1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 時間電價系統的最佳契約容量
Optimal contract capacities for Time-of-Use electricity pricing systems
作者 王家琪
Wang, Jia Qi
貢獻者 洪英超
Hung, Ying Chao
王家琪
Wang, Jia Qi
關鍵詞 時間電價系統
最佳化契約容量
分形布朗運動
赫斯特指數
離散變異法
蒙地卡羅模擬
Time-of-Use pricing system
Optimum contract capacity
Fraction Brownian Motion
Hurst Parameter
Discrete Variation Method
Monte Carlo Simulation
日期 2017
上傳時間 10-Aug-2017 09:43:07 (UTC+8)
摘要 隨著各行各業的飛速發展、科技的不斷進步，一般的公司行號、工廠及現代化的建築對於電力需求大大增加。但是在有限的電力資源下，有時候一到用電高峰時期，很難滿足各行各業的用電需求，因此難免會出現很多地方在用電高峰期跳電的情況。電力公司為了更加有效的分配電力，提出所謂時間電價的概念，和用戶實現簽訂各自的契約容量，將這個契約容量作為每個月分配給各個用戶的最大電量標準。對於用戶來說，若選擇相對較低的契約容量，其所需要負擔的基本電費會較低。然而，當用電量超過契約容量時，用戶可能需要支付非常高額的罰款；若選擇相對較高的契約容量，雖然其支付高額罰款的機率會降低很多，但是所需要負擔的基本電費會增多。因此，對於電力公司和用戶而言，使用時間電價系統，來選擇一個適當的且最佳化的契約容量，已然成為一個非常重要的課題。本文介紹如何用分形布朗運動的模型，來描述用戶用電量趨勢，同時介紹了如何估計分形布朗運動模型中的各個參數。本文也介紹如何建立每月總電費期望值的估計方程式，並利用估計出來的用電量分形布朗運動模型來搜尋最佳化的契約容量。最後，本文以美國密西根州的安娜堡的居民住宅大樓用電量為數據資料作為研究的實例，進一步的提出並論證了選擇最佳化契約容量的方法。
Over the last few decades, the advances in technology and industry have significantly increased the need of electric power, while the power resource is usually limited. In order to best control the power usage, a so-called Time-of-Use (TOU) pricing system is recently developed so that different rates over different seasons and/or weekly/daily peak periods are charged (this is different from the traditional pricing system with flat rate contracts). An important feature of the TOU system is that the consumers have to pre-select the power contract capacities (i.e. the maximum power demands claimed by consumers over different pricing periods) so that the electricity tariff can be calculated accordingly. This means that risk is transferred from the retailer side to the consumer side -- one has to pay more if a larger contract capacity is selected but can potentially mitigate the penalty charge placed when the maximum demand exceeds the contract level. In this thesis, a general stochastic modeling framework for consumer`s power demand based on which the contract capacities of a Time-of-Use pricing system can be best selected so as to minimize the mean electricity price. Due to the observed nature of self-similarity and time dependence, the power demand over a homogeneous peak period is modeled as a constant mean with the noise described by a scaled fractional Brownian motion. However, the underlying optimization problem involves an intricate mathematical formulation, thus requiring techniques such as Monte Carlo simulation and numerical search so as to estimate the solution. Finally, a real data set from Ann Arbor, Michigan along with two pricing systems are used to illustrate our proposed method.
參考文獻 [1] 游振利 (2015)，《利用隨機模型訂定電力之最佳契約容量》，國立政治大學統計學研究所碩士學位論文。
[2] 陳建，譚獻海，賈真(2006)，「7種Hurst係數估計算法的性能分析」，《計算機應用》，1001-9081(2006)04-0945-03
[3] Ross, S. M. (1996). Stochastic Processes, 2nd Ed., New York: John Wiley & Sons.
[4] Vardar, C. (2011). Results on the supremum of fractional Brownian motion, Hacettepe Journal of Mathematics and Statistics, 40(2), 255-264
[5] Chiung-Yao Chen and Ching-Jong Liao (2011), A linear programming approach to the electricity contract capacity problem, Applied Mathematical Modelling, 35 4077–4082
[6] Achard, S., & Coeurjolly, J. F. (2010). Discrete variations of the fractional Brownian motion in the presence of outliers and additive noise. Statistics Surveys, 4, 117-147.
[7] Beran, J. (1994). Statistics for Long-Memory Process. New York: Chapman & Hall.
[8] Coeurjolly, J. F. (2001). Estimating the parameters of a fractional Brownian motion by discrete variations of its sample paths, Statistics Inference for Stochastic Processes, 4, 199-227.
