由於可再生能源發展和空氣污染的區域性和季節性，能源轉型有了新的挑戰。大多數研究涉及核能發電、可再生能源技 術創新和發電廠所造成的污染。能源轉型需要建立一個長期穩定的規劃，需要考量許多因素。長期能源規劃發展如何兼顧經濟和環境指標？考慮到社會公平和居民的健康，如何在能源轉型期間減少空氣污染成為一個關鍵問題。本研究的目的是製定跨區域和季節性電力規劃模型（CRSPPM），考慮發電成本和污染、決定季節、區域和空氣污染因素下的電力混合配比和調度。我們還將模型分為兩部分，以解決可再生能源和電力需求的不確定性。採用模糊邏輯控制來處理模糊問題。以台灣為例研究，結果表明跨越地區的電力傳輸可以有效地實現區域電力平衡，有利於穩定整體電力平衡。可再生能源將的發展也會有不同的表現，新增的非再生能源裝置量具有更好的發電效率，還將進行敏感性分析以在決策者的偏好改變時提供建議。該研究可以作為台灣政策制定者在電力規劃、調度和管理方面的決策支持系統。此外，該模型可以應用於正在經歷經濟發展和環境保護困境的其他國家。

Due to the regional and seasonal nature of renewable energy development and air pollution, new challenges in energy transition are generated. Researches on technological innovation of renewable energy, pollution reduction of power plants, and nuclear power generation have been continuously explored. However, only a few of them tackled long term energy planning development by considering minimization of economic impacts with mitigation of air pollution during energy transition. Therefore, the aim of this study is to develop a Cross-Regional and Seasonal Power Planning Model (CRSPPM) with compromise solutions and to decide the power generation configuration and dispatch under seasonal, regional and air pollution factors in addition to cost. We also divided our model into two parts to address the uncertainty of renewable energy and power demand. We also use Fuzzy logic control to handle fuzzy problems. Based on empirical study of Taiwan, the results show that the transmission of electricity across regions can effectively achieve regional power balance, which is conducive to stabilize the overall power balance. Renewable energy will mitigate the emissions, and the minor increase in non-renewable energy with better equipment has better power generation efficiency that will also mitigate the emissions. A sensitivity analysis is conducted to provide suggestions when decision makers’ preference change. This study can serve as a decision support system for policymakers in Taiwan on power planning, dispatch and management. Further, this model can be applied to other countries that are experiencing economic development and environmental protection dilemmas.

Table of content
Highlight 2
Abstract 3
中文摘要 5
1. Introduction 11
2. Literature Review 15
2.1 Energy supply planning 15
2.2 Air pollution from Power Generation 17
2.3 Uncertainty of renewable power generation 19
2.4 Compromise programming 22
3. Proposed Cross-Regional and Seasonal Power Planning Model 23
3.1 Mathematical model 1 24
3.1.1. Indices 24
3.1.2. Decision Variables 25
3.1.3. Parameters 25
3.1.4. Objectives 28
3.1.4.1. Objective 1: Minimize cost 28
3.1.4.2. Objective 2: Minimize emissions 29
3.1.5. Compromise programming 29
3.1.6. Constraints 30
3.2 Mathematical model 2 32
3.2.1 Additional Parameters 33
3.2.2 Model 2 objectives 33
3.2.2.1 Objective 1: Minimize cost 34
3.2.2.2 Objective 2: Minimize emissions 34
3.2.3 Constraints 34
3.2.4 Fuzzy logic control 36
3.2.4.1 Membership function 36
3.2.4.2 Rules 37
3.2.4.3 Defuzzificantion 38
4. Case study 40
4.1 Background description 40
4.2 Current policies and assumption 41
4.3 Results and suggestions in model 1 43
4.3.1 Overall electricity generation mix in 2030 43
4.3.2 Regional differences of power generation 46
4.3.3 Seasonal power differences 49
4.3.4 Comparison of emission 53
4.3.5 Suggestions 55
4.4 Results and suggestions in model 2 56
4.4.1 Overall electricity generation mix in 2030 56
4.4.2 Regional differences of power generation 59
4.4.3 Seasonal power differences 61
4.4.4 Comparison of emission 63
4.4.5 Suggestions 64
4.5 Discussion 65
5. Sensitivity Analysis 68
6. Conclusions 71
Reference 73

1.Abdel-Shafy, HI., Mansour, M.S.M., 2016. A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation. Egyptian Journal of Petroleum, 25 (1), 107-123.
2.Almasri, R., Muneer, T., Cullinane, K., 2011. The effect of transport on air quality in urban areas of Syria. Energy Policy, 39(6), 3605-3611.
