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    題名: 用戶別售電量與電費收入之研究:台電公司實證案例
    A Study on Customer-by-Category Energy Sales and Power Sales Revenue Model: The Case of Taiwan Power Company
    作者: 蔡佩容
    貢獻者: 許志義
    蔡佩容
    關鍵詞: 售電量
    電費收入
    群集分析
    時間序列
    複迴歸模型
    energy sales
    power sales revenue
    cluster analysis
    ARIMA time series
    multiple regression model
    日期: 2003
    上傳時間: 2009-09-18 10:56:33 (UTC+8)
    摘要: 本文旨在檢定台電公司現行季節電價月份劃分之合理性，並探討影響用戶別售電量與電費收入之經濟因素。為達成此目的，本文先就負載觀點與成本觀點進行群集分析，以檢定季節電價是否具統計意義之正當性；其次建立經濟計量模型，分別採用戶別之總售電量與總電費收入做為被解釋變數，運用民國88年1月至民國91年12月之月資料進行實證分析。本文建立之經濟模型有二，分別為時間序列以及複迴歸方程式模型。經檢定分析後，本文就各實證參數之經濟意涵加以闡示，最後並提出結論以及未來研究之方向。
    本文透過月資料之群集分析，顯示夏月相對於非夏月之群集差異與台電公司現行季節電價夏月與非夏月之月份相一致，證實台電公司季節電價月份劃分之合理性。其次，透過ARIMA時間序列建立之短期電力需求預測模型，經實證結果顯示：電燈與電力用戶別之售電量均逐年增加，預測民國93年1月至民國99年12月，電燈用戶之年售電量平均成長率為3.33%、電力用戶為3.23%。再者，利用複迴歸模型進行實證分析之結果發現：(一)影響售電量之主要變數為溫度。惟因電燈用戶每隔兩月抄表一次，與電力用戶按月抄表之作業方式不同，故電燈用戶每月售電量係受前期(月)溫度影響，而電力用戶則受當期(月)溫度影響。(二)各用戶別之總電費收入與售電量有明顯相關，且經估算出各月售電量之電費收入彈性顯示：電燈用戶約為0.5，電力用戶約為1。由於總電費收入為總售電量與平均電價之乘積，故電燈用戶之電費收入增加1% 時，其售電量僅增加0.5%，顯示總電費的收入增加係有部分來自於平均電價的提高；換言之，就電燈用戶別而言，其電費收入增減變化之百分比除了會受到售電量增減幅度之影響外，亦反映了平均電價變化的情形。同理，對電力用戶來說，其各月售電量之電費收入彈性接近於1，表示電費收入變化1% 時，售電量亦增加1%，即電費收入之增減變化比例主要受到售電量之同向等幅變化所影響。
    至於各用戶別之電費收入方面，電燈與電力兩類用戶自民國88年初至91年底四年期間均有逐年增加之趨勢，惟電力用戶之年增加幅度有隨時間遞減之現象，且歷年大抵以7-10月份較高，2月份最低。此外，影響用戶別電費收入之解釋變數中，各類用戶之售電量最為顯著，其參數值係隱示每增加一度售電量對其電費收入之影響。其中，電燈用戶之估計參數值為2.69，而電力用戶則為1.35。再者，由其電費收入之售電量彈性係數可以發現：電燈用戶約為1.2，電力用戶約為0.7，顯示電燈用戶總售電量增加1%時，總電費收入增加的幅度大於1%，而電力用戶則相反。推估電力用戶此一彈性係數較電燈用戶低之原因在於：電力用戶與電燈用戶之電價結構不同，前者係採需量電費與能量電費之兩部電價制，而後者僅包含流動電費之一部電價。最後，實證結果亦顯示電力系統之尖峰負載與負載率會影響電費收入，惟其影響幅度不大。
    A Study on Customer-by-Category Energy Sales and Power Sales Revenue Model: The Case of Taiwan Power Company
    Abstract
    The main purposes of this study are to examine the rationality of the seasonal pricing scheme defined by summer and non-summer months and to identify economic factors influencing customer-by-category energy sales and power sales revenue, utilizing the data of Taiwan Power Company (Taipower) as an empirical case. In order to achieve this objective, the cluster analysis from the perspective of load pattern and cost pattern are examined respectively to see if the seasonal pricing scheme has statistical meaning in its pattern differences in terms of summer vs. non-summer season. Second, two economic models including time-series analysis and multiple regression equations are formulated for the empirical case study. The subtotal energy sales and the subtotal power sales revenue by different type of customer categories, i.e. lighting and industrial customers, are set to be the explained variables. Data from January 1999 to December 2002 are collected for modeling simulation tests. The economic meanings and policy implications of the modeling results are elaborated on. And conclusions with directions for further research are presented.
