評估未來臺灣再生能源發電之供電特性

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呂昀儒(Yun-Ru Lu) 查詢紙本館藏

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能源工程研究所

論文名稱

評估未來臺灣再生能源發電之供電特性

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摘要(中)

由於風力發電與太陽光電發電的裝置容量將占所有再生能源的80%以上(經濟部能源局2016 我國再生能源發展政策)，在未來供電系統上扮演重要的角色，因此本研究以此二種能源為主，評估此二種能源可以在未來供電系統上扮演怎樣的角色，探討未來再生能源(風能、太陽能)是否可以成為台灣穩定的電力來源。
本研究以WRF模式模擬2030年風場狀況，再將風場模擬結果以Windographer計算2030年之離岸風力發電量與發電時間分布，在2030年離岸風力發電的預計年發電量為11343百萬度，其中在冬、夏季的月平均發電量分別為1000至1500百萬度及500至600百萬度。在一天當中的小時變化，離岸風力發電在一天當中的發電量較平穩，在午後的發電量較高，小時平均發電量在47萬度左右。在太陽光電的部分，本研究以美國國家航空暨太空總署大氣科學資料中心(NASA, National Aeronautics and Space Administration, Atmospheric Science Data Center )提供之日照量估算2030年太陽光電發電狀況，再透過裝置容量計算2030年太陽光電的年發電量為11367百萬度，太陽光電在夏月平均發電量可以達到1300百萬度上下，在冬季的月發電量只有約夏季月發電量的一半。在一天當中的小時變化部分，一天當中則在11~14點平均小時發電量最高約為350萬度。
本研究在評估未來電力系統的供需平衡以年發電量、月發電量與小時發電量的角度去評估電力系統是否穩定。根據政府目前的能源政策，在年發電量上，預估年備用容量率可以達到15%以上，月發電量在2019~2024年預估月備用容量率無法達到15%，2024年達到最低(8.8%)，在夏季尖峰小時(下午2~3點)的預估備用容量率，在2017~2030，介於-1%至10%，在2024年之電量不足(預估備用容量率為-0.9%)。
本研究也根據不同的太陽光電與離岸風力發電的工程設置進度分別為25%、50%及75%來討論未來電力系統的可靠度，以年或月的發電量來看，在2024年的預估年/月備用容量率最低，即使當進度為75%時，在2024年的預估月備用容量也僅有7.8% (其他年份的預估月備用容量率皆可達到15%)。以夏季尖峰發電小時(下午2~3點)來探討，進度為25%、50%及75%時預估備用容量率在2017~2030年皆無法達到15%，當進度25%時2021~2024都會有缺電問題(預估備用容量率分別為-1.1%、-2.1%、-1.4%及-6.2%)，當進度為50%時2022與2024有缺電的問題(預估備用容量率分別為-0.7%及-4.4%)，進度為75%時則僅有在2024年有缺電的危機(預估備用容量率為-2.7%)。由此可知再生能源的工程進度事關重大。
若將核能一號、二號、三號發電廠延役，進度為25%、50%或75%，雖然年發電量與月發電量，皆可以達到預估備用容量率15%以上，但夏季尖峰小時發電量，進度為25%時，預估備用容量率介於2~15%，在2017年最低(2.9%)，發展進度50%及75%，僅有2025及2030年超過或接近預估備用容量率15%，其它年份的預估備用容量率介於3~10%。若太陽光電與離岸風力的工程設置如期完成，核能一廠、二廠¬及三廠延役，在夏季尖峰小時發電量僅有2025至2030年達到預估備用容量率15%，其他年份的則介於5~11%。若將核能電廠一、二、三與四號機皆啟用，太陽光電與離岸風力的工程設置也如期完成，夏季尖峰小時發電量在2018與2023~2030年可達預估備用容量率15%，其他年份則介於在12%至14%。因此，另尋除風能及太陽光電之外的再生能源或是使用核電，才能確保2030年前發電來源不虞匱乏。

