應用基因演算法於考慮風力發電不確定之實功率-虛功率排程最佳化

Using Genetic Algorithm on Optimal Active-Reactive Power Scheduling with Considering Wind Generation Uncertainties

高薪淙(Hsin-Tsung Kao)

指導教授 ： 陳昭榮

國立臺北科技大學/電資學院/電機工程系研究所/碩士(2014年)

摘要

近年來因為環保意識抬頭和國際能源不斷的飆漲，國際上開始重視使用乾淨的再生能源來供電的重要性，其中以風力發電技術為最有前景的乾淨能源。本論文應用基因演算法在電力系統的實功率-虛功率的24小時最佳化調度，在以往的研究上大多數都將實功率和虛功率分開討論，少部分一起考慮兩者的研究也只考慮單個操作點，而本文中將兩者合併排程調度，並考慮24小時且考慮風力發電不確定因素，並運用基因演算法找出滿足所有系統運轉限制式的發電成本最小化和線路損失率最小化。本文中因為有考慮風力發電的不確定因素，故利用機率取樣方式取出10種24小時之情境資料，運用此情境模擬各種風力發電的情況，找出在風力發電的不確定因素下，可以滿足所有限制條件的最佳化排程。
本論文使用基因演算法來演算找出最佳化參數，目標函數是將虛功率調度中之最小化輸電線損失率及實功率調度之最小化燃料成本，藉由加權值合成一多目標函數。透過分析IEEE 30-Bus，證實所使用最佳化方法應用在實-虛功率排程之可行性，並求得較低成本、降低線路損失以及系統操作電壓皆在合理的範圍內，期望可給予調度人員有更經濟及安全的調度。

關鍵字

虛功率和電壓控制 ； 基因演算法 ； 機組調派 ； 風力發電 ； 最佳化電力調度

並列摘要

Recently, the importance of power supply from clean recycle energy has drawn great attention because of environmental protection and increasing cost of international energy, especially the technique of wind power generator because of its potential in research. In this thesis, genetic algorithm is applied to power system in optimal management for 24 hours active-reactive power. Among the past research, active power and reactive power were usually analyzed separately and single operation point was only considered in those cases. Here, both active power and reactive power are combined to schedule. Additionally, time of 24 hours and uncertainty of wind power generation into consideration, genetic algorithm is introduced to meet the satisfaction of minimized fuel cost and transmission line losses rate in all limited operation systems. Due to those uncertain factors considered in this research, ten different twenty four hour-wind power data is used to simulation of wind power for various situations in wind power generation. Therefore, the optimal schedule can be found in those uncertain factors of wind power generation and fit to all restrictions.
In this study, genetic algorithm is adopted to find the optimized parameters while the goal function is a multiple objective function through the weighted value synthesis of the minimize transmission line loss rate for reactive power scheduling and the minimized fuel cost in active power scheduling. By analyzing IEEE 30-BUS, prove the method is feasible in active-reactive power scheduling. Obtain reduce cost and transmission line loss. Also power system operating voltage are within a reasonable rang. Above all, those are expected to provide much more economical and safer operation as reference for the dispatchers in the future.

並列關鍵字

Reactive Power and Voltage Control ； Genetic Algorithm ； Unit Commitment ； Wind Power ； Optimal Power Dispatch

參考文獻

[24] 盧乃彥，應用田口－基因演算法於有效－無效功率調度最佳化，碩士論文， 國立臺北科技大學電機工程系，臺北，2012。

[26] 張益誠，使用班德式分解法於最佳無效電力排程，碩士論文，國立台北科技大學電機工程系，臺北，2013。

[2] Y. Y. Hong and Y. F. Luo, “Optimal VAR control considering wind farms using probabilistic load-flow and gray-based genetic algorithms,” IEEE Transactions on Power Delivery, vol. 24, no. 3, 2009, pp. 1441-1449.

[3] A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, “Optimal power flow by enhanced genetic algorithm,” IEEE Transactions on Power Systems, vol. 17, no. 2, May 2002, pp. 229-236.

[4] I. M. Malik and D. Srinivasan, “Optimum power flow using flexible genetic algorithm model in practical power systems,” in Proceedings of the 9th International Power and Energy Conference, Oct. 2010, pp. 1146-1151.