Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

► Decision making under uncertainty. ► Stochastic Mixed Integer Quadratic Programming applied to short-term maintenance scheduling. ► Outage scheduling in Oligopolistic electricity markets. ► Generation companies maintenance scheduling. In the proposed model, the independent system operator (ISO) pr...

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Published in:Applied energy Vol. 101; pp. 667 - 677
Main Authors: Fotouhi Ghazvini, Mohammad Ali, Canizes, Bruno, Vale, Zita, Morais, Hugo
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.01.2013
Elsevier
Subjects:
algorithms
Applied sciences
business enterprises
Cournot model
decision making
demand functions
Economic data
Electric energy
electricity
Energy
Energy economics
Exact sciences and technology
Game theory
General, economic and professional studies
Maintenance scheduling
markets
Methodology. Modelling
Nash equilibrium
Oligopolistic market
Stochastic mixed integer programming
Oligopolistic market
Nash equilibrium
Stochastic mixed integer programming
Cournot model
Maintenance scheduling
Game theory
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:► Decision making under uncertainty. ► Stochastic Mixed Integer Quadratic Programming applied to short-term maintenance scheduling. ► Outage scheduling in Oligopolistic electricity markets. ► Generation companies maintenance scheduling. In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.
Bibliography:http://dx.doi.org/10.1016/j.apenergy.2012.07.009
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2012.07.009