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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 Chao en_US dc.contributor.author (Authors) 王家琪 zh_TW dc.contributor.author (Authors) Wang, Jia Qi en_US dc.creator (作者) 王家琪 zh_TW dc.creator (作者) Wang, Jia Qi en_US dc.date (日期) 2017 en_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) G0104354032 en_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) 104354032 zh_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/#G0104354032 en_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 system en_US dc.subject (關鍵詞) Optimum contract capacity en_US dc.subject (關鍵詞) Fraction Brownian Motion en_US dc.subject (關鍵詞) Hurst Parameter en_US dc.subject (關鍵詞) Discrete Variation Method en_US dc.subject (關鍵詞) Monte Carlo Simulation en_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 systems en_US dc.type (資料類型) thesis en_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
