近年來，於國際發生能源事件及國際協議引導下，節能減碳已成為各國重要的議題，而綠色能源的進步不僅只是提供了替代能源方案，也同時降低了傳統能源製造上對環境的各種汙染；但供應不穩定的綠色能源增加，勢必將對傳統電網造成影響，諸如電力品質、系統可靠度、電力成本…等等，因此能快速反應並調整的微電網逐漸受到重視，如何建構一個能快速反應且能提升能源使用效益的微電網架構，將是目前重要的議題。
本文將傳統微渦輪機、風力發電、太陽能發電、電力儲存系統及冷熱電聯產結合，形成微電網，並將此設計應用進澎湖電力系統，使澎湖電力系統形成微電網的架構，並藉由電力儲存系統來達成需量管理的規劃；以最低發電成本為目標，使用結合模糊規則的蜂群演算法計算發電機組排程及經濟調度問題，除了考慮供需平衡、機組發電量限制、發電機升載率與降載率限制之外，還包含發電機組最低運轉/停機時間及電力儲存系統容量限制等等；為了改善蜂群演算法陷入局部最佳解的問題，本文提出結合機率選取型模糊規則的自適應增強型蜂群演算法，具有跳脫局部最佳解的能力，使問題能更為精確和迅速地獲得整體最佳解。

Under the guidance of international energy event occurred and international agreements, so Energy Saving and Carbon Reduction have already become an important issue in every county. However, the advances in green power not only provided alternative programs, but also reduced environmental pollution when using traditional way to produce energy. As increasing those unstable supply of green power. It must do some impact on traditional power grid. Such as power quality, system reliability, cost of power, etc. Therefore a microgrid which can quick react and dispatch the power demand is taken seriously gradually. How to build a microgrid with quick reaction and enhance power efficiency is an important issue currently.
This thesis combined microturbines, wind power, solar power, power storage system, and combined cooling, heating and power(CCHP) to form a microgrid system. Then applying this design into Penghu power system, and reach the function of demand response by power storage system. For minimum cost of generating power this objective. Using combine fuzzy rule into Bee Swarm Optimization (BSO) to solve the problem of generation unit commitment (UC) and economic dispatch(ED). The UC and ED problem must satisfy the constraints of load demand, generating limits, ramp rate limits, and also the minimum up/down time of generators, and capacity of power storage system, etc. For avoid the local optimality problem, this thesis proposed the utilization of combined Probability Selection Fuzzy Rule into Self-Adaption Enhanced Bee Swarm Optimization (SAEBSO) method, which can quickly reach the optimal solution with better performance and accuracy.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與方法 2
1.3 論文架構 4
第二章 微電網架構設計 6
2.1 微電網設計 6
2.1.1 微電網架構 7
2.1.2 微電網運轉特性及相關技術問題 14
2.2 電池儲能系統於微電網的重要性 16
2.3 冷熱電聯產應用於微電網 18
第三章 微電網系統機組排程架構及數學模型 21
3.1 系統機組排程問題描述 21
3.1.1 機組排程之目標函數 22
3.1.2 機組排程之等式限制式及不等式限制式 24
3.1.3 機組排程之機組成本 29
3.1.4汽電共生機組發電模型 29
3.1.5 冷熱電聯產應用的熱能轉換數學模型 30
3.2 微電網機組排程架構 32
第四章 改良型蜂群演算法之設計 34
4.1 機率選取型模糊理論設計 34
4.1.1 前言 35
4.1.2 應用於機組排程的機率選擇法設計 42
4.2 改良型蜂群演算法之設計 49
4.2.1 前言 49
4.2.2 改良型蜂群演算法之設計概念 60
4.2.3 時變性質加速係數增強型蜂群演算法之設計 60
4.2.4 自適應增強型蜂群演算法之設計 62
第五章 系統測試與案例分析 66
5.1 結合機率選取型模糊規則的自適應增強型蜂群演算法之收斂測試 68
5.2 澎湖系統案例測試 80
5.2.1 案例一：澎湖電力系統夏季最高單日負載測試 88
5.2.2 案例二：澎湖電力系統夏季單日最高負載測試(再生能源兩倍) 93
5.2.3 案例三：澎湖電力系統冬季最低單日負載測試 99
5.2.4 案例四：澎湖電力系統冬季單日最低負載測試(再生能源兩倍) 105
5.2.5 冷熱電聯產測試及效益比較 111
5.3 本章總結 116
第六章 結論與未來發展方向 117
6.1 結論 117
6.2 未來發展方向 118
參考文獻 119

[1] Peza RD, Bernal-Agustína JL, Contrerasb J, “Optimization of control strategies for stand alone renewable energy systems with hydrogen storage,” International Journal of Renewable Energy, vol. 32, pp. 1102–26, 2007.
