近年來，於國際發生能源事件及國際協議引導之下，節能減碳已成為各國重要的議題，而再生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。太陽能和風力發電是全球再生能源持續發展的關鍵。然而，再生能源會受到地理因素、環境和氣候的影響，其發電具有間歇性、波動性和不確定性的特點。因此，如何減少再生能源對於系統供電不穩定、利用儲能電池提高再生能源的使用效益，尤關重要。
本文提出雙重粒子群優化法，用以計算兩大案例，第一部分以發電業者最小成本機組排程為目標，計算原始系統、加入太陽能與風力發電之再生能源、加入儲能電池三種情形下，各個調度結果與分析比較；第二部分以再生能源業者的角度，計算在得到最大售電獲利為目標下，再生能源與儲能電池系統的最佳電力能量保證值，包括在整日、尖峰與三段式電力能量保證的探討。最後模擬台灣電力系統的電價成本，實現再生能源業者銷售電力的最大利潤。

In recent years, under the guidance of international energy events and international agreements, energy conservation and carbon reduction have become an important issue in various countries. The progress of renewable energy not only provides alternative energy solutions, but also reduces the environmental protection of traditional energy manufacturing. Solar and wind power are the key to the continued development of renewable energy. However, renewable energy is affected by geographical factors, environment and climate. Its power generation is characterized by intermittent, volatility and uncertainty. Therefore, how to reduce the energy consumption of the system which is unstable, and how to use the energy storage battery to improve the use efficiency of renewable energy are all important.
In this paper, an dual particle swarm optimization algorithm is proposed to calculate two cases. The first part is to calculate the lowest cost by power producer. Calculate the original system, add the renewable energy of solar energy and wind power generation, and add energy storage batteries in three cases. Compare the results of each scheduling and analyze it. The second part is to calculate the maximum sales profit by renewable energy industry. Calculate the power energy guarantee value of the renewable energy and storage battery system. It includes the discussion of all-day, peak and three-stage power energy guarantees. Finally, applying this design into electricity cost of Taiwan power system to realize the maximum profit of the renewable energy industry.

審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究方法與目的 2
1.3 論文架構 3
第二章 系統機組排程架構 5
2.1 前言 5
2.2 系統機組排程之目標函式 5
2.2.1 目標函式 5
2.2.2 機組排程之等式限制式 6
2.2.3 不等式限制式 7
第三章 儲能電池應用之描述與最佳電力能量保證值 9
3.1 電池儲能系統 9
3.1.1 前言 9
3.1.2 電池儲能系統之重要性 9
3.1.3 再生能源結合儲能電池售電效益最大化 11
3.1.4 目標函式與電池之等式限制式及不等式限制式 11
3.2 保證電力能量售電 13
3.2.1 整日電力能量保證 14
3.2.2 尖峰時段電力能量保證 15
3.2.3 三段式電力能量保證 17
第四章 雙重粒子群優化法 20
4.1 前言 20
4.2 粒子群演算法 20
4.2.1 概述 20
4.2.2 參數分析 24
4.2.3 離散粒子群優化演算法 25
4.3 雙重粒子群優化法之設計 26
4.3.1 離散粒子群演算法確定外層機組排程 26
4.3.2 連續粒子群演算法進行內層電量分配 31
4.3.3 演算法優點 32
4.3.4 機組排程問題的演算法實現與流程圖 33
第五章 案例測試與結果分析 36
5.1 案例一：10機組調度系統 36
5.1.1 以發電業者最小成本的機組排程為目標函式 38
5.1.2 以再生能源業者的最佳售電獲利為目標函式 42
5.2 案例二：台電系統 48
5.2.1 再生能源以台電三段式電價售電 49
5.2.2 再生能源以電力成本(單純燃料成本)售電 54
第六章 結論與未來方向 60
6.1 結論 60
6.2 未來發展方向 61
參考文獻 62

