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:371 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%.
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