近幾年石油價格波動幅度大，使得替代品煤炭之價格也面臨上漲壓力，我國煤炭完全仰賴進口，燃煤發電為主要供電來源，因此如何確保供應的穩定與降低價格波動的風險，維持經濟發展並避免因能源價格波動所造成的影響，已成為政府的重要課題。儘管過去學者曾以線性規劃分析成本最小之購煤策略，及以靜態投資組合理論追求價格風險最小，但近年來能源價格波動顯著，利用傳統線性規劃或投資組合理論時，價格是以年成長率估算，無法在規劃期間內調整配置，難以因應短期間價格大幅波動之風險，而失去購煤策略之效率。
基此，有別於一般投資組合理論在投資期間內資產配置不變，本研究應用動態資產配置(Dynamic asset allocation)理論，求出動態投資組合前緣與資產配置，即在給定預期價格變動率下的最小變異之購煤組合，並假設在個案公司的燃煤總需求量(總資產)限制下，建構動態購煤模型，且在此模型中購煤策略屬非線性最適化問題，為將動態資產配置應用於(單一)能源採購策略之嘗試性研究。受限於資料可得性，以澳洲燃煤為研究標的，考量兩種資產配置(定期契約與單一現貨)，結果顯示，在給定的預期價格變動率下，動態效率前緣上的變異數明顯小於靜態效率前緣之變異數，表示動態購煤組合較靜態購煤組合有效率，因此在燃煤價格波動劇烈時，宜採用動態購煤方式進行採購，隨著價格變動及時調整購煤數量，以提高採購效率並降低風險。本研究並評估三種資產配置(定期契約與兩種現貨)，結果顯示，資產配置以定期契約為主，現貨為輔，隨價格變動率彈性調整，表示動態購煤模型可進行一般化擴充應用。在燃煤交易價格取得不易下，相關價格參數屬初步推估，未來可針對各參數進行更詳盡之檢視，以提高模型之合理性與實用性。

The dramatic fluctuating of oil price in recent years triggers the coal price to increase. Coal thermal power stations being the main resource of power supply, plus the dependency upon importing, it is essential for the government to sort out the way to stabilize coal supplication. Also, to reduce the economic impacted cause by price volatility is also an issue. Previous researchers have applied linear programming to minimize the cost and portfolio theory to minimize the risk to analyze coal purchasing. The recent irregular and frequent changes of fossil fuel price cannot cope with the risk of price volatility. Also, by using conventional linear programming and portfolio theory, will make purchasing strategies inefficient –due to the rapid changes of price.
Accordingly, different from general portfolio theory-which the allocation is fixed during an investigational period-this study applies Dynamic Asset Allocation Theory. This theory sorts out the frontier and allocation. In other words, we apply this theory in order to minimize the variation of coal purchasing-under an expected rate of changing price. We built the dynamic coal-purchasing model under the hypothesis of the case company’s total demand. Furthermore, in this model, the coal-purchasing strategy is non-linear optimization-to apply dynamic asset allocation to an exclusive energy purchasing strategy’s trial. Restricted by the information’s availability, we set Australia’s steam coal as a target. We consider two kinds of allocation-contracts and spots. As a result, under a given expected rate of changing price, the dynamic efficiency frontier’s variation is apparently lower than the static one. This implicates dynamic coal purchasing is more efficient than the static. Thus, when the fluctuation is intense, we should apply to the dynamic coal-purchasing model. We should adjust the quantity with the changing of price, to enhance the purchasing efficiency and lower the risk. This study further evaluates three kinds of allocations- contracts and two spots. Consequently, the majority of allocation is contracts, whereas spots the minor.
Along with the flexibility of the price-changing rate, it shows that dynamic coal purchasing model can proceed to the extended application. Due to the low availability of the steam coal’s price, the relevant price parameters belong to the beginning estimation. We can focus on different parameters to do further evaluations, in order to escalate the rationality and practicality of the model.