    Through the cluster analysis utilizing monthly data within the time frame mentioned above, empirical research results on the grouping cluster of summer vs. non-summer months shows a consistent trend with those defined by Taipower’s present seasonal pricing scheme. Second, the empirical results of ARIMA time-series model show that the forecasted energy sales of both lighting and industrial customers will be gradually increasing through January 2004 to December 2010, and the average annual growth rate of energy sales for the lighting customer is 3.33%, and for the industrial customer is 3.23%. On the other hand, the empirical research results through the multiple regression equations show that the main factor affecting the energy sales is temperature. Due to the different time schedules for reading electricity meters between the lighting customer and the industrial customer, i.e. the time interval for reading the meter of lighting customers is every two months and for industrial customers is every month, the monthly energy sales of the lighting customer are directly related to the temperature of the previous month, while the monthly sales of the industrial customer are directly related to the temperature of the present month. In addition, for each type of customers, there is an obvious correlation between the total power sales revenue and the total energy sales. Furthermore, the estimated elasticity of the total power sales revenue versus total energy sales is about 0.5 for the lighting customer, and about 1 for the industrial customer.
    Since the total power sales revenue is the product of total energy sales times the average electricity price, when the total power sales revenue increases 1% with the total energy sales only increases 0.5%, it implies that the increase of total power sales revenue not just only comes from the increase of energy sales, but also partially affected by the increase of average electricity price. Similarly, for the industrial customer, when the elasticity of their monthly total power sales revenue versus total energy sales is close to 1, it implies that when the total power sales revenue increases 1%, the total energy sales also increase about 1%. In other words, the change of percentage of the total power sales revenue is mostly attributed to the variation of total energy sales, not because of the average electricity price.
    As for the simulation results of the total power sales revenue, those of the lighting and industrial customers are both gradually increasing between the years 1999 to 2002. However, the increasing pace of the industrial customer tended to slow down. Moreover, both types of the customers possess a similar trend that their total power sales are higher in statistical meaning for the months from July to October, and lower for February, for those above three years. Besides, among the variables affecting each type of customer’s power sales revenue, the energy sales is the most significant one, its parameter implies that whenever the total energy sales increases one unit, i.e. one kwh, it would affect the total power sales revenue by that amount equivalent to the figure of the parameter. According to the empirical results, the estimated parameter mentioned-above of the lighting customer is 2.69, and 1.35 of the industrial customer respectively. That implies one kwh unit price for the lighting customer is 2.69 N.T. dollars, and 1.35 N.T. dollars for the industrial customer. Moreover, from the elasticity of the total energy sales versus the total power sales revenue, it shows that the elasticity of the lighting customer is around 1.2, and the elasticity of the industrial customer is around 0.7. The underlining reason of the difference between the two figures could be that the electricity pricing structure of the lighting and industrial customers are quite different. The industrial customer is charged by two-part tariff including a demand charge for the capacity use and an energy charge for the kwh use. While the lighting customer is charged simply by a single rate, i.e. the energy use. Finally, the empirical results also show that the magnitude of the peak load and the load factor of the whole electricity system also affect the total power sales revenue of each type of the customer, though with much less effect.
