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研究生:馮閔
研究生(外文):Min-Fung
論文名稱:以互補式資料分群技術實現電力負載聚合
論文名稱(外文):Aggregating Electric Load with a Complementary Data Clustering Technique
指導教授:鄧維光
指導教授(外文):Wei-Guang Teng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:39
中文關鍵詞:契約容量用戶群代表負載曲線資料分群小波轉換
外文關鍵詞:contract capacityaggregatorload profiledata clusteringdiscrete wavelet transform
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現今電力市場中,電力系統即時平衡電力的生產和消費是一項主要任務。有鑑於此,電力供應商通常與其關鍵的消費者 (如大型工業或商業用戶) 簽訂合約來管理他們的電力容量需求。而隨著電業自由化的開放,市場中引入用戶群代表的角色,將消費者進行群聚,並藉由他們的用電需求與電力供應商進行價格談判。用戶群代表通過將眾多消費者合理分組並對用電負載進行聚合,有助於確保穩定的電力系統平衡供應,並減輕管理許多消費者的負擔。在此研究中,我們提出了一種新的資料分群方法,根據消費者每日電力負載曲線 (即消費者用電模式) 找出合併後可以平緩電力負載曲線尖峰、用電型態相對互補的消費者進行群聚。除此之外,我們採用小波轉換技術來加速分群運算過程。具體而言,透過少數小波係數推導的近似值可以精確地迫近原始用電負載形狀。而基於真實資料所進行的實驗結果顯示,我們所提出之方法極具實際應用的價值。
In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power suppliers usually sign contracts capacity with their essential consumers (i.e., large-scale industrial and commercial companies) for managing their capacity demands. With the electricity liberalization, the characters called aggregators, join to the electricity market and group consumers to integrate their demands to negotiate with power suppliers. With a proper grouping of numerous consumers and aggregating their electricity load profile, aggregators help to ensure stable and balance electric supply, and reduce the burden of managing many consumers. In this approach, we propose a novel data clustering algorithm to cluster complementary consumers based on their daily load profile to damp the aggregation load. Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.
Chapter 1 Introduction 1
1.1 Motivation and Overview 1
1.2 Contributions of This Work 2
Chapter 2 Preliminaries 3
2.1 Billing of Electricity Usage 3
2.2 Analysis of Electricity Consumption Data 4
2.3 Aggregator 6
2.3.1 Background Statement 6
2.3.2 Load aggregation 8
2.3.3 Examples of Aggregator in the U.S.A 9
2.3.4 Examples of Aggregator in Europe 12
Chapter 3 Proposed Scheme of Complementary Clustering 15
3.1 Data Clustering 15
3.2 Discrete Wavelet Transform 18
3.2.1 Reconstruction of Wavelet Transform 20
3.2.2 Distance to the Maximum Value 21
3.2.3 DMV Derived by Wavelet Transform 22
3.3 Progress Value 24
Chapter 4 Empirical Studies 26
4.1 Experimental Environment 26
4.2 Data Preprocessing 27
4.3 Measure the Quality of Experiment 28
4.4 Experiment Control Flow 29
4.5 Experimental Results 31
4.6 Results Discussion 34
Chapter 5 Conclusions and Future Works 35
Bibliography 36
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