透過您的圖書館登入
IP:3.147.61.142
  • 學位論文

資訊不對稱:需量競價之實證研究

Asymmetric Information: Empirical Evidence from Demand Bidding Program

指導教授 : 朱建達 陳嘉雯
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


本研究利用212位高壓用電戶於2018-2019年間,每小時用電量及需量競價資料,探討用戶是否會為了提高報酬而去衝高基準用電量。本文依據用戶調整報價及得標的狀態,將用電日進行分類後,使用固定效果模型分別估計用戶轉移生產時段及拉高短時間內用電量的效果。在轉移生產時段方面之實證結果顯示,在調低報價且成功得標的情況下,用戶於基準用電日之平均用電量較平時高出3.99%契約容量單位,而在未調低報價但得標的情況下,用電戶之平均用電量幾乎與平時無異,顯示用戶會以轉移生產排程的方式,將用電時段集中於特定時期後,再以調低報價的方式來增加得標機率。另外在拉高短時間內用電量方面,研究結果發現用戶於基準用電日內皆存在異於平時的用電波動,其中以調低報價且得標的情況下效果最強,而在得標但未調低報價的情形下效果最弱。最後,本研究發現金屬製品製造業在三種基準用電日標記方法下所估計出的用電波動皆遠大於其他行業別。

並列摘要


This study uses hourly electricity consumption and demand bidding data of 212 high-voltage consumers from 2018 to 2019 to investigate whether users will inflate their baseline level in order to increase their reduction rewards? This article classifies days according to user's adjusted quotation and the bid-winning status, and uses a fixed-effect model to estimate the effect of users shifting consumption their time and increasing consumption in a short period of time. The empirical results of the former show that in the case of lowering the quotation and winning the bid, the average consumption during the baseline days is 3.99% higher than usual in terms of the contract capacity unit. In the case of not lower the quotation, the average consumption is almost the same as usual. It shows that users will transfer the production schedule to concentrate the power usage period, and then increase its chance of bid-winning by lowering the quotation. On the other hand, the results found that users have different consumption fluctuations during the the baseline days. The effect is strongest when price is lowered and the bid is won. Finally, we found that the estimated effects of the fluctuations are far greater in the metal product manufacturing industry than those of other industries.

參考文獻


Albadi, M. H., and E. F. El-Saadany. 2008. “A summary of demand response in elec- tricity markets.” Electric Power Systems Research 78(11): 1989–1996.
Bushnell, James, Benjamin Hobbs, and Frank Wolak. 2009. “When It Comes to De- mand Response, Is FERC Its Own Worst Enemy?” The Electricity Journal 22: 9–18.
Chao, Hung-po, and Mario DePillis. 2013. “Incentive effects of paying demand response in wholesale electricity markets.” Journal of Regulatory Economics 43(3): 265–283.
Chen, Y., W. S. Lin, F. Han, Y. Yang, Z. Safar, and K. J. R. Liu. 2012. “A cheat-proof game theoretic demand response scheme for smart grids.” 2012 IEEE International Conference on Communications (ICC): 3362–3366.
Muthirayan, D., D. Kalathil, K. Poolla, and P. Varaiya. 2016. “Mechanism design for self-reporting baselines in Demand Response.” 2016 American Control Conference (ACC): 1446–1451.

延伸閱讀