近年來，隨著科技進步，人們對於電力需求逐年上升。但是傳統火力發電之能源使用率低且排放大量汙染氣體，是造成能源枯竭危機和溫室效應日益嚴重之主因。而冷熱電供聯系統 (Combined Cooling, Heating and Power system, CCHP) 之微電網架構，因其主要發電機之燃料是天然氣，排放之汙染氣體相較於傳統火力發電燃煤或燃油的方式少，且發電產生之廢熱能夠回收利用，使得其能源使用率高。在當今環保意識抬頭，世界各國都在追求永續經營的理念的趨勢下，如何應用CCHP發電並同時達到節能減碳是重要的研究議題。
CCHP之最佳化問題，是透過調整CCHP系統內各個機台在每個時段之功率，考慮在不同的負荷需求條件下，以最低經濟成本、消耗最少一次性能源和最低汙染成本等作為目標，求得品質最優之最佳解。而隨著啟發式演算法日益成熟，近年許多相關文獻開始利用啟發式演算法求解CCHP最佳化問題，本文也將採用啟發式演算法以求得到更優秀之最佳解。
因應台灣近年來致力推動離島地區微電網及再生能源的發展，且台灣於民國102年起實行建置金門低碳島計畫，本篇論文因此探討的是CCHP最佳化問題。本研究將採用CCHP結合再生能源之微電網架構，以台灣金門縣烈嶼鄉西口村做為案例分析，以混和簡化群差異進化演算法(Simplified Swarm Optimization with Differential Evolution, SSO-DE)，結合逼近理想解排序法 (Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS) 來表示營運成本、碳排放量及能源轉換率之權衡關係。實驗結果將與其他演算法進行比較。實驗結果顯示，本研究所提出之研究方法能夠有效求解CCHP之最佳化問題。且相對於傳統能源供應，能夠取得十分優秀的成效，也提供給決策者一個評估CCHP系統效益的標準。

In recent years, with the advancement of science and technology, demand of energy has increased year by year. However, the low energy utilization and large amount of polluting gases emission of traditional thermal power system are the main causes of energy exhaustion and greenhouse effect. Therefore, the awareness of environmental protection raises, and we need a more effective way to generate power. Hence, combined cooling, heating and power system (CCHP) is discussed because it is more effective and environmental protective than traditional thermal power system.
The optimization problem of CCHP is to adjust the power of each machine in the CCHP system at each time period, considering the lowest economical cost, the lowest carbon dioxide emission and the highest primary energy utilization under different load demand conditions. Nevertheless, by improving of heuristic algorithm, more and more related literature applies it for solving optimization problem of CCHP. As a consequent, the heuristic algorithm will be adopted in this paper for obtaining better solution.
In this paper, in response to promote the development of micro-grid and renewable energy in the outlying islands by Taiwan government recently, we use CCHP hybrid renewable energy system (RECCHP) as a model, taking Jinmen county as a case study. Further, we present the Simplified Swarm Optimization with Differential Evolution (SSO-DE). In addition, we apply TOPSIS method to trade off the operation cost, carbon emission and primary energy utilization. Finally, the experimental results show that the proposed method SSO-DE is effective on the RECCHP problem, and also, it can provide decision makers with a standard for evaluating the effectiveness of the RECCHP system.

