隨著科技日新月異，能源在各個領域皆扮演了相當重要的角色，在化石能源逐漸耗盡與全球暖化現象加劇的情況下，各國政府紛紛訂立能源相關規範。為了因應低碳經濟時代來臨，如何提高能源使用效率以降低成本，已經成為企業追求永續發展的重要課題。本論文目的為發展一套混合再生能源系統之最佳化模型，其結合儲能策略、儲能系統容量、發電、輸電、配電，使再生能源達到最有效的利用，進而降低能源系統營運成本。此模型適用在社區、工廠、辦公處、園區、學校等具有獨立發配電處之區域，藉由增設再生能源以及儲能設備，使能源使用成本得以有效節省。管理者可根據此模型之結果，增設或縮減所需的儲能容量、更改較佳之儲能策略或啟動可用之傳統柴油發電機，使管理地區從電力供應商購買之電能成本控制在理想範圍。
由於此模型涉及大量混整數變數，在建構此模型後，將更進一步的發展一套混整數隨機最佳化演算法求解。最後，為了使決策者能更清楚以及方便的運用所求出之結果，我們更進一步地將模型與演算法整合在一能源管理決策資源系統。此系統可讓使用者自行將現有數據導入，經由分析後將提供給管理者詳細之數據、圖表，並對結果進行敏感度分析。使決策者可在參酌、評估後，做出更合適之決定。

Renewable energy gains more popularity over the decades because it is environmentally friendly and never runs out. Hybrid renewable energy system (HRES), which combines renewable energy resources such as wind and solar power generators and conventional power generators, utilizes renewable energy first and uses conventional power generators to make up the difference when renewable power is insufficient. Due to a large variability in the renewable energy supply and power demand, how to effectively and efficiently store the excessive power in HRES so as to achieve minimum cost has been a challenging problem. In this paper, we propose a stochastic programming model to characterize the power storage problem. Further, we develop an analysis methodology to decide on the optimal power storage strategy. An extensive numerical study is conducted to verify the viability of the proposed model in real settings.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 5
第二章 文獻探討 7
2.1 混和再生能源系統 7
2.2 混整數演算法 10
第三章 數學模型 12
3.1 問題定義 12
3.2 符號定義 13
3.3 混和再生能源系統模型 14
第四章 求解方法 20
4.1 傳統隨機混整數演算法 20
4.2.1 Particle Swarm Optimization (PSO) 21
4.2.2 Nested Partition Method 22
4.2 Nested Particle Swarm (NPS) 23
第五章 數值結果 27
5.1 參數設定 27
5.2 數值結果 28
5.2.1 儲能系統之比較 28
5.2.2 演算法之比較 31
第六章 決策支援系統 33
6.1 系統介紹 35
6.1.1 參數設定與輸出資料 35
6.1.2 敏感度分析 36
6.1.3 參數預測 37
6.2 簡易操作說明 38
6.2.1 使用者輸入與選擇部分 38
6.2.2 系統輸出部分 39
第七章 結論與未來規劃 41
7.1 結論 41
7.2 未來研究 42
參考文獻 44

鄭婉真 〈電網用電化學儲能市場發展趨勢分析〉 新竹：工業技術研究院，2012。，
Baran, M.E., Bhattacharya, S., Huang, A.Q., 2009. “Optimal control of battery energy storage for wind farm dispatching”, IEEE Transactions on Energy Conversion, pp.787-794.
Boss, G.J., Doran, J.R., Rick, A.H.II., Sand, A.R., 2010. “Policy-based energy management”, Patent Application Publication, US 20100063643 A1.
Brekken, T.K.A., Yokochi, A., von Jouanne, A., Yen, Z.Z., Hapke, H.M., Halamay, D.A., 2010. “Optimal energy storage sizing and control for wind power applications” IEEE Transactions on Sustainable Energy, Vol.2, pp.69-77.
Chang, K.H., 2014. “A decision support system for planning and coordination of hybrid renewable energy systems” Decision Support Systems, Vol.64, pp.4-13.
Chang, K.H., 2013. “Modeling and optimization of hybrid renewable energy systems design” Omega, submitted.
Clerc, M., 1999, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization” IEEE Proceedings of Congress on Evolutionary Computation, Vol.3, pp.1951-1957.
Coelho, L.S., 2009. “An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications”, Reliability Engineering and System Safety, Vol.94, pp.830-837.
Eberhart, R., Kennedy, J., 1995. “A new optimizer using particle swarm theory”, Proceedings of the International Symposium on Micro Machine and Human Science, Vol.6, pp.39-43.
Glover, F., 2014. “Tabu search: a tutorial”, Interfaces, Vol.20, No.4, pp.74-94.
Johnson, E.L., Nemhauser G.L., Savelsbergh M.W.P., 1999. “Progress in linear programming-based algorithms for integer programming: an exposition”, INFORMS Journal on Computing, Vol.12, No.1, pp.2-23.
Kuznia, L., Zeng, B., Centeno, G., Miao, Z., 2013. “Stochastic optimization for power system configuration with renewable energy in remote areas”, Annals of Operations Research, Vol.210, pp.411-432.
Lee, D., Kim, J., Baldick, R., 2012. “Ramp rates control of wind power output using a storage system and gaussian processes”, ourenergypolicy.org.
Lulli, G., Sen, S., 2003. “A branch-and-price algorithm for multi-stage stochastic integer programming with application to stochastic batch-sizing problems”.
Matthews, M., Nayar, H.P., 2012. “Energy storage and power management system”, Patent Application Publication, CA 2766593 A1.
Powell, W.B., George, A., Simão, H., Scott, W., Lamont, A., Stewart, J., 2012. “SMART: a stochastic multiscale model for the analysis of energy resources, technology, and policy”, INFORMS Journal on Computing, Vol.24, pp.665-682.
Sen, S., 2005. “Algorithms for stochastic mixed-integer programming models”, Handbooks in OR & MS, Vol.12, pp.515-558.
Shi, L., Ólafsson, S., 2009. “Nested partitions method, theory and applications”.
Shi, Y., Eberhart, R., 1999. “Empirical study of particle swarm” IEEE Proceedings of Congress on Evolutionary Computation, Vol.3, pp.1945-1950.
Zhao, L., Zeng, B., 2012. “Robust unit commitment problem with demand response and wind energy”, IEEE Power and Energy Society General Meeting.