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author:

Jiang, Yuewen (Jiang, Yuewen.) [1] (Scholars:江岳文) | Wen, Buying (Wen, Buying.) [2] (Scholars:温步瀛)

Indexed by:

EI Scopus PKU CSCD

Abstract:

There is a certain difference between the ultra-short-term wind power forecast and the actual output power in the real-time market, though it is smaller than that in the day-ahead market, therefore, the influence of wind power uncertainty on the output power regulation of real-time market should be researched. According to the normal distribution characteristics of wind speed in short period, the expression of wind power distribution and its error probability are deduced, and the dispatch model of output power regulation based on the quoted price is built for real-time market, which takes the minimum sum of regulation cost of general generator and error cost of wind power forecast as its objective function. A specific case is taken to analyze the influence of normal distribution parameters, wind power forecast and penalty coefficient on the output power regulation cost, the error cost of wind power forecast and the error expectation is analyzed for the real-time market. Simulative results verify the effectiveness of the proposed model. ©, 2015, Electric Power Automation Equipment Press. All right reserved.

Keyword:

Commerce Costs Electric load dispatching Energy policy Errors Forecasting Normal distribution Weather forecasting Wind Wind power

Community:

  • [ 1 ] [Jiang, Yuewen]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Wen, Buying]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

Reprint 's Address:

  • 江岳文

    [jiang, yuewen]college of electrical engineering and automation, fuzhou university, fuzhou; 350108, china

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Source :

Electric Power Automation Equipment

ISSN: 1006-6047

CN: 32-1318/TM

Year: 2015

Issue: 3

Volume: 35

Page: 12-17

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 14

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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