摘要(英)

The capacity of wind power and solar photovoltaic power will account for more than 80% of all renewable energy in 2030, according to the Ministry of Economic Affairs and Energy 2016 renewable energy development policy. These two renewable energies will play an important role in the future power supply system. Therefore, the aim of this study is to assess the role of these two energy sources and to explore whether wind and solar power can become a stable source of electricity for Taiwan in the future power supply systems.
In this study, the WRF model was first used to simulate the wind field in 2030. The simulation results were then used to calculate the magnitude and spatial distribution of offshore wind power generation in 2030 by using Windographer software. The estimated annual generation of offshore wind power by 2030 is 11343 GWh, of which the average monthly power generation in winter and summer is 1000 to 1500 GWh and 500 to 600 GWh, respectively. In the course of a day, the hourly change in wind power generated by off-shore wind power was relatively steady. In the afternoon, the wind power generation was high, with an average generating capacity of 470 MWh. For solar photovoltaic, this study estimates the solar photovoltaic power generation in 2030 by the solar radiation data provided by the National Aeronautics and Space Administration (NASA). The estimated annual generation of solar photovoltaic power in 2030 is 11367 GWh, the average generating capacity of solar photovoltaic in the summer months can reach 1300 GWh. In winter months the solar power generation is only about half of that in summer months. For the hourly variation in the day, the solar power generation reaches to the maximum of about 3.5 GWh during 11 a.m.- 2 p.m.
This study evaluates the stability of the power system in terms of annual, monthly, and hourly power generation and assesses the balance of future supply and demand of power systems. According to the current energy policy of the government, the estimated annual percent reserve margin (PRM) can reach 15% or more in annual power generation. The monthly estimated PRM cannot reach 15% in 2019-2024, especially only 8.8% in 2024 , estimated PRM during summer rush hours, 2 to 3 p.m., ranging from -1% to 10% between 2017 and 2030 and below zero in 2024 ,estimated percent reserve margin of -0.9 %.
This study also discusses the stability of future power systems based on the progress of solar photovoltaic and offshore wind power projects set at 25%, 50% and 75%, respectively. In terms of annual or monthly power generation, the estimated annual and monthly PRM are the lowest in 2024, even at 75%, estimated monthly percent reserve margin in 2024 is only 7.8%, estimated monthly PRM for other years are up to 15%. In the summer rush hours, 2 to 3p.m., for the progress of 25%, 50%, and 75% the estimated PRM in 2017-2030 cannot reach 15%. When the progress of 25%, there will be power shortage problems in 2021-2024, estimated PRM of -1.1%, -2.1%, -1.4% and -6.2%. When the progress is 50%, the 2022 and 2024 have a power shortage problem, estimated PRM are -0.7% and -4.4%, respectively. At 75%, there is only a shortage of electricity in 2024, estimated PRM of -2.7%. This shows how important the completion of renewable energy project is.
If the first, second, and third nuclear power plants extended service to 2030, the estimated annual or monthly PRM can still reach 15% no matter the of progress of renewable energy projects is 25%, 50% or 75%. However, when we consider the summer rush hour at the progress of 25%, the estimated PRM ranged from 2% to 15%, lowest, 2.9%, in 2017, only exceeding or approaching 15% at progress of 50% and 75%, in 2025 and 2030, and between 3 ~ 10% in other years. If the solar photovoltaic and offshore wind projects are completed on schedule, plus the first, second, and third nuclear power plants extended service to 2030, the hourly PRM can reach 15% during summer rush hours from 2025 to 2030 and from 5 to 11% for other years. If the first, second, third, and fourth nuclear power plants are enabled during 2017-2030 plus solar photovoltaic and offshore wind engineering settings are completed on schedule, the estimated summer rush hour PRM is up to 15% in 2018 and 2023-2030 and range from 12% to 14% in other years. Therefore, in addition to wind energy and solar photovoltaic, looking for the other renewable energy besides or using nuclear power will ensure sufficiency of power generation before 2030.