[9] J.B. Bassingthwaighte and G.M. Raymond (1994), Evaluating rescaled range analysis for time series, Annals of Biomedical Engineering, 22, pp. 432-444.
[10] J. Beran, R. Sherman, M.S. Taqqu, and W. Willinger (1995), Long-range dependence in variable-bit-rate video tra_c, IEEE Trans. on Communications, 43, pp. 1566-1579.
[11] K. L. Chung (2001), A Course in Probability Theory, Academic Press, San Diego, third ed., 2001.
[12] I. Daubechies (1992), Ten lectures on wavelets, CBMS-NSF Regional Conference Series, SIAM, 1992.
[13] M. S. Crouse and R. G. Baraniuk (1999), Fast, exact synthesis of Gaussian and nonGaussian long range dependent processes. Submitted to IEEE Transactions on Information Theory, 1999.
[14] J.D. Gibbons (1971), Nonparametric Statistical Inference, McGraw-Hill, Inc.
[15] B.B. Mandelbrot and J.W. van Ness (1968), Fractional Brownian motions, fractional noises and appli-cations, SIAM Review, 10, pp. 422-437.
[16] M.B. Priestley (1981), Spectral analysis and time series, vol. 1, Academic Press.
描述 碩士
國立政治大學
統計學系
104354032
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104354032
資料類型 thesis
dc.contributor.advisor 洪英超zh_TW
dc.contributor.advisor Hung, Ying Chaoen_US
dc.contributor.author (Authors) 王家琪zh_TW
dc.contributor.author (Authors) Wang, Jia Qien_US
dc.creator (作者) 王家琪zh_TW
dc.creator (作者) Wang, Jia Qien_US
dc.date (日期) 2017en_US
dc.date.accessioned 10-Aug-2017 09:43:07 (UTC+8)-
dc.date.available 10-Aug-2017 09:43:07 (UTC+8)-
dc.date.issued (上傳時間) 10-Aug-2017 09:43:07 (UTC+8)-
dc.identifier (Other Identifiers) G0104354032en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111727-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 104354032zh_TW
dc.description.abstract (摘要) 隨著各行各業的飛速發展、科技的不斷進步，一般的公司行號、工廠及現代化的建築對於電力需求大大增加。但是在有限的電力資源下，有時候一到用電高峰時期，很難滿足各行各業的用電需求，因此難免會出現很多地方在用電高峰期跳電的情況。電力公司為了更加有效的分配電力，提出所謂時間電價的概念，和用戶實現簽訂各自的契約容量，將這個契約容量作為每個月分配給各個用戶的最大電量標準。對於用戶來說，若選擇相對較低的契約容量，其所需要負擔的基本電費會較低。然而，當用電量超過契約容量時，用戶可能需要支付非常高額的罰款；若選擇相對較高的契約容量，雖然其支付高額罰款的機率會降低很多，但是所需要負擔的基本電費會增多。因此，對於電力公司和用戶而言，使用時間電價系統，來選擇一個適當的且最佳化的契約容量，已然成為一個非常重要的課題。本文介紹如何用分形布朗運動的模型，來描述用戶用電量趨勢，同時介紹了如何估計分形布朗運動模型中的各個參數。本文也介紹如何建立每月總電費期望值的估計方程式，並利用估計出來的用電量分形布朗運動模型來搜尋最佳化的契約容量。最後，本文以美國密西根州的安娜堡的居民住宅大樓用電量為數據資料作為研究的實例，進一步的提出並論證了選擇最佳化契約容量的方法。zh_TW
dc.description.abstract (摘要) Over the last few decades, the advances in technology and industry have significantly increased the need of electric power, while the power resource is usually limited. In order to best control the power usage, a so-called Time-of-Use (TOU) pricing system is recently developed so that different rates over different seasons and/or weekly/daily peak periods are charged (this is different from the traditional pricing system with flat rate contracts). An important feature of the TOU system is that the consumers have to pre-select the power contract capacities (i.e. the maximum power demands claimed by consumers over different pricing periods) so that the electricity tariff can be calculated accordingly. This means that risk is transferred from the retailer side to the consumer side -- one has to pay more if a larger contract capacity is selected but can potentially mitigate the penalty charge placed when the maximum demand exceeds the contract level. In this thesis, a general stochastic modeling framework for consumer`s power demand based on which the contract capacities of a Time-of-Use pricing system can be best selected so as to minimize the mean electricity price. Due to the observed nature of self-similarity and time dependence, the power demand over a homogeneous peak period is modeled as a constant mean with the noise described by a scaled fractional Brownian motion. However, the underlying optimization problem involves an intricate mathematical formulation, thus requiring techniques such as Monte Carlo simulation and numerical search so as to estimate the solution. Finally, a real data set from Ann Arbor, Michigan along with two pricing systems are used to illustrate our proposed method.en_US
