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Modeling real-time balancing power market prices using combined SARIMA and Markov processes
KTH, School of Electrical Engineering (EES), Electric Power Systems.
KTH, School of Electrical Engineering (EES), Electric Power Systems.ORCID iD: 0000-0002-8189-2420
2008 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 23, no 2, 443-450 p.Article in journal (Refereed) Published
Abstract [en]

This paper describes modeling of real-time balancingpower market prices by using combined seasonal auto regressiveintegrated moving average (SARIMA) and discrete Markovprocesses. The combination of such processes allows generationof price series with periods where no demand for balancingpower exists. The purpose of the model is simulation of prices toconstruct scenario trees representing possible realization of thestochastic prices. Such scenario trees can be used in planningmodels based on stochastic optimization to generate bid sequencesto the balancing market. The spread of the prices in the treeand the shape of the scenarios are of central importance. Modelparameter estimation methods reflecting the demands on scenariotrees have therefore been used. The proposed model is also appliedto data from the Nordic power market. The conclusion of thispaper is that the developed model is appropriate for modelingreal-time balancing power prices.

Place, publisher, year, edition, pages
2008. Vol. 23, no 2, 443-450 p.
Keyword [en]
Markov processes, power prices, SARIMA processes, scenario generation, time series analysis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-10262DOI: 10.1109/TPWRS.2008.920046ISI: 000258765900021Scopus ID: 2-s2.0-43849104117OAI: oai:DiVA.org:kth-10262DiVA: diva2:213670
Note

QC 20100804

Available from: 2009-04-28 Created: 2009-04-28 Last updated: 2017-12-13Bibliographically approved
In thesis
1. On optimal hydropower bidding in systems with wind power: Modeling the impact of wind power on power markets
Open this publication in new window or tab >>On optimal hydropower bidding in systems with wind power: Modeling the impact of wind power on power markets
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The introduction of large amounts of wind power into power systems will increase the production uncertainties due to unforeseen wind power production variations. This will have a significant impact on the required balance management quantities. The most suitable power source to balance fast production or consumption variations is hydropower because of its flexibility and low operational costs.

This thesis addresses the problem of trading of electricity on the daily marketfrom a hydropower producer perspective in a system with large amounts of wind power. The overall aim is to present models that can be used in the trading decision process. This thesis describes models within three different areas:1. Modeling of the demand for balancing power by using deterministic andstochastic models. The stochastic models are based on stochastic differentialequations.2. Modeling of prices on the day-ahead and real-time markets using deterministic and stochastic models. The stochastic models are based on time series modeling.3. Short-term hydropower scheduling of trading decisions. These problems areformulated as stochastic optimization problems where the market prices arerandom variables.

The first two can be used to simulate the impact of wind power on various market prices, while the third simulates how the hydropower producer responds to market prices. Thereby, the thesis presents the necessary models for short-term scheduling of hydropower for a future system with significant amounts of wind power.

This thesis concludes that the proposed price models are sufficient to reflect the relevant price properties, and that the proposed short-term hydropower scheduling models can be used to simulate the actions taken by the hydropower producer in a system with significant amounts of wind power. This is also supported by the case studies in the appended publications.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. xi, 76 p.
Series
Trita-EE, ISSN 1653-5146 ; 2009:021
Keyword
Hydropower, wind power, stochastic modeling, optimization
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-10266 (URN)978-91-7415-301-9 (ISBN)
Public defence
2009-05-13, E1, Lindstedsvägen 3, KTH, Stockholm, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC 20100804Available from: 2009-05-08 Created: 2009-04-28 Last updated: 2011-01-12Bibliographically approved

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