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標題: 風力發電量隨機模型之研究
Development of a Stochastic Model for Wind Power Generation
作者: 吳予馨
Wu, Yu-Sin
Contributors: 蔡玫亭
Mei-Ting Tsai
企業管理學系所
關鍵字: 隨機模型;季節性;最大概似估計
stochastic model;seasonality;maximum likelihood estimation
日期: 2013
Issue Date: 2013-11-18 11:18:06 (UTC+8)
Publisher: 企業管理學系所
摘要: 近年來氣候暖化與能源短缺問題日益嚴重,發展綠色替代能源刻
不容緩。其中,風力發電是目前普遍使用且技術成熟,具商業價值的
再生能源。傳統推估風機發電量的方法是利用台灣歷年風況資料計算
出風速機率分配,並配合標準性能曲線、風機預定高度…等因素來預
估。但即使已有成熟的發電理論公式,由於實際發電量會受到天候變
化或發電機本身遭遇例行性維護或故障維修等因素而有變動,可能影
響預估發電量的準確性。
為了解決實際發電量與預估發電量有不一致的情況,本研究從隨
機程序的觀點,使用「幾何布朗運動」及「均值復歸」兩種隨機程序
建立發電量模型。而針對發電量在某間斷時間點有大幅波動的現象,
我們以「跳躍模型」以及「季節模型」來解釋。之後,針對台灣電力
公司所提供之歷年實際運轉資料進行隨機模式之檢定,並使用最大概
似法估計參數,最後根據估計出的參數進行比較,並藉由模擬發電量
來預測未來的發電量,選擇最適合的風力發電模型。
根據最大概似估計法及模擬值的結果顯示:使用「季節模型」比
使用「跳躍模型」對於發電量波動現象具有較高的解釋能力。而使用
均值復歸模型比使用幾何布朗運動模型更能解釋風力發電量的預估結
果。
In recent years, climate change and energy shortages have been
challenging the world. It is extremely critical to develop green alternative
energies. Among these green energies, wind power is renewable, and has
good performances in terms of energy security, environmental protection
and economic concerns. It is currently competitive and widely used due to
its mature technology and commercial value. The traditional approach to
estimate the energy production of wind turbines is by calculating the
distribution of wind speeds from historical data of the wind regime in
Taiwan, and then combined with the wind turbine height and the nominal
performance curve provided by manufacturers. Even though there are
well-developed theoretical formulas to calculate energy production, but the
actual amount fluctuates due to factors such as weather changes, routine
maintenance, or occasional repair of the generator. These interrelated
uncertainties may affect the accuracy in estimating the energy production.
In order to solve the inconsistency between the actual and the
estimated power generated, this study applies the perspective of stochastic
process to building a wind turbine power model. Two continuous stochastic
processes are in use: Geometric Brownian Motion process (GBM) and
mean reversion process (MR). “Jump model” and “seasonal model” are
used to describe the uncertain factors power plants face, such as fan failure,
turbine maintenance, etc. Afterwards, stochastic process tests are used on
the actual operation data provided by Taiwan Power Company, and then
estimate the parameters with maximum likelihood estimation (MLE).
Finally, the crucial parameters were compared to choose the most suitable
values for wind power generation model.
The result based on maximum likelihood estimation method reveals
that: the “seasonal model” is greater than the “jump process model”, and
MR model has better predictions in the energy production of wind turbines
than GBM model.
Appears in Collections:[依資料類型分類] 碩博士論文

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