研究生: |
黃江凱 Jiang-Kai Huang |
---|---|
論文名稱: |
應用類神經網路於具再生能源發電之配電饋線短期負載預測 Short-term Load Forecasting of Distribution Feeders with Renewable Energy Generation by Using Artificial Neural Networks |
指導教授: |
郭明哲
Ming-Tse Kuo |
口試委員: |
連國龍
Kuo-Lung Lian 吳進忠 none |
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: | 2015 |
畢業學年度: | 103 |
語文別: | 中文 |
論文頁數: | 98 |
中文關鍵詞: | 關鍵字 、負載預測 、再生能源發電量預測 、電力調度 |
外文關鍵詞: | Keyword, Load Forecasting, generation forecasting of renewable energy, power dispatching |
相關次數: | 點閱:364 下載:1 |
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負載預測在電力調度作業中扮演著極為重要的角色。精確的負載預測可提供較正確的機組排程與規劃,進而提高供電品質,尤其在大量再生能源發電併入系統後,提高負載預測的精確度更是增加系統調度安全及降低成本的治本之道。再生能源雖然取之不盡、用之不竭,但卻難以穩定且持續的供電,將使電力調度帶來相當的不確定性。因此若以傳統化石燃料機組配合再生能源機組操作,則需要更為精確的負載預測方法,以降低電力營運的風險與成本。本研究擬以考量再生能源發電與氣象資訊及饋線負載之關聯,進而提出一套更精確的負載預測方法,以應用在太陽光電與風力發電併接於同一饋線的狀況。本論文除了應用類神經網路對於配電饋線執行逐時負載預測外,更針對同一饋線上併接之太陽光電與風力發電裝置進行逐時發電量預測,並以台電雲林橋村變電所及苗栗山佳變電所的饋線資訊進行實際測試,測試結果證實可確實地改善負載預測的效果,進而提高饋線的供電品質並提供電力調度單位更彈性的調度策略。
Load forecasting plays the extremely important role in power dispatch operations. Accurate load forecasting can provide the more accurate unit commitment and planning in order to improve quality of power supply. Especially, after a lot of renewable energy generations were integrated into power systems, improving the precision of load forecasting is important for increasing safety of system operation and reducing the cost. Although renewable energy is inexhaustible, it is difficult for stable and sustained supply. The power dispatch will be considerable uncertainty. Therefore, if traditional fossil fuel units work together with renewable energy units, we need a more accurate load forecasting method to reduce the risk and cost of electricity operations.
This thesis is intended to consider the relevance among the renewable energy generation, weather information and feeder load in order to propose a more accurate load forecasting method to be applied in the same feeder which photovoltaic and wind generators are connected to.
This thesis proposed to operate the hourly load forecasting in distribution feeders by the application of the neural network and calculate hourly generation forecasting when photovoltaic and wind generations are connected in the same feeder. The feeder information of Qiaocun substation in Yunlin and Shanjia substation in Miaoli which belong to Taipower Company was tested and the test results confirmed that the proposed methods can improve load forecasting and thus improve quality of power supply for feeders. The results also provide more flexible scheduling policy for power dispatching units.
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