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博碩士論文 etd-0712120-135747 詳細資訊
Title page for etd-0712120-135747
論文名稱
Title
基於回歸和遺傳演算法與風力預測模型的動態經濟調度以評估潛在獨立電力系統
Dynamic Economic Dispatch for Evaluating a Potential Standalone Power System Using Regression and Genetic Algorithm with a Wind Forecasting Model
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
102
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2020-07-29
繳交日期
Date of Submission
2020-08-12
關鍵字
Keywords
動態經濟調度、遺傳算法、核回歸、遠程混合動力電廠
Remote Hybrid Power Plant, Kernels Regression, Genetic Algorithm, Dynamic Economic Dispatch
統計
Statistics
本論文已被瀏覽 5641 次,被下載 60
The thesis/dissertation has been browsed 5641 times, has been downloaded 60 times.
中文摘要
獨立的電源系統可以幫助偏遠地區接收電力,特別是在遠離電網的地區,但是可以確定和評估要建造的偏遠地區。我們可以使用的這種評估方法是預測,並且該預測可以是風速和太陽強度,然後可以將預測轉換為電能,以評估該區域可以產生電能的潛力,但運營成本較低。
在本論文中,尋找評估者確定RHPS建造地點的問題可以看作是可以通過Kernels回歸解決的問題,其中,Kernels Regression將接收時間和風速等2個輸入,以預測未來的風能速度。之後,將獲得的預測風速轉換為具有最大和最小能量的潛在電能,並將使用遺傳算法(GA)求解動態經濟調度(DED),以查看分配到電網中時的運營成本。該數據來自Baron Techno Park和PLTH Pantai Baru,由於雨季,因此僅使用4月,5月,6月和7月的數據。
Baron Techno-Park是從4月,5月,6月和7月獲得的,也是從需求1等於126837($ / day),123518.30($ / day),133510.80($ / day)和124512.60($ / day)和PLTH Pantai Baru,分別為65354.10(美元/天),65254.10(美元/天),51065.80(美元/天),70479.80(美元/天)。對於需求2,男爵技術園265508.10($ /天),261132.80($ /天),271894.10($ /天)和262401.70($ /天)。從PLTH Pantai Baru,分別為177663.30($ / day),177663.30($ / day),165981.70($ / day),185416.60($ / day)。因此,由於諸如每種貨幣的能量之類的重要參數表明,Baron Techno-Park的運營成本低於PLTH Pantai Baru,因此在這些地區創建可再生能源發電廠是合適的,並且將具有良好的運營成本合理性。
Abstract
A standalone power system can help remote areas to receive electricity especially in areas that are far reached from the power grid, but in order to determine and evaluate which remote areas to build. Such method of evaluation we can used is prediction, and that prediction can be of wind speed and solar intensity, where the prediction can then be converted to electrical energy to evaluated the potential that area can produced in power but also with less operational cost.
In this Thesis, the problem to find an evaluator to determine a location to build the RHPS can be seen as problem that can be solved with Kernels Regression where, it will receive 2 inputs such as time and wind speed in order to predict the future wind speed. Afterward the obtained predicted wind speed will be converted into potential electrical energy with maximum and minimum energy and will be using the Genetic Algorithm (GA) to solve the Dynamic Economic Dispatch (DED) to see the operational cost when dispatch into the grid. The data was taken from Baron Techno Park and PLTH Pantai Baru, and only will be using data from the month of April, May, June, and July, since is the rainy season.
Baron Techno-Park was obtained from the month of April, May, June, and July, also from demand 1 equal to 126837 ($/day), 123518.30 ($/day), 133510.80 ($/day), and 124512.60 ($/day), and PLTH Pantai Baru, 65354.10 ($/day) , 65254.10 ($/day), 51065.80 ($/day), 70479.80 ($/day). For Demand 2, Baron Techno-Park 265508.10 ($/day), 261132.80 ($/day), 271894.10 ($/day), and 262401.70 ($/day). From PLTH Pantai Baru, 177663.30 ($/day), 177663.30 ($/day), 165981.70 ($/day), 185416.60 ($/day), respectively. Therefore, since significant parameters such as energy per currency shows that Baron Techno-Park have the less operational cost than PLTH Pantai Baru, then the creation of renewable power plants in these areas are suitable and will have a good operational cost justification.
目次 Table of Contents
Validation Letter ............................................................................................................. i
Acknowledgements ......................................................................................................... ii
摘要 ................................................................................................................................. iii
Abstract .......................................................................................................................... iv
Table of Content ............................................................................................................. v
Table of Figures ........................................................................................................... viii
Table of Tables ............................................................................................................... xi
Chapter I Introduction ................................................................................................... 1
1.1 The Importance of Wind Forecasting and DED in RHPS ....................................... 1
1.2 Related Works ............................................................................................................. 2
1.3 Thesis Objectives ......................................................................................................... 3
1.4 Research Methodologies ............................................................................................. 3
1.5 Thesis Outline .............................................................................................................. 4
Chapter II The Role of the Dynamic Economic Dispatch Procedure with Remote Power Plant ..................................................................................................................... 5
2.1 Basic Structure of the Electrical Power System ....................................................... 5
2.1.1 Characteristic of the Conventional Power System ............................................ 5
2.1.2 Generation ............................................................................................................. 6
2.1.3 Transmission ......................................................................................................... 7
2.1.4 Distribution ........................................................................................................... 8
2.2 Remote Power System ................................................................................................. 8
vi
Chapter III Formulation of the Dynamic Economic Dispatch and Predictive Regression ..................................................................................................................... 10
3.1 Problem Formulation Dynamic Economic Dispatch ............................................. 10
3.1.2 Equality and Inequality Constraints ................................................................ 11
3.2 Prediction Technique Using Regression .................................................................. 13
3.2.1 Regression Visualization .................................................................................... 13
Chapter IV Genetic Algorithm and Regression Method .......................................... 15
4.1 Foundation of the Genetic Algorithm ...................................................................... 15
4.1.1 Initialization ........................................................................................................ 15
4.1.2 Selection ............................................................................................................... 16
4.1.3 Crossover ............................................................................................................. 17
4.1.4 Mutation .............................................................................................................. 18
4.1.5 Elitism/Fitness Function .................................................................................... 18
4.2 Regression with Non-Linear Feature ...................................................................... 19
4.2.1 Non-Linear Regression with Polynomial Features .......................................... 20
4.2.2 Regularization and Evaluation .......................................................................... 21
4.2.3 Kernel Function .................................................................................................. 22
4.3 Wind Energy Conversion System ............................................................................ 23
Chapter V Simulation Results ..................................................................................... 26
5.1 9 Generators Operational Cost Data and Demand for 24 Hours ......................... 26
5.2 Wind Prediction Results Using Kernels Regression............................................... 29
5.3 DED Results Using Genetic Algorithm ................................................................... 33
5.3.1 Baron Techno-Park DED Results Demand 1 ................................................... 34

5.3.2 PLTH Pantai Baru DED Results Demand 1 .................................................... 43
5.3.1 Baron Techno-Park DED Results Demand 2 ................................................... 52
5.3.2 PLTH Pantai Baru DED Results Demand 2 .................................................... 61
5.4 Residual Load ............................................................................................................ 70
5.5 Energy per Currency for 24 Hours.......................................................................... 79
Chapter VI Conclusion ................................................................................................ 84
6.1 Conclusion .................................................................................................................. 84
6.2 Limitation ................................................................................................................... 85
6.3 Future Research ........................................................................................................ 85
References...................................................................................................................... 87
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