[2] Soroudi A, Ehsan M, Zareipour H, “A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources,” International Journal of Renewable Energy, vol. 36, pp. 179–88, 2011.
[3] Zangeneh A, Jadid Sh, Rahimi-Kian A, “Promotion strategy of clean technologies in distributed generation expansion planning, ” International Journal of Renewable Energy, vol. 34, pp. 2765–73, 2009.
[4] Jiayi H, Chuanwen J, Rong X, “A review on distributed energy resources and microgrid,” J Renewable and Sustainable Energy Reviews, vol. 12, pp. 2472–83, 2008.
[5] Lee YD, Chang YR, Chan CM, Ho YH, “Preliminary implementation of microgrid with photovoltaic and microturbine for stand alone operation,” IEEE Industry Applications Society Annual Meeting (IAS), 2012.
[6] Moghaddam AA, Seifi A, Niknam T, “Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study,” Renewable and Sustainable Energy Reviews, pp. 1268–1281, 2012.
[7] 王漢威，「應用智慧型轉速與旋角控制器於離岸式風力發電系統」，碩士論文，國立中山大學電機工程研究所，民國一百零三年七月。
[8] “Tesla Motors Incorporation”, 2015/5, http://www.teslamotors.com/
[9] 「火力營運指標」，台灣電力公司，2015/4, http://www.taipower.com.tw/content/new_info/new_info-14.aspx?LinkID=6
[10] “Capstone C1000 Megawatt Power Package High-pressure Natural Gas”, Capstone Turbine Corporation.
http://www.capstoneturbine.com/_docs/datasheets/C1000 HPNG_331044F_lowres.pdf
[11] “BOSCH Combined heat and power plants”, BOSCH Industrial, 2015.
http://www.bosch-industrial.com/en/product-page/combined-heat-and-powerplants/
[12] Z. Qin, R. Frowd, A. Papalexopoulos, D. Lamb, and E. Ledesma, “Minimizing Market Operation Costs Using A Security-Constrained Unit Commitment Approach,” IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, pp. 1-7, 2005.
[13] 「再生能源發電概況」，台灣電力公司，2015/4。
http://www.taipower.com.tw/content/new_info/new_info-b34.aspx?LinkID=8
[14] Thammasorn C, “Generation Unit Commitment in Microgrid with Renewable Generators and CHP,” Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2013.
[15] Farag A, Al-Baiyat S, Cheng TC, “Economic load dispatch multiobjective optimization procedures using linear programming techniques, ” IEEE Proceedings Generation, Transmission and Distribution, vol.10, pp. 731–8, 1995.
[16] S. Jalilzadeh and Y. Pirhayati, “An Improved Genetic Algorithm for Unit Commitment Problem With Lowest Cost,” IEEE International Conference on ICIS Intelligent Computing and Intelligent Systems, pp. 571-575, 2009.
[17] Lee KY, Yome AS, Park JH, “Adaptive Hopfield neural networks for economic load dispatch, ” IEEE Proceedings Generation, Transmission and Distribution, vol. 13, no. 2, pp. 519–26, 1998.
[18] A. Pathom, K. Hiroyuki, T. Eiichi, and H. Jun, “A hybrid EP and SQP for dynamic economic dispatch with non-smooth fuel cost function,” IEEE Proceedings Generation, Transmission and Distribution, vol. 17, no. 2, pp. 411–416, May 2002.