摘要 -------------------------- 1
英文摘要 ----------------------- 2
圖目錄 ----------------------- 5
表目錄 ----------------------- 6
第一章、 緒論 --------------- 8
1.1 研究背景與動機 ------- 8
1.2 研究目的 --------------- 12
1.3 研究架構 --------------- 16
1.4 縮略語、符號、指標------- 18
第二章、 文獻回顧 ------- 20
2.1 微電網 --------------- 20
2.1.1風力發電系統 ------- 21
2.1.2太陽能發電系統 ------- 22
2.1.3分布式能源系統 ------- 23
2.2冷熱電供聯系統 ------- 24
2.3簡化群體演算法 ------- 25
2.4改良式簡化群體演算法 ------- 26
2.5 差異進化演算法 ------- 28
2.6 TOPSIS --------------- 29
第三章、 問題描述 ------- 31
3.1 決策變數 --------------- 31
3.2 目標式 --------------- 31
3.3 限制式 --------------- 34
第四章、 研究方法 ------- 38
4.1前置資料取得 --------------- 38
4.2解編碼方式 --------------- 39
4.3利用TOPSIS法評估適應值函數---- 42
4.4簡化群體演算法 ------- 44
4.5差異進化區域演算法 ------- 48
4.6簡化群差異進化演算法 ------- 53
第五章、 實驗結果與分析 ------- 55
5.1實驗設計 --------------- 55
5.1.1春(秋)季節資料實驗設計結果-- 57
5.1.2夏季資料實驗結果 ------- 60
5.1.3冬季資料實驗結果 ------- 62
5.2實驗情境 --------------- 63
5.3實驗結果 --------------- 68
5.4實驗結果分析 --------------- 72
第六章、 結論與未來研究方向------ 77
6.1結論 ----------------------- 77
6.2未來研究方向 --------------- 78
參考文獻 ----------------------- 79

1. Jha, S.K., et al., Renewable energy: Present research and future scope of Artificial Intelligence. Renewable and Sustainable Energy Reviews, 2017. 77: p. 297-317.
2. 2018, B.S.R.o.W.E.J., http://large.stanford.edu/courses/2018/ph241/kumar2/docs/bp-2018.pdf. 2018.
3. Zhang, Y.-R.C., Cheng-Lun; Lee, Yi-Te, The Perspective of Microgrid Technology Development. Journal of Taiwan Energy, 2015.
4. Yang, H., L. Lu, and W. Zhou, A novel optimization sizing model for hybrid solar-wind power generation system. Solar Energy, 2007. 81(1): p. 76-84.
5. Atwa, Y., et al., Optimal renewable resources mix for distribution system energy loss minimization. 2010. 25(1): p. 360-370.
6. Ning, Y., et al. Optimal schedule strategy of battery energy storage systems for peak load shifting based on interior point method. in 2016 12th World Congress on Intelligent Control and Automation (WCICA). 2016.
7. Sigrist, L., E. Lobato, and L. Rouco, Energy storage systems providing primary reserve and peak shaving in small isolated power systems: An economic assessment. International Journal of Electrical Power & Energy Systems, 2013. 53: p. 675-683.
8. Cho, H., et al., Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme. Applied Energy, 2009. 86(12): p. 2540-2549.
9. Cho, H., A.D. Smith, and P. Mago, Combined cooling, heating and power: A review of performance improvement and optimization. Applied Energy, 2014. 136: p. 168-185.
10. Fu, L., et al., Laboratory research on combined cooling, heating and power (CCHP) systems. Energy Conversion and Management, 2009. 50(4): p. 977-982.
11. Li, Y., et al. Multi-objective optimal operation of combined cooling, heating and power microgrid. in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). 2017.
12. Company, T.P., https://www.taipower.com.tw/tc/page.aspx?mid=96. 2020.
13. Environment Protection Bureau, K.C., https://kepb.kinmen.gov.tw/News_Content.aspx?n=C326FCC8544D820C&sms=4FEFD09995C4BB81&s=C3546D66725495D1. 2013.
14. Abdollahi, G. and M. Meratizaman, Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis. Energy and Buildings, 2011. 43(11): p. 3144-3153.
15. Wang, J.-J., Y.-Y. Jing, and C.-F. Zhang, Optimization of capacity and operation for CCHP system by genetic algorithm. Applied Energy, 2010. 87(4): p. 1325-1335.
16. Chen, S.-B., Assessment of Autonomous Renewable Energy System for Kinmen. National Digital Library of These and Dissertations in Taiwan, 2013.
17. Li, K. Constraints of Developing Natural Gas Combined Cooling, Heating and Power (CCHP) Systems. in 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics. 2016. Atlantis Press.
