei University, Taipei.
22.Leou, R. C. (2012). An economic analysis model for the energy storage system applied to a distribution substation. International Journal of Electrical Power & Energy Systems, 34(1), 132-137. Retrieved from (Go to ISI)://WOS:000298022900015. doi:10.1016/j.ijepes.2011.09.016
23.Liu, J., & Zhong, C. F. (2019). An economic evaluation of the coordination between electric vehicle storage and distributed renewable energy. Energy, 186. Retrieved from (Go to ISI)://WOS:000492797300021. doi:10.1016/j.energy.2019.07.151
24.Pfenninger, S., Hawkes, A., & Keirstead, J. (2014). Energy systems modeling for twenty-first century energy challenges. Renewable and Sustainable Energy Reviews, 33, 74–86. doi:10.1016/j.rser.2014.02.003
25.Schill, W.-P., & Zerrahn, A. (2018). Long-run power storage requirements for high shares of renewables: Results and sensitivities. Renewable and Sustainable Energy Reviews, 83, 156-171. Retrieved from http://www.sciencedirect.com/science/article/pii/S1364032117308419. doi:https://doi.org/10.1016/j.rser.2017.05.205
26.Tseng, L.-T. (2015). Development of the Dynamics Model for Taiwan Power Supply Planning. (Master), National Cheng Kung University, Tainan.
27.Ueckerdt, F., Pietzcker, R., Scholz, Y., Stetter, D., Giannousakis, A., & Luderer, G. (2017). Decarbonizing global power supply under region-specific consideration of challenges and options of integrating variable renewables in the REMIND model. Energy Economics, 64, 665-684. Retrieved from http://www.sciencedirect.com/science/article/pii/S014098831630130X. doi:https://doi.org/10.1016/j.eneco.2016.05.012
28.Wang, Y.-H. (2011). The CO2 Emission Reduction Benefit Assessment of the Supply-Side Policies for Taiwan Power Sector. (Master), National Cheng Kung University Tinan.
29.Wei, Y.-F. (2016). Impact Analysis of Electricity Deregulation in Taiwan – A Supply Side Perspective. (Master), National Cheng Kung University Tainan.
30.Wu, B. (2020). Trend Force: 2020 lithium battery focus: large energy storage systems. Retrieved from https://www.energytrend.com.tw/research/20200226-14317373.html
31.Wu, J.-H., & Huang, Y.-H. (2014). Electricity portfolio planning model incorporating renewable energy characteristics. Applied Energy, 119, 278-287. Retrieved from http://www.sciencedirect.com/science/article/pii/S0306261914000208. doi:https://doi.org/10.1016/j.apenergy.2014.01.001
32.Zerrahn, A., Schill, W.-P., & Kemfert, C. (2018). On the economics of electrical storage for variable renewable energy sources. European Economic Review, 108, 259-279. Retrieved from http://www.sciencedirect.com/science/article/pii/S0014292118301107. doi:https://doi.org/10.1016/j.euroecorev.2018.07.004
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