1. 台灣電力公司 (2008)，台電公司燃煤採購說明。
2. 台灣電力公司 (2009)，台灣電力公司98年年報。
3. 台灣電力公司 (2010)，電價燃料條款機制Q&A。
4. 周國瑞、黃鴻禧 (2002)，“動態資產配置下的投資組合前緣”，2002年中華民國風險管理年會暨國際風險管理研討會。
5. 洪鼎倫 (2009)，考量燃煤價格風險的購煤策略-以台電公司為例，碩士論文，國立成功大學資源工程研究所。
6. 徐振湖 (2009)，“台灣電力公司因應國際金融風暴採行之燃煤及核燃料採購策略”，中國礦冶工程學會民國98年專題討論會，台北。
7. 張四立、陳彥尹 (2005)，“台灣再生能源發展與能源供應安全之策略評估—投資組合理論之應用”， 兩岸能源經濟學術研討會，台北。
8. 郭訓德、陳家榮 (1994)，“燃煤採購策略分析模式之建立”，能源季刊，第24卷，第2 期，pp106-119。
9. 黃鴻禧 (2002)，最適動態資產配置暨理性預期均衡，博士論文，國立台灣大學財務金融研究所。
10. 黃韻勳 (2007)，考量發電風險之電力供給規劃研究—再生能源範例分析，博士論文，國立成功大學資源工程研究所。
11. 楊奕農 (2009)，時間序列分析－經濟與財務上之應用(第二版)，雙葉書廊。
12. 經濟部能源局 (2011)，民國99年台灣能源統計手冊。
13. 賴正文 (2006)，燃煤價格預測及其變動因素之研究，行政院國家科學委員會專題研究計畫。
14. 鐘瑩燕 (2007)，台電公司發電燃料採購避險操作可行性研究及避險措施規劃，台電公司委託計畫，台綜院。
15. Awerbuch, S. (2004), “Portfolio-Based Electricity Generation Planning: Policy Implications for Renewables and Energy Security”, Paper Prepared to The Renewable Energy & Energy Efficiency Partnership (REEEP), and UNEP.
16. Awerbuch, S. and Berger, M. (2003), Applying Portfolio Theory to EU Electricity Planning and Policy-Making, IEA.
17. Bajeux-Besnainou, I. and Portrait, R. (1998), “Dynamic Asset Allocation in a Mean- Variance Framework”, Management Science, Vol. 44, No. 11, Part 2 of 2 (Nov., 1998), pp. S79-S95.
18. Brandt, M.W. and Santa-Clara, P.(2006), “Dynamic Portfolio Selection by Augmenting the Asset Space”, Journal of Finance, Vol.61, Iss.5, pp.2187-2217.
19. British Petroleum (BP) (2011), BP Statistical Review of World Energy.
20. Brooke, A., Kendrick, D., Meeraus, A., and Raman, R. (2005), GAMS - A Users Guide, GAMS Development Corporation.
21. Energy Information Administration (EIA) (2011), Annual Energy Outlook 2011, DOE/EIA.
22. Huang, Y. H. and Wu, J. H. (2008), “A Portfolio Risk Analysis on Electricity Supply Planning”, Energy Policy, Vol.36, Iss.2, pp627-641.
23. Humphreys H.B. and McClain K.T. (1998), “Reducing the impacts of energy price volatility through dynamic portfolio selection”, Energy Journal, Vol. 19, Iss.3, pp.107-131.
24. International Energy Agency (IEA) (2010), Coal Information 2010, IEA.
25. Richardson, H. R., (1989), “A Minimum Variance Result in Continuous Trading Portfolio Optimization”, Vol.35, No.9, pp.1045-1055.
26. Tang, C. (1993), “Structure and price adjustments in long-term contracts : The case of coking coal trade in the Asian-Pacific market”, Energy Policy, Vol.21, Iss.9, pp.944-952.
27. Wu, G., Liu, L. C. and Wei, Y. M. (2009), “Comparison of China’s Oil import Risk: Results Based on Portfolio Theory and a Diversification Index Approach”, Energy Policy, Vol.37, Iss.9, pp3557-3565.
28. Wu, G., Wei, Y. M., Fan, Y. and Liu, L. C. (2007), “An Empirical Analysis of the Risk of Crude Oil Imports in China Using Improved Portfolio Approach”, Energy Policy, Vol.35, Iss.8, pp4190-4199.
29. Zaklan, A., et al. (2011), “The Globalization of Steam Coal Markets and the Role of Logistics”, Energy Economics. doi:10.1016/j.eneco.2011.03.001