18. Xu, D. and M. Qu, Energy, environmental, and economic evaluation of a CCHP system for a data center based on operational data. Energy and Buildings, 2013. 67: p. 176-186.
19. Soheyli, S., M.H.S. Mayam, and M. Mehrjoo, Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm. Applied Energy, 2016. 184: p. 375-395.
20. Li, L., et al., Analysis of the integrated performance and redundant energy of CCHP systems under different operation strategies. Energy and Buildings, 2015. 99: p. 231-242.
21. Gimelli, A., M. Muccillo, and R. Sannino, Optimal design of modular cogeneration plants for hospital facilities and robustness evaluation of the results. Energy Conversion and Management, 2017. 134: p. 20-31.
22. Mago, P., N. Fumo, and L. Chamra, Performance analysis of CCHP and CHP systems operating following the thermal and electric load. International Journal of Energy Research, 2009. 33(9): p. 852-864.
23. Fang, F., Q.H. Wang, and Y. Shi, A novel optimal operational strategy for the CCHP system based on two operating modes. IEEE Transactions on power systems, 2011. 27(2): p. 1032-1041.
24. Wu, J.-y., J.-l. Wang, and S. Li, Multi-objective optimal operation strategy study of micro-CCHP system. Energy, 2012. 48(1): p. 472-483.
25. Soheyli, S., M.H. Shafiei Mayam, and M. Mehrjoo, Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm. Applied Energy, 2016. 184: p. 375-395.
26. Li, G., et al., Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g. 2018. 11(4): p. 743.
27. Yousefi, H., M.H. Ghodusinejad, and Y. Noorollahi, GA/AHP-based optimal design of a hybrid CCHP system considering economy, energy and emission. Energy and Buildings, 2017. 138: p. 309-317.
28. Yeh, W.-C., W.-W. Chang, and Y.Y. Chung, A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Systems with Applications, 2009. 36(4): p. 8204-8211.
29. Wang, J., et al., Life cycle assessment (LCA) optimization of solar-assisted hybrid CCHP system. Applied Energy, 2015. 146: p. 38-52.
30. Jiayi, H., J. Chuanwen, and X. Rong, A review on distributed energy resources and MicroGrid. Renewable and Sustainable Energy Reviews, 2008. 12(9): p. 2472-2483.
31. Bull, S.R., Renewable energy today and tomorrow. Proceedings of the IEEE, 2001. 89(8): p. 1216-1226.
32. Zeng, J., et al., Multi-objective Optimal Operation of Microgrid Considering Dynamic Loads. 2016. 36(12).
33. Ren, H., et al., Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects. Applied Energy, 2010. 87(12): p. 3642-3651.
34. Sharafi, M. and T.Y. Elmekkawy, Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renewable Energy, 2014. 68: p. 67-79.
35. Parisio, A., E. Rikos, and L. Glielmo, A Model Predictive Control Approach to Microgrid Operation Optimization. IEEE Transactions on Control Systems Technology, 2014. 22(5): p. 1813-1827.
36. Hafez, O. and K. Bhattacharya, Optimal planning and design of a renewable energy based supply system for microgrids. Renewable Energy, 2012. 45: p. 7-15.
37. Mills, A. and S. Al-Hallaj, Simulation of hydrogen-based hybrid systems using Hybrid2. International Journal of Hydrogen Energy, 2004. 29(10): p. 991-999.
38. Aghaei, J. and M.-I. Alizadeh, Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems). Energy, 2013. 55: p. 1044-1054.
39. Katsigiannis, Y.A., P.S. Georgilakis, and E.S. Karapidakis, Hybrid Simulated Annealing–Tabu Search Method for Optimal Sizing of Autonomous Power Systems With Renewables. IEEE Transactions on Sustainable Energy, 2012. 3(3): p. 330-338.