關鍵字(中)

★ 離岸風力
★ 太陽光電
★ 備用容量率
★ WRF

關鍵字(英)

★ offshore wind power
★ solor photovoltaic
★ percent reserve margin
★ WRF

論文目次

目錄
摘要 I
Abstract III
目錄 V
圖目錄 VII
表目錄 IX
第一章前言 1
1.1 研究緣起 1
1.2 研究目的 3
第二章文獻回顧 5
2.1再生能源發展與策略的重要性 5
2.2風力發電 9
2.2.1風力發電的特性 9
2.2.2 臺灣目前風力發電發展狀況與策略 11
2.3 太陽光電發電 15
2.3.1太陽光電的特性 15
2.3.2 臺灣目前太陽光電發電發展狀況與策略 17
2.4 風能評估方法 19
2.5 太陽能評估方法 21
2.6模式模擬應用 23
第三章研究方法 25
3.1 研究流程圖 25
3.2資料蒐集 25
3.2.1 氣象資料觀測資料蒐集 25
3.2.2 太陽光電日照資料 25
3.3模式模擬方法 27
3.3.1模擬的時間 27
3.3.2風場模擬 –ARW-WRF 27
3.4 模式模擬資料評估方法 31
3.4.1 WRF模式氣象評估 33
3.5風能評估方法與驗證 35
3.5.1風能密度 35
3.5.2風力發電機選擇 35
3.5.3 風力發電過程中的能源損耗 37
3.5.4 Windographer 軟體風能計算 39
3.6太陽能評估方法 41
3.6.1 太陽能計算公式 41
3.6.2 日照量資料 41
3.6.3 太陽能發電過程中的能源損耗 43
3.7 臺灣未來需電量評估 45
第四章結果與討論 51
4.1 能源評估驗證 51
4.1.1 WRF氣象模式評估驗證 51
4.1.2 風力發電能源評估驗證 59
4.1.3 太陽能評估驗證 63
4.2 2030年再生能源評估 65
4.2.1 WRF 2030年氣象模擬結果 65
4.2.2 2030年離岸風能評估 69
4.2.3 2030年太陽能發電評估 81
4.3 評估2030年再生能源扮演角色與電力供給狀態 83
4.3.1 未來太陽能發電與離岸風力發電與臺灣電力供需關係 83
4.3.2評估臺灣2030年離岸風力與太陽光電發電的穩定性 91
4.3.3評估在不同供電結構的供電狀態 97
4.3.4未來再生能源發展狀況的情境討論 107
第五章結論與建議 133
5.1 結論 133
5.2未來建議 137
參考文獻 139
附錄一口試委員意見答覆 143

參考文獻

中國氣象局，2006，中國人民共和國氣象行業標準-太陽能資源評估方法。
台灣土地開發公司，2013。太陽光電發電系統發電效益分析與保養維護說明。
行政院環保署，2003，空氣品質模式模擬規範。
經濟部能源局，2016，2016年能源技術產業白皮書。
經濟部能源局，2016，我國再生能源發展政策。(中華生質能學會第16屆第2次研討會)
臺灣電力公司，2012，風力工程與運維。
臺灣電力公司，2017，106年台電長期電源開發方案
Archer, C.L., Jacobson, M.Z., 2013. Geographical and seasonal variability of the global “practical” wind resources. Applied Geography 45,119-130.
AWS Scientific, Inc, 1997. Wind Resource Assessment Handbook. National Renewable Energy Laboratory.
Benjamin, S.G., Brown, J.M., Brundage, K.J., Schwartz, B.E., Smirnova, T.G., Smith, T.L., Morone, L.L., 1998. NWS Technical Procedures Bulletin 448 RUC-2 - The Rapid Update Cycle Version 2. NOAA/OAR Forecast Systems Laboratory.
Boudia, S.M., Benmansour, A., Ghellai, N., Benmdjahed, M., Hellal, M.A.T., 2012. Temporal assessment of wind energy resource in algerian highlands regions. Revue des Energies Renouvelables 15, 43-55.
Buttler, A.l., Dinkel, F., Franz, S., Spliethoff, H., 2016. Variability of wind and solar power : An assessment of the currentsituation in the European Union based on the year 2014. Energy 106, 147-161.
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F.P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A.A., Zhang, C., 2011. The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. International Journal of Climatology 31, 273–288.
Chen, H.H., Lee, A.H.I., 2014.Comprehensive overview of renewable energy development in Taiwan. Renewable and Sustainable Energy Reviews 37, 215-228.
Cotton, W.R., Pielke Sr, R., Walko, R., Liston, G., Tremback, C., Jiang, H., McAnelly, R., Harrington, J., Nicholls, M., Carrio, G., 2003. RAMS 2001: Current status and future directions. Meteorology and Atmospheric Physics 82, 5-29.