[1] GWEC, Global Wind Report Annual Market Update 2016.
[2] 台電網站：資訊揭露-發電資訊-再生能源發電概況-再生能源發電簡介http://www.taipower.com.tw/content/new_info/new_info-b36-1.aspx?LinkID=8.
[3] Z. Qin, R. Frowd, A. Papalexopoulos, D. Lamb, and E. Ledesma, “Minimizing Market Operation Costs Using A Security-Constrained Unit Commitment Approach”, IEEE/PES and Distribution Conference and Exhibition: Asia and Pacific, pp. 1-7, 2005.
[4] A.J. Wood BFW. Power Generation, Operation and Control, 2nd Ed. New York: Wiley, 1996.
[5] R.R. Shoults, S.V. Venkatesh, S.D. Helmick, G.L. Ward and M.J. Lollar, “A dynamic programming based method for developing dispatch curves when incremental heat rate curves are non-monotonically increasing”, IEEE Trans. on Power System, vol. PWRS-1, no. 1, pp. 10-16, 1986.
[6] A. G. Bakirtzis, P. n. Biskas, C. E. Zoumas, and V. Petridis, “Optimal Power Flow by Enhanced Genetic Algorithm”, IEEE on Power Systems, vol. 14, no. 2, pp. 229-236, May 2002.
[7] J. Yuryevich, and K. P. Wong, “Evolutionary Programming Based Optimal Power Flow Algorithm”, IEEE on Power Systems, vol. 14, pp. 1245-1250, Nov. 1999.
[8] Taher Niknam, Faranak Golestaneh, “Enhanced Bee Swarm Optimization Algorithm for Dynamic Economic Dispatch”, IEEE Systems Journal, vol. 7, no. 4, Dec. 2013.
[9] 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.
[10] Li C, Johnson RB,Svoboda AJ, ”A New Unit Commitment Method”, IEEE PowerSystems, vol. 12, no. 1, pp. 113-119 , 1997.
[11] Florian Helling, Stefan Götz, Thomas Weyh, “A battery modular multilevel management system for electric vehicles and stationary energy storage systems”, IEEE on Power Electronics, pp.1-10, 2014.
[12] http://pv.energytrend.com.tw/news/20151019-12378.html
[13] K. Abookazemi and M. W. Mustafa, “Unit Commitment Optimization Using Improved Genetic Algorithm”, IEEE Bucharest Power Technology, pp. 1-6, 2009.
[14] 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。
[15] 葉泰和, "台灣風場風能評估方法分析," 新能源施工處. http://www.hvac-net.org.tw/archive/100052733.pdf.
[16] J. Kennedy, R. C. Eberhart, “Particle swarm optimization. Neural Networks”, IEEE International Conference, pp. 1942-1948, 1995.
[17] Z-H. Zhan, J. Zhang, Y. Li, H.S-H. Chung, “Adaptive Particle Swarm Optimization”, IEEE on Systems, Man, and Cybernetics, vol. 39, no. 6, pp. 1362-1381, 2009.
[18] J. Kennedy, and R. C. Eberhart, “Empirical study of particle swarm optimization”, Proceedings of the 1999 Congress Evolutionary Computation, vol. 3, 1999.
[19] Shi Y, R. C. Eberhart, “Empirical Study of Particle Swarm Optimization”, Congresson Evolutionary Computation, CEC 1999, pp. 1945-1950, July 6, 1999-July 9, 1999.
[20] Ratnaweera A, Halgamuge SK, Watson HC, ”Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients”, IEEE on Evolutionary computation, vol. 8, no. 3, pp. 240-255, 2004.
[21] Zhang L-P, Yu H-J, Hu S-X, ”Optimal Choice of Parameters for Particle Swarm Optimization”, Journal of Zhejiang University: Science, vol. 6, no. 6, pp. 528-534, 2005.
[22] J. Kennedy, R. C. Eberhart, “A Discrete Binary Version of the Particle Swarm Algorithm”, IEEE International Conferenceon Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, pp. 4104-4108, 1997.
[23] Jia Liu, Chuanzhe Li, Denghua Yan, Fuliang Yu, “Research On Reservoir Optimal Operation Based On Dynamic Programming Model”, IEEE 2011.
[24] 胡家聲，郭創新，曹一家．一種適合於電力系統機組組合問題的混合粒子群優化算法[J].中國電機工程學報, vol. 24, no. 4 , pp. 24-28, 2004.
[25] http://www.stat.nuk.edu.tw/prost/Web/pdf13.htm
[26] M. Gen , R. Cheng, “Genetic Algorithms and Engineering Design”, John Wiley and Sons, Inc., pp. 1-96, 1997.
[27] 「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