40. Basu, A.K., et al., Planned Scheduling for Economic Power Sharing in a CHP-Based Micro-Grid. IEEE Transactions on Power Systems, 2012. 27(1): p. 30-38.
41. Bhumkittipich, K. and W. Phuangpornpitak, Optimal Placement and Sizing of Distributed Generation for Power Loss Reduction Using Particle Swarm Optimization. Energy Procedia, 2013. 34: p. 307-317.
42. Kong, X.Q., R.Z. Wang, and X.H. Huang, Energy optimization model for a CCHP system with available gas turbines. Applied Thermal Engineering, 2005. 25(2): p. 377-391.
43. Cho, H., et al., Cost-optimized real-time operation of CHP systems. Energy and Buildings, 2009. 41(4): p. 445-451.
44. Mago, P.J. and L.M. Chamra, Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations. Energy and Buildings, 2009. 41(10): p. 1099-1106.
45. Yeh, W.-C., W.-W. Chang, and C.-W. Chiu, A simplified swarm optimization for discovering the classification rule using microarray data of breast cancer.
46. Syukran, M., et al., Image classification technique using modified particle swarm optimization. 2011. 5(5): p. 150-164.
47. Yeh, W., New Parameter-Free Simplified Swarm Optimization for Artificial Neural Network Training and its Application in the Prediction of Time Series. IEEE Transactions on Neural Networks and Learning Systems, 2013. 24(4): p. 661-665.
48. Yeh, W.-C., et al., Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization. International Journal of Electrical Power & Energy Systems, 2014. 55: p. 741-748.
49. Yeh, W.-C., Orthogonal simplified swarm optimization for the series–parallel redundancy allocation problem with a mix of components. Knowledge-Based Systems, 2014. 64: p. 1-12.
50. Yeh, W.-C., An improved simplified swarm optimization. Knowledge-Based Systems, 2015. 82: p. 60-69.
51. Yeh, W.-C., et al., Correction: A New Soft Computing Method for K-Harmonic Means Clustering. PloS one, 2017. 12(1): p. e0169707.
52. Lai, C.-M., W.-C. Yeh, and C.-Y. Chang, Gene selection using information gain and improved simplified swarm optimization. Neurocomputing, 2016. 218: p. 331-338.
53. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11(4): p. 341-359.
54. Shao, W. and D. Pi, A self-guided differential evolution with neighborhood search for permutation flow shop scheduling. Expert Systems with Applications, 2016. 51: p. 161-176.
55. Zou, D.-X., et al. An effective modified differential evolution algorithm for reliability problems. in 2016 4th International Symposium on Computational and Business Intelligence (ISCBI). 2016. IEEE.
56. Hwang, C.-L. and A.S.M. Masud, Multiple objective decision making—methods and applications: a state-of-the-art survey. Vol. 164. 2012: Springer Science & Business Media.
57. Shih, H.-S., H.-J. Shyur, and E.S. Lee, An extension of TOPSIS for group decision making. Mathematical and computer modelling, 2007. 45(7-8): p. 801-813.
58. Lin, M.-C., et al., Using AHP and TOPSIS approaches in customer-driven product design process. Computers in industry, 2008. 59(1): p. 17-31.
59. Bureau of Energy, M.o.E.A., https://www.moeaboe.gov.tw/ECW/populace/content/SubMenu.aspx?menu_id=2224. 2020.
60. Po-Yen Kuo, H.-T.L., A Study on Electrical Consumption Survey and Evaluation System of Residential Buildings. 2005.
61. Bureau, T.C.M., https://opendata.cwb.gov.tw/index. 2020.
62. Teknomo, K., Analytic hierarchy process (AHP) tutorial. Revoledu. com, 2006: p. 1-20.
63. Wu, T., et al. Optimal operation of combined cooling heat and power microgrid with PEVs. in 2015 IEEE Eindhoven PowerTech. 2015. IEEE.
64. Li, G., et al., Multi-objective optimal design of renewable energy integrated CCHP system using PICEA-g. Energies, 2018. 11(4): p. 743.