應用基因演算法於核能火力發電機組維修排程

近年來台灣的經濟成長快速，用電量亦逐年增加。除了2008~2009年期間受全球金融海嘯影響，使得尖峰負載用電呈現有史以來首次的下降。其餘年度的負載均呈現穩定成長的現象。依據經濟部能源局委託台電綜合研究所進行負載成長預測之研究在全國系統尖峰負載方面，平均成長率為3.6%。因此，穩定供給用戶可靠的電力，乃為電力自由化後，電力公司競爭之必備條件。然而在環保意識抬頭，發電廠不易增設的情況之下，如何能夠讓現有的發電機組產生最大的效益，妥善的維修排程規劃乃不可或缺。本文採用基因演算法，並以均化備用率做為目標函數，考慮維修人力、機組連續檢修、檢修起始時段限制、負載限制、備轉容量等等限制因素，並以台電32部核能火力機組做為測試，求取以均化備用率為目標函數的發電機組維修排程最佳解。經測試結果分析後，證明此方法可以在很短的時間內求取多組發電機組維修排程最佳化後之可行解，可供制定機組維修計畫人員參考。

關鍵字

維修排程；基因演算法；均化備用率

並列摘要

In recent years, due to the rapid economic growth in Taiwan, the consumption of electric energy has increased year by year. Apart from the period of 2008 to 2009, affected by the global financial tsunami, the peak load showed a decline in consumption of all time. For the rest of other years, consumption has increased steadily. According to the Taiwan Power Research Institute, the average load growth rate is 3.6%. Therefore, the supply of stable and reliable power is the prerequisite to the competition of power companies after the liberalization of the power industry. However, under the circumstances of the focus of the environmental consideration, and the difficulty in building new power plants, how to adjust existing generators to produce the energy with the greatest efficiency, proper maintenance schedule planning is essential. This article uses genetic algorithms, with objective function of levelized spinning reserve, and consider the maintenance manpower, continuous maintenance period, repair starting time, system loads, spinning reserve constraints, etc. Testing of 32 nuclear and fossil power generators unit maintenance scheduling is conducted to obtain the optimal solution. The analysis results showed that this method can obtain multiple sets of generator maintenance scheduling in a short period of time, that can help the program staff to develop the generator maintenance plan.

並列關鍵字

Maintenance Scheduling ； Genetic Algorithm ； Levelized Spinning Reserve

參考文獻

[3] Pillai N, Vijayamohanan, ” Loss of Load Probability of a Power System” MPRA Paper No. 6953, posted 01, February 2008.

[5] H. H. Zurn and V. H. Quintana, “Generator Maintenance Scheduling via Successive Approximation Dynamic Programming,” IEEE Transactions on Power Apparatus and System, Vol. 94, Issue 2 part 1, Mar 1975, pp. 666-671.

[6] D. Chattopadhyay, K. Bhattacharya and J. Parikh, “A System Approach to Least Cost Maintenance Scheduling for an Interconnected Power System,” IEEE Transactions on Power System, Vol. 10, Issue. 4, Nov 1995, pp. 2002-2007.

[7] K. P. Dahal and J. R. McDonald, “Generator Maintenance Scheduling of Electric Power Systems Using Genetic Algorithms with Integer Representation,” Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow, 2-4 Sep 1997, pp. 456-461.

[8] M. Negnevitsky and G. Kelareva, “Application of Genetic Algorithms for Maintenance Scheduling in Power Systems,” Neural Information Processing, Proc. ICCNIP ’96. 6th International Conference, Perth, WA, Vol. 2, 16-20 Nov 1999, pp. 447-452

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應用基因演算法於核能火力發電機組維修排程

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