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研究生:劉威良
研究生(外文):Wei-Liang Liu
論文名稱:田口-免疫演算法應用於含風力發電之火力機組調派
論文名稱(外文):Thermal Unit Commitment with Wind Farms Using Taguchi-Immune Algorithm
指導教授:陳昭榮陳昭榮引用關係
口試委員:吳啟瑞黃有評劉志文
口試日期:2012-06-29
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:105
中文關鍵詞:免疫演算法免疫-田口混合演算法機組調派風力發電
外文關鍵詞:Immune AlgorithmHybrid Taguchi-Immune AlgorithmUnit CommitmentWind Power
相關次數:
  • 被引用被引用:9
  • 點閱點閱:227
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近年來國際能源價格不斷高漲,許多國家意識到再生能源的重要性,而再生能源技術中以風力發電為最成熟、具有商業化發展前景的新能源技術。機組調派是指一天24小時中規劃發電機組排程及發電量,其中必須滿足發電機及系統上各項限制條件,有效地調度各台發電機,並且需要在足夠的備轉容量下運轉,其中限制式以最小起/停機時間及斜率變動量為本文考慮之重點。將風力與火力機組有效地結合,以減少石化能源的使用,達到全球關注的環保議題。
因為風力具有不確定性之因素,故本論文假設已知隔天最有可能的10種風力發電資料,使火力機組在其風力發電資料下達到近似最佳化的調度與排程,並且加入了輸電線損失,以符合實際系統的運轉情況。演算法則使用田口-免疫演算法應用於風力發電下進行火力機組調派,利用免疫演算法之親合度篩選,可有效避免運算太過相似的解,以減少運算次數,為了增進搜尋近似最佳解,在免疫演算法於交配與突變間,插入田口直交表實驗,此方法能有效搜尋全局最佳解。
本文以IEEE 30-Bus及IEEE 118-Bus之系統來驗證其功效,並與免疫演算法及基因演算法做比較,模擬結果證明該方法是可行的,並比其他演算法在相同時間下更能獲得近似最低成本。在此,期望能給調度人員提供更經濟之參考。


Due to the increase of international energy prices in recent years, many countries are aware of the importance of renewable energies. To this day, wind power is the most advantageous renewable energy with a technological prospect for business development. Each unit is committed to aim a schedule power plan for 24 hours a day. It has to meet various constraints on the generators and in the systems, also to schedule each generator effectively, as well as the operation requirement for adequate spinning reserve. We focus on the minimum of the on and off time and constraint the ramp amount in this different way. To achieve this global issue, the acquisition of wind power may reduce fossil fuels efficiently and protect the ecosystem and environment.
Due to the uncertainty factors of wind, we can possible assume that ten possibility wind power data will be known the next day and this will allow to achieve approximately optimization of dispatch and unit commitment in these wind power data. By joined the transmission line losses, in order to comply with the actual functioning of the system the result would be exponential. The Taguchi - immune algorithm is used to deploy thermal units when wind turbines are operating. The immune algorithms’ affinity screening will avoid computing similar solutions effectively in order to reduce the numbers of operations. For enhancing the search result of finding optimal solution, we insert the Taguchi orthogonal array experiment between crossover and mutation of the immune algorithm. This method can search the global optimal solution effectively.
In this thesis, the IEEE 30-Bus and IEEE 118-Bus systems are applied to verify their effectiveness and to compare the immune algorithm and genetic algorithm. Simulation results are approximated by the lowest cost at the same time. This proves that the method is better and more feasible than other algorithms. By this point, we would expect to give the dispatchers a more performing and economical reference.


摘 要-----i
ABSTRACT-----ii
致謝-----iv
目錄-----vi
表目錄-----viii
圖目錄-----x
第一章 緒論-----1
1.1 背景與動機-----1
1.2 文獻回顧-----3
1.3 研究目的及方法-----6
1.4 研究貢獻-----6
1.5 論文內容概述-----7
第二章 機組調派理論-----9
2.1 火力機組調派目標函數及限制式-----9
2.2 電力潮流----- 13
2.2.1 求解電力潮流的方法-----18
2.2.2 牛頓-拉福森法求解電力潮流-----20
2.3 經濟調度-----22
2.3.1 忽略損失但考慮發電機限制之經濟調度-----24
第三章 田口-免疫演算法之理論概述-----25
3.1 免疫系統-----25
3.1.1免疫反應-----26
3.1.2免疫反應專有名詞解釋-----27
3.1.3免疫反應特性-----30
3.2 田口法概述-----32
3.2.1田口法品質特性-----33
3.2.2田口方法專有名詞解釋-----36
3.2.3田口法實驗步驟-----37
3.3 田口-免疫演算法步驟-----40
3.4.1田口-免疫演算法應用於火力機組調派-----51
第四章 模擬結果與討論-----57
4.1資料前處理-----57
4.2 IEEE 30-bus系統應用說明-----59
4.2.1 IEEE 30-bus系統模擬結果與討論-----65
4.3 IEEE 118-bus系統應用說明-----75
4.3.1 IEEE 118-bus系統模擬結果與討論-----87
第五章 結論與未來研究方向-----100
5.1 結論-----100
5.2 未來研究方向-----101
參考文獻-----102


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