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研究生： Sryang Tera Sarena
Sryang - Tera Sarena
論文名稱： A Framework for Microgrid Analysis using OpenDSS
A Framework for Microgrid Analysis using OpenDSS
指導教授： 連國龍
Kuo-Lung Lian
口試委員: 吳啟瑞
Chi-Jui Wu
郭政謙
Cheng-Chien Kuo
學位類別： 碩士
Master
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 72
中文關鍵詞： Economic Dispatch 、Neural Network 、OpenDSS 、Particle Swarm Optimization 、Solar Irradiance Prediction
外文關鍵詞： Economic Dispatch, Neural Network, OpenDSS, Particle Swarm Optimization, Solar Irradiance Prediction
相關次數： 點閱：361  下載：15 
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    • As different technologies are combined together in microgrid operation, specific organization and integration of methods and software are needed in microgrid analysis. This study will use OpenDSS in cooperation with Matlab. Thus optimization algorithm implementation can be achieved by Matlab and simulations of microgrid performed in OpenDSS. The establishment constructs a flexible framework which is extensible for various practical studies. Economic Dispatch (ED) is used as an analytical example to demonstrate the capability and suitability of the platform set up by the author. As an essential element in ED, Photovoltaic (PV) forecasting is also addressed in this study. Backpropagation Neural Network (BPNN) is used as the method of PV forecasting. The proposed network training method yield good accuracy shown by Mean Absolute Percentage Error (MAPE) less than 21%.

    As different technologies are combined together in microgrid operation, specific organization and integration of methods and software are needed in microgrid analysis. This study will use OpenDSS in cooperation with Matlab. Thus optimization algorithm implementation can be achieved by Matlab and simulations of microgrid performed in OpenDSS. The establishment constructs a flexible framework which is extensible for various practical studies. Economic Dispatch (ED) is used as an analytical example to demonstrate the capability and suitability of the platform set up by the author. As an essential element in ED, Photovoltaic (PV) forecasting is also addressed in this study. Backpropagation Neural Network (BPNN) is used as the method of PV forecasting. The proposed network training method yield good accuracy shown by Mean Absolute Percentage Error (MAPE) less than 21%.

    Abstract i Table of Content ii List of Figures iv List of Tables v 1 CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Problem statement 3 1.3 Outline 3 2 CHAPTER 2 OVERVIEW & VALIDATION OF OPENDSS 5 2.1 Background of OpenDSS 5 2.2 Implementation of OpenDSS 6 2.3 Validation of OpenDSS 9 3 CHAPTER 3 METHODS 10 3.1 Back Propagation Neural Network (BPNN) 10 3.2 Particle Swarm Optimization 14 4 CHAPTER 4 SYSTEM ARCHITECTURE 19 4.1 The Microgrid Single Line Diagram 19 4.2 The Microgrid Power Conversion Elements 20 4.2.1 Photovoltaic 20 4.2.2 Micro Turbine 23 4.2.3 Battery 25 4.2.4 Load 25 4.3 Power Flow 27 4.4 Electricity Price of Taiwan Power Company 29 4.5 Economic Dispatch 30 4.5.1 Objective Function 31 4.5.2 Constrains 32 4.5.3 PSO Parameters 34 5 CHAPTER 5 SIMULATION & RESULTS 36 5.1 Validation Result of OpenDSS Power Flow 36 5.2 Photovoltaic Forecasting Result 39 5.3 Economic Dispatch Result 47 5.3.1 Grid Connected Operating Mode 47 5.3.2 Islanding Operating Mode 54 6 CHAPTER 6 CONCLUSIONS AND FUTURE WORK 58 6.1 Conclusions 58 6.1.1 OpenDSS Power Flow Validation 58 6.1.2 Photovoltaic Forecasting 58 6.1.3 PSO Based Renewable Energy Dispatch using OpenDSS Power Flow 59 6.2 Future Work 60 REFERENCES 61

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