[19] K. Abookazemi and M. W. Mustafa, “Unit Commitment Optimization Using Improved Genetic Algorithm,” IEEE Bucharest Power Technology, pp. 1-6, 2009.
[20] 洪語禪，「微電網內蓄電池儲能系統裝置最佳化」，碩士論文，國立台灣海洋大學電機工程研究所，民國一百零三年二月。
[21] 黃詩婷，「使用基因演算法進行微電網前一日機組排程」，碩士論文，國立台灣海洋大學電機工程研究所，民國一百三年六月。
[22] 劉庭瑋，「台灣電力系統結合碳交易市場之可行性評估」，碩士論文，國立中山大學電機工程研究所，民國一百零二年六月。
[23] Rifaat RM, “Economic dispatch of combined cycle cogeneration plants with environmental constraints,” Proceedings International Conference on Energy Management and Power Delivery, vol.1, pp. 149–53, 1998.
[24] Zhang XJ, Karady GG, Ariaratnam ST, “Optimal Allocation of CHP-Based Distributed Generation on Urban Energy Distribution Networks,” IEEE Transactions on Sustainable Energy, vol. 5, no. 1, Jan. 2014.
[25] 陳智堯，“整合基因演算法及田口方法於火力機組排程之研究”，碩士論文，國立中山大學電機工程研究所，民國九十五年六月。
[26] D. Karaboga and B. Akay, “A comparative study of artificial bee colony algorithm,” International Journal of Applied Mathematics and Computer, vol. 214, pp. 108–132, Aug. 2009.
[27] Song YH, Wang GS, Wang PV, Johns AT, “Environmental/economic dispatch using fuzzy logic controlled genetic algorithms” IEEE Proceedings Generation, Transmission and Distribution, vol. 144, no. 4, pp. 377–82, 1997.
[28] P. Lučić, D.Teodorović, “Bee System: Modeling Combinatorial Optimization Transportation Engineering Problems by Swarm Intelligence,” in Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis. Sao Miguel, Azores Islands, Portugal, pp. 441-445, 2001.
[29] Chen, Yumin, Miao, Duoqian and Wang, Ruizhi, “A Rough Set Approach to Feature Selection Based on Ant Colony Optimization,” Elsevier, vol. 31, 2010.
[30] Gaing Z-L. “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Proceedings Generation, Transmission and Distribution, vol. 18, no. 3, pp. 1187–95, 2003.
[31] Mahor A, Prasad V, Rangnekar S, “Economic dispatch using particle swarm optimization: a review,” Renewable and Sustainable Energy Reviews, vol. 13, pp. 2134–41, 2009
[32] Y. Wang, J. Zhou, H. Qin, and Y. Lu, “Improved chaotic particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects,” Energy Conversion and Management, vol. 51, no. 12, pp. 2893–2900, 2010.
[33] T. O. Ting, M. V. C. Rao, and C. K. Loo, “A Novel Approach for Unit Commitment Problem Via An Effective Hybrid Particle Swarm Optimization,” IEEE Transactions on Power Systems, vol. 21, pp. 411-418, 2006.
[34] X. Yuan, A. Su, Y. Yuan, H. Nie, and L. Wang, “An improved PSO for dynamic load dispatch of generators with valve-point effects,” Energy, vol. 34, no. 1, pp. 67–74, 2009.
[35] R. Akbari, A. Mohammadi, and K. Ziarati, “A novel bee swarm optimization algorithm for numerical function optimization,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 10, pp. 3142–3155, 2010.
[36] X. S. Yang, “Engineering optimizations via nature-inspired virtual bee algorithms,” Lecture Notes in Computer Science, Springer, Berlin, Germany, pp. 317–323, 2005.
[37] 林峙良，「人工蜂群演算法使用隨機搜尋與隨機鄰點搜尋機制之比較與應用」， 碩士論文，國立中央大學土木工程研究所，民國一百零二年六月。
[38] Taher Niknam, Faranak Golestaneh, “Enhanced Bee Swarm Optimization Algorithm for Dynamic Economic Dispatch,” IEEE Systems Journal, vol. 7, no. 4, Dec. 2013.