Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain

被引:102
作者
Gonzalez-Aparicio, I. [1 ]
Zucker, A. [1 ]
机构
[1] Commiss European Communities, Joint Res Ctr, Inst Energy & Transport, Energy Technol Policy Outlook Unit, NL-1755 ZG Petten, Netherlands
关键词
Wind power uncertainty; Multivariate Gaussian mixture model; Wind integration; Market design; ELECTRICITY PRICES; MODEL; GENERATION; DESIGN; COSTS;
D O I
10.1016/j.apenergy.2015.08.104
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The growing share of electricity production from variable renewable energy sources increases the stochastic nature of the power system. This has repercussions on the markets for electricity. Deviations from forecasted production schedules require balancing of a generator's position within a day. Short term products that are traded on power and/or reserve markets have been developed for this purpose, providing opportunities to actors who can offer flexibility in the short term. The value of flexibility is typically modelled using stochastic scenario extensions of dispatch models which requires, as a first step, understanding the nature of forecast uncertainties. This study provides a new approach for determining the forecast errors of wind power generation in the time period between the closure of the day ahead and the opening of the first intraday session using Spain as an example. The methodology has been developed using time series analysis for the years 2010-2013 to find the explanatory variables of the wind error variability by applying clustering techniques to reduce the range of uncertainty, and regressive techniques to forecast the probability density functions of the intra-day price. This methodology has been tested considering different system actions showing its suitability for developing intra-day bidding strategies and also for the generation of electricity generated from Renewable Energy Sources scenarios. This methodology could help a wind power producer to optimally bid into the intraday market based on more accurate scenarios, increasing their revenues and the system value of wind. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:334 / 349
页数:16
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