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A Stochastic Programming Model for the Thermal Optimal Day-Ahead Bid Problem with Physical Futures Contracts

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Bibliographic Details
Title: A Stochastic Programming Model for the Thermal Optimal Day-Ahead Bid Problem with Physical Futures Contracts
Authors: Corchero García, Cristina, Heredia, F.-Javier (Francisco Javier)
Contributors: Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Recercat. Dipósit de la Recerca de Catalunya
Universitat Jaume I
Publisher Information: 2009.
Publication Year: 2009
Subject Terms: Classificació AMS::90 Operations research, Optimal bid, Programming (Mathematics), Matemàtiques i estadística::Investigació operativa::Programació matemàtica [Àrees temàtiques de la UPC], mathematical programming::90C Mathematical programming, Classificació INSPEC::Optimisation::Mathematical programming::Stochastic programming, Stochastic programming, Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització, 90 Operations research, mathematical programming::90C Mathematical programming [Classificació AMS], Optimisation::Mathematical programming::Stochastic programming [Classificació INSPEC], Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, Classificació AMS::90 Operations research, mathematical programming::90B Operations research and management science, mathematical programming::90B Operations research and management science, Futures contracts, 90 Operations research, mathematical programming::90B Operations research and management science [Classificació AMS], Programació (Matemàtica), Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica, Electricity day-ahead market, Matemàtiques i estadística::Investigació operativa::Optimització [Àrees temàtiques de la UPC]
Description: The reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market. One main characteristic of MIBEL’s Derivatives Market is the existence of physical futures contracts; they imply the obligation to settle physically the energy. The market regulation establishes the mechanism for including those physical futures in the day-ahead bidding of the Generation Companies. The goal of this work is to optimize coordination between physical futures contracts and the Day-Ahead bidding which follow this regulation. We propose a stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy and power planning production (unit commitment) for the thermal units of a price-taker Generation Company. The uncertainty of the day-ahead market price is included in the stochastic model through a set of scenarios. Implementation details and some first computational experiences for small real cases are presented.
Document Type: Report
File Description: application/pdf
Language: English
Access URL: https://hdl.handle.net/2117/2795
http://hdl.handle.net/2117/2795
Rights: CC BY NC ND
Accession Number: edsair.dedup.wf.002..f9dd5311b762db8b93c16bebbf91cf3f
Database: OpenAIRE
Description
Abstract:The reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market. One main characteristic of MIBEL’s Derivatives Market is the existence of physical futures contracts; they imply the obligation to settle physically the energy. The market regulation establishes the mechanism for including those physical futures in the day-ahead bidding of the Generation Companies. The goal of this work is to optimize coordination between physical futures contracts and the Day-Ahead bidding which follow this regulation. We propose a stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy and power planning production (unit commitment) for the thermal units of a price-taker Generation Company. The uncertainty of the day-ahead market price is included in the stochastic model through a set of scenarios. Implementation details and some first computational experiences for small real cases are presented.