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Parametric Sensitivity Study for Wind Power Trading through Stochastic Reserve and Energy Market Optimization
CDEEE, Ave Independencia Ctr Heroes,Apartado Postal 1428, Santo Domingo, Dominican Rep. (Computational Renewables)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. (Elektricitetslära, Electricity)
2015 (English)In: Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE, 2015, 82-87 p.Conference paper, Published paper (Refereed)
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Text
Abstract [en]

Trading optimal wind power in energy and regulation markets offer possibilities for increasing revenues as well as impacting security of the system via additional regulation reserve [1]. The bidding in both energy and regulation markets can be computed through stochastic optimization process of both markets as demonstrated in a previous study by Liang [1]. This paper is furthering the previous study by Liang [1] by analyzing the impact of price ratios between energy and reserve market on the revenues for Swedish market. The parametric study reveals that as long as up-regulation prices are below day-ahead energy, the algorithm will bid in both markets to optimize revenue. When regulation prices surpass or equal to day-ahead energy market price then it only bids energy in the regulation market with the current objective function.

Place, publisher, year, edition, pages
2015. 82-87 p.
Series
IEEE Green Technologies Conference, ISSN 2166-546X
Keyword [en]
power generation economics;power markets;power system security;stochastic programming;wind power;Swedish market;day-ahead energy market price;energy market optimization;objective function;optimal wind power trading;parametric sensitivity study;regulation markets;stochastic reserve optimization;system security;up-regulation prices;Linear programming;Optimization;Parametric study;Uncertainty;Wind forecasting;Wind power generation;Reserve market;bidding strategy;electricity market;energy market;power system security;regulation reserve;stochastic optimization;wind power
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-268965DOI: 10.1109/GREENTECH.2015.37ISI: 000380903700012ISBN: 9781479988846 (print)OAI: oai:DiVA.org:uu-268965DiVA: diva2:899724
Conference
Green Technologies Conference (GreenTech), 2015 Seventh Annual IEEE
Available from: 2016-02-02 Created: 2015-12-11 Last updated: 2016-12-19

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Uzunoglu, Bahri

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CiteExportLink to record
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Citation style
  • apa
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Output format
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