dc.description.tableofcontents 第一章 介紹以及文獻綜述 1
第二章 電量需求模型的建構 4
第一節 電量需求過程 4
第二節 使用分形布朗運動模型建立誤差項模型 9
第三節 參數估計 10
第三章 最佳化契約容量 17
第一節 最佳化契約容量問題 17
第二節 最佳化契約容量問題的解決 20
第三節 利用蒙地卡羅模擬法估計最佳契約容量 25
第四章 實例研究 26
第一節 實例一 26
第二節 實例二 33
第五章 結論及未來方向 35
參考文獻 37
附錄一 39
附錄二 40
附錄三 41		zh_TW
dc.format.extent 1837943 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104354032en_US
dc.subject (關鍵詞) 時間電價系統zh_TW
dc.subject (關鍵詞) 最佳化契約容量zh_TW
dc.subject (關鍵詞) 分形布朗運動zh_TW
dc.subject (關鍵詞) 赫斯特指數zh_TW
dc.subject (關鍵詞) 離散變異法zh_TW
dc.subject (關鍵詞) 蒙地卡羅模擬zh_TW
dc.subject (關鍵詞) Time-of-Use pricing systemen_US
dc.subject (關鍵詞) Optimum contract capacityen_US
dc.subject (關鍵詞) Fraction Brownian Motionen_US
dc.subject (關鍵詞) Hurst Parameteren_US
dc.subject (關鍵詞) Discrete Variation Methoden_US
dc.subject (關鍵詞) Monte Carlo Simulationen_US
dc.title (題名) 時間電價系統的最佳契約容量zh_TW
dc.title (題名) Optimal contract capacities for Time-of-Use electricity pricing systemsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] 游振利 (2015)，《利用隨機模型訂定電力之最佳契約容量》，國立政治大學統計學研究所碩士學位論文。
[2] 陳建，譚獻海，賈真(2006)，「7種Hurst係數估計算法的性能分析」，《計算機應用》，1001-9081(2006)04-0945-03
[3] Ross, S. M. (1996). Stochastic Processes, 2nd Ed., New York: John Wiley & Sons.
[4] Vardar, C. (2011). Results on the supremum of fractional Brownian motion, Hacettepe Journal of Mathematics and Statistics, 40(2), 255-264
[5] Chiung-Yao Chen and Ching-Jong Liao (2011), A linear programming approach to the electricity contract capacity problem, Applied Mathematical Modelling, 35 4077–4082
[6] Achard, S., & Coeurjolly, J. F. (2010). Discrete variations of the fractional Brownian motion in the presence of outliers and additive noise. Statistics Surveys, 4, 117-147.
[7] Beran, J. (1994). Statistics for Long-Memory Process. New York: Chapman & Hall.
[8] Coeurjolly, J. F. (2001). Estimating the parameters of a fractional Brownian motion by discrete variations of its sample paths, Statistics Inference for Stochastic Processes, 4, 199-227.
[9] J.B. Bassingthwaighte and G.M. Raymond (1994), Evaluating rescaled range analysis for time series, Annals of Biomedical Engineering, 22, pp. 432-444.
[10] J. Beran, R. Sherman, M.S. Taqqu, and W. Willinger (1995), Long-range dependence in variable-bit-rate video tra_c, IEEE Trans. on Communications, 43, pp. 1566-1579.
[11] K. L. Chung (2001), A Course in Probability Theory, Academic Press, San Diego, third ed., 2001.
[12] I. Daubechies (1992), Ten lectures on wavelets, CBMS-NSF Regional Conference Series, SIAM, 1992.
[13] M. S. Crouse and R. G. Baraniuk (1999), Fast, exact synthesis of Gaussian and nonGaussian long range dependent processes. Submitted to IEEE Transactions on Information Theory, 1999.
[14] J.D. Gibbons (1971), Nonparametric Statistical Inference, McGraw-Hill, Inc.
[15] B.B. Mandelbrot and J.W. van Ness (1968), Fractional Brownian motions, fractional noises and appli-cations, SIAM Review, 10, pp. 422-437.
[16] M.B. Priestley (1981), Spectral analysis and time series, vol. 1, Academic Press.		zh_TW