    參考文獻: 一、中文部分
    吳東隆 (1989)，《電價措施在台灣地區之實證分析》，台灣工業技術學院工程技術研究所管理組未出版之碩士論文，台北：台灣工業技術學院。
    許志義、陳澤義 (1990)，《台灣地區電力負載管理實證分析》，台灣銀行季刊，第四十一卷第三期，頁36-69。
    劉泰英等 (1991)，《時間電價變動幅度對產業生產成本及用電影響之研究》，台灣經濟研究院研究所，台北。
    薛淑敏 (1991)，《尖離峰發電成本之實證分析—尖峰負載訂價理論與成本會計之整合研究》，台灣大學商學研究所未出版之碩士論文，台北：台灣大學。
    許志義、洪育民 (1992)，《國際油價分析與預測》，經濟叢書(26)，台北：中華經濟研究院。
    林茂文 (1992)，《時間數列分析與預測》，華泰書局，台北。
    于宗先等 (1994)，《台灣地區住宅與商業部門能源消費調查研究》，經濟部能源委員會，台北。
    黃鐘慶 (1995)，《負載特性在合理化電價策略之應用》，中山大學電機工程研究所博士論文，高雄：中山大學。
    許志義、陳澤義 (1995)，《電力經濟學理論與應用》，華泰書局，台北。
    許志義、毛維凌、柏雲昌 (1996)，《台灣中長期能源需求預測》，經濟專論170，台北：中華經濟研究院。
    徐麗萍 (1996)，《溫度因素對台灣地區電力需求及尖峰負載之影響》，政治大學統計研究所未出版之碩士論文，台北：政治大學。
    許志義、王大成 (1999)，「電力輔助服務市場探討」，經濟情勢暨評論，第五卷第四期，頁128-38。
    許志義等 (1999)，《台灣環境、能源、經濟整合模型之建立與溫室氣體減量策略研究》，行政院環保署，台北。
    許志義、王京明、郭婷瑋 (2000)，「台灣二氧化碳減量政策之模擬分析」，經濟專論199，台北：中華經濟研究院。
    洪德生、董金蓮 (2000)，《負載管理目標直訂定及績效評估模式之研究》，台灣經濟研究院，台北。
    錢玉蘭等 (2000)，《供電成本與電價結構之研究》，台北：中華經濟研究院。
    蔡蓉媛 (2000)，《通用迴歸類神經網路在中長期電力需求預測模式之研究》，元智大學工業工程研究所未出版之碩士論文，桃園：元智大學。
    蔡志孟 (2001)，《多元電力市場競爭下訂定負載率差異電價之研究--高壓以上用戶為例》，台北大學企業管理所碩士論文，台北：台北大學。
    楊惠婷 (2001)，《長期需求預測之一研究--以台灣電力需求為例》，淡江大學管理科學學系未出版之碩士論文，台北：淡江大學。
    王京明等(2001)，《電力調度費率結構及計算公式之研究》，經濟部能源委員會委託研究計劃報告，中華經濟研究院，台北。
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    二、英文部分
    Bergstrom, T. and J.F. Mackie-Mason (1991), “Some Simple Analytics Peak-Load Pricing,” The Rand Journal of Economics, 22:2, 241-249.
    Crew, M.A. and P.Kleindorfer (1976), “Peak Load Pricing with a Diverse Technology,” The Bell Journal of Economics, 7:1, 207-231.
    Lillard, L.A. and J. P. Acton (1981), “Seasonal Electricity Demand amd Pricing Analysis with a Variable Response Model,” The Bell Journal of Economics,12:1, 71-92.
    Wender, J.T. (1976), “Peak Load Pricing in the Utility Industry,” The Bell Journal of Economics,7:1, 232-241.
    Wender, J.T. (1976), “Experiments in Seasonal-Time-of-Day Pricing of Electricity to Residential Users,” The Bell Journal of Economics, 7:2, 531-552.
    描述: 碩士
    國立政治大學
    財政研究所
    91255014
    92
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0091255014
    資料類型: thesis
    顯示於類別:[財政學系] 學位論文

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