3.Alvarez-Herranz, A., Balsalobre-Lorente, D., Shahbaz, M., 2017. Energy innovation and renewable energy consumption in the correction of air pollution levels. Energy Policy, 105, 386-397.
4.Ballestero, E., 2007. Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. · European Journal of Operational Research, 182(3),1369-1382.
5.Bogdanov, D., Breyer, C., 2016. North-East Asian Super Grid for 100% renewable energy supply: Optimal mix of energy technologies for electricity, gas and heat supply options. Energy Conversion and Management, 122, 176-190.
6.Bureau of Energy, Ministry of Economic Affairs, 2017
https://www.moeaboe.gov.tw/ECW/populace/news/News.aspx?kind=9&menu_id=4360&news_id=9808 viewed on 12/20/2018.
7.Casisi, M., De Nardi, A., Pinamonti, P., Reini, M., 2015. Effect of different economic support policies on the optimal synthesis and operation of a distributed energy supply system with renewable energy sources for an industrial area. Energy Conversion and Management, 95, 131-139.
8.Chan, CC., 2015. NTU Scholars Highlight Health Hazards Caused by PM2.5 Air Pollutants. https://www.ntu.edu.tw/english/spotlight/2015/475_20150330.html viewed on 12/20/2018.
9.Chang, KH., Chang, LF., Chen, WK., Tsuang, BJ., Lin WY., Lin CH., Lai, HC., Chen, LY., Chen, JH., Liu, TH., Chen, RF., Sheng, YF., Tsai, JL., Kuo, PH., Wang, CT., 2017. The Project of Consolidating the System of Air Quality Model.
10.Chen, HH., Lee, AHI., 2014. Comprehensive overview of renewable energy development in Taiwan. Renewable and Sustainable Energy Reviews, 37, 215-228.
11.Dai, YH., Zhou, WX., 2017. Temporal and spatial correlation patterns of air pollutants in Chinese cities. PLoS ONE, 12, e0182724.
12.Diaz-Balteiro, L., Voces González, R. & Romero, C., 2011. Making sustainability rankings using compromise programming. An application to European paper industry, Silva Fennica, 45(4), 761-773.
13.Eltamaly, AM., Farh, HM., 2013. Maximum power extraction from wind energy system based on fuzzy logic control. Electric Power Systems Research, 97, 144-150.
14.Gils, HC., Scholz, Y., Pregger, T., de Tena, DL., Heide, D., 2017. Integrated modelling of variable renewable energy-based power supply in Europe. Energy, 123, 173-188.
15.Hemmati, R., Saboori, H., Saboori, S., 2016. Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units. Renewable Energy, 95, 74-84.
16.Huang, Y., Yunchang, J.B., Peng, CY., 2011. The long-term forecast of Taiwan's energy supply and demand: LEAP model application. Energy Policy, 39 (11), 6790-6803.
17.Industrial Technology Research Institute (ITRI), 2015. Taiwan 2050 Calculator http://2050.twenergy.org.tw/
18.International Renewable Energy Agency (IRENA), 2018. Global Energy Transformation: A Roadmap to 2050 http://irena.org viewed on 12/20/2018
19.Jaramillo, P., Muller, NZ., 2016. Air pollution emissions and damages from energy production in the U.S.: 2002–2011. Energy Policy, 90, 202-211.
20.Kampa, M., Castanas, E., 2008. Human health effects of air pollution. Environmental Pollution, 151, 362-367.
21.Khodaei,H., Hajiali, M., Darvishan, A., Sepehr, M. &　Ghadimi, N. 2018. Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming, Applied Thermal Engineering, 137(5), 395-405.
22.Killinger, S., Mainzer, K., McKenna, R., Kreifels, N., Fichtner, W., 2015. A regional optimisation of renewable energy supply from wind and photovoltaics with respect to three key energy-political objectives. Energy, 84, 563-574.
23.Kim, Y., Kim, M., Kim, W., 2013. Effect of the Fukushima nuclear disaster on global public acceptance of nuclear energy. Energy Policy, 61, 822-828.
24.Lee, HC., Chang, CT., 2018. Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews, 92, 883-896.
25.Lee, JH., 2014. Energy supply planning and supply chain optimization under uncertainty. Journal of Process Control, 24(2), 323-331.
26.Lin, LF., Lee, WJ., Li, HW., Wang, MS., Chang-Chien, GP., 2007. Characterization and inventory of PCDD/F emissions from coal-fired power plants and other sources in Taiwan. Chemosphere, 68 (9), 1642-1649.