Dudhia, J., 1993. A nonhydrostatic version of the Penn State-NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Monthly Weather Review 121, 1493-1513.
Emery, W.J., Baldwin, D.J., Schlüssel, P., Reynolds, R.W., 2001. Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements. Geophysical Research 106, 148-227.
Fang, H.F., 2014.Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago. Renewable Energy 67, 237-241.
Global Wind Energy council, 2017.Global Wind Statistics 2016 .
Hsieh, C.H., Dai, C.F., 2012. The analysis of offshore islands wind characteristics in Taiwan by Hilbert–Huang transform. Wind Engineering and Industrial Aerodynamics 107–108, 160–168.
REN21, 2016. Renewables 2016 global status report.
IRENA, 2016. Roadmap for a renewable energy future.
Ko, L., Wang, J.C., Chen, C.Y., Tsai, H.Y., 2015. Evaluation of the development potential of rooftop solar photovoltaic in Taiwan. Renewable Energy 76, 582-595.
Li, J.H., Guo Z.H., Wang, H.J., 2014. Analysis of Wind Power Assessment Based on the WRF Model. Atmospheric and Oceanic Science Letters 7, 126-131.
Li, M., Zhang, Q., Kurokawa, J., Woo, J.H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D., Carmichael, G., 2015. MIX: a mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects. Atmospheric Chemistry and Physic 15, 34813–34869.
Lima, L.D.A., Filho, C.R.B., 2011. Wind resource evaluation in São João do Cariri (SJC) – Paraiba, Brazil. Renewable and Sustainable Energy Reviews 16, 474-480.
Lin, C.J., Yu, O.S., Chang, C.N., Liu, Y.H., Chuang, Y.F., Lin, Y.L., 2009. Challenges of wind farms connection to future power systems in Taiwan. Renewable Energy 34, 1926–1930.
Liu, S.Y., Perng, Y.H., Ho, Y.F., 2013. The effect of renewable energy application on Taiwan buildings: What are the challenges and strategies for solar energy exploitation? Renewable and Sustainable Energy Reviews 28, 92–106.
Lu, S.D., Ho, W.C., Lu, W.H., Hu, C.K., Chen, M.L., Lien, Y.S., 2015. A research on the potential energy of offshore wind power and preferable offshore blocks in Taiwan. 中華民國第三十六屆電力工程研討會

Nnawuike, N. E., Emmanuel C.O., 2014. Assessment of Wind Energy Potential as a Power Generation Source in Five Locations of South Western Nigeria. Journal of Power and Energy Engineering 2, 1-13.
Skamarock, W.C., Klemp, J.B., 2008. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics 227, 3465-3485.
Upreti, B.N., Shakya, A., 2012. Wind energy potential assessment in Nepal. Tribhuvan University and Government of Nepal.
Worley, P.H., Craig, A.P., Dennis, J.M., Mirin, A.A., Taylor, M.A., Vertenstein, M., 2011. Performance of the Community Earth System Model. Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. Article 54.

指導教授

莊銘棟(Ming-Tung Chuang)

審核日期

2017-11-15

推文