27.Lin, QG., Zhai, MY., Huang, GH., Wang, XZ., Zhong, LF., Pi, JW., 2017. Adaptation planning of community energy systems to climatic change over Canada Journal of Cleaner Production, 143, 686-698.
28.Liu, CH., Lin, SJ., Lewis, C., 2013. Evaluation of NOx, SOx and CO2 Emissions of Taiwan's Thermal Power Plants by Data Envelopment Analysis. Aerosol and Air Quality Research, 13 (6), 1815-1823.
29.Mac Kinnon, M., Zhu, SP., Carreras-Sospedra, M., Soukup, JV., Dabdub, D., Samuelsen, GS., Brouwer, J., 2019. Considering future regional air quality impacts of the transportation sector. Energy Policy, 124, 63-80.
30.Maleki, A., Khajeh, MG., Ameri, M., 2016. Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty. International Journal of Electrical Power & Energy Systems, 83, 514-524.
31.McLeod, H., Langford, IH., Jones, AP., Stedman, JR., Day, RJ., Lorenzoni, I., Bateman, IJ., 2010. The relationship between socio-economic indicators and air pollution in England and Wales: implications for environmental justice. Regional Environmental Change, 1, 78-85.
32.Ming, LL., Jin, L., Li, J., Fu, PQ., Yang, WY., Liu, D., Zhang, G., Wang, ZF., Li, XD., 2017. PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events. Environmental Pollution, 223, 200-212.
33.Nedaei, M., Assareh, E., Walsh, PR., 2018. A comprehensive evaluation of the wind resource characteristics to investigate the short-term penetration of regional wind power based on different probability statistical methods. Renewable Energy, 128, 362-374.
34.Ohunakin, OS., Adaramola, MS., Oyewola, OM., Fagbenle, R., 2015. Solar radiation variability in Nigeria based on multiyear RegCM3 simulations. Renewable Energy, 74, 195-207.
35.Ouammi, A., Dagdougui, H., Sacile, R, Mimet, A., 2010. Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy). Renewable and Sustainable Energy Reviews, 14 (7), 1959-1968.
36.Rabiee, A., Sadeghi, M., Aghaei, J., 2018. Modified imperialist competitive algorithm for environmental constrained energy management of microgrids. Journal of Cleaner Production, 20, 273-292.
37.Roumila, Z., Rekioua, D., Rekioua, T., 2017. Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery. International Journal of Hydrogen Energy, 42 (30),19525-19535.
38.Samal, RK., Kansal, ML., 2015. Sustainable Development Contribution Assessment of Renewable Energy Projects using AHP and Compromise Programming Techniques. International Conference on Energy, Power and Environment: Towards Sustainable Growth.
39.Sandgani, MR., Sirouspour, S., 2018. Energy Management in a Network of Grid-Connected Microgrids/Nanogrids Using Compromise Programming. IEEE Transactions on Smart Grid, 9 (3), 2180-2191.
40.Shapiro, MA., Bolsen, T., 2019. Korean perceptions of transboundary air pollution and domestic coal development: Two framing experiments. Energy Policy, 126, 333-342.
41.Sharma, AP., Ki, HK., Ji, WA., Zang, HS., Jong, RS., Jin, HL., Chang, JM., Brown, Richard J.C., 2014. Ambient particulate matter (PM10) concentrations in major urban areas of Korea during 1996–2010. Atmospheric Pollution Research, 5 (1), 161-169.
42.Sonibare, J.A., 2010. Air pollution implications of Nigeria’s present strategy on improved electricity generation. Energy Policy, 38 (1), 5783-5789.
43.Taiwan Power Company(TPC), 2017
http://www.taipower.com.tw/tc/index.aspx viewed on 12/20/2018.
44.Tan-Soo, JS., Qin, P., Zhang, XB., 2018. Power stations emissions externalities from avoidance behaviors towards air pollution: Evidence from Beijing. Energy Policy, 121, 336-345.
45.Wang, HF., Sung, MP., Hsu, HW., 2016. Complementarity and substitution of renewable energy in target year energy supply-mix planning in the case of Taiwan. Energy Policy, 90, 2016, 172-182.
46.Xie, NM., Yuan, CQ., Yang, YJ., 2015. Forecasting China's energy demand and self-sufficiency rate by grey forecasting model and Markov model. International Journal of Electrical Power & Energy Systems, 66, 1-8.
47.Yorker, B., Leton, TG., Ugbebor, JN., 2017. The Role of Meteorology for Seasonal Variation in Air Pollution Level in Eleme, Rivers State, Nigeria. Journal of Scientific Research & Reports, 17, 1-17.