Solution of the mixed integer large scale unit commitment problem by means of a continuous Stochastic linear programming model

Power Systems all around the World faced in the last decade a large increase of penetration of power generation produced by Renewable Energy Sources, a large part of which (in particular wind generation and solar generation) is affected by high levels of uncertainty. Being Renewable Energy Sources o...

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Veröffentlicht in:Energy systems (Berlin. Periodical) Jg. 5; H. 2; S. 269 - 284
Hauptverfasser: Siface, D., Vespucci, M. T., Gelmini, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2014
Springer Nature B.V
Schlagworte:
Alternative energy sources
Computer simulation
Economics and Management
Electric utilities
Electricity distribution
Energy
Energy Policy
Energy resources
Energy Systems
Generators
Heuristic
Integer programming
International
Linear programming
Mathematical analysis
Mathematical models
Mixed integer
Numerical analysis
Operations Research/Decision Theory
Optimization
Original Paper
Power plants
Renewable energy sources
Renewable resources
Simulation
Solar power generation
Stochastic models
Stochasticity
Studies
Uncertainty
Zonal market
Medium term market simulator
Unit commitment
Stochastic programming
ISSN:1868-3967, 1868-3975
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Abstract Power Systems all around the World faced in the last decade a large increase of penetration of power generation produced by Renewable Energy Sources, a large part of which (in particular wind generation and solar generation) is affected by high levels of uncertainty. Being Renewable Energy Sources only partially controllable all this uncertainty is transferred into Power System operation. Thus, numerical simulation of Power Systems needs to be able to cope with this uncertainty, that is Stochastic Programming techniques have to be considered. Anyway, numerical simulation of Power Systems also involves a very large number of variables, some of which are of integer nature: a large scale mixed integer stochastic problem has thus to be solved, but the requirement in computational power and time could reveal to be far too large. Thus, heuristic procedures must be introduced. In this paper it is introduced the sMTSIM model, based on a Stochastic continuous relaxation of the mixed integer Unit Commitment problem and on an heuristic procedure capable of re-introducing the mixed integer constraints.
AbstractList Power Systems all around the World faced in the last decade a large increase of penetration of power generation produced by Renewable Energy Sources, a large part of which (in particular wind generation and solar generation) is affected by high levels of uncertainty. Being Renewable Energy Sources only partially controllable all this uncertainty is transferred into Power System operation. Thus, numerical simulation of Power Systems needs to be able to cope with this uncertainty, that is Stochastic Programming techniques have to be considered. Anyway, numerical simulation of Power Systems also involves a very large number of variables, some of which are of integer nature: a large scale mixed integer stochastic problem has thus to be solved, but the requirement in computational power and time could reveal to be far too large. Thus, heuristic procedures must be introduced. In this paper it is introduced the sMTSIM model, based on a Stochastic continuous relaxation of the mixed integer Unit Commitment problem and on an heuristic procedure capable of re-introducing the mixed integer constraints.
Issue Title: Managing Uncertainty in Energy Markets (pp 209-369) Power Systems all around the World faced in the last decade a large increase of penetration of power generation produced by Renewable Energy Sources, a large part of which (in particular wind generation and solar generation) is affected by high levels of uncertainty. Being Renewable Energy Sources only partially controllable all this uncertainty is transferred into Power System operation. Thus, numerical simulation of Power Systems needs to be able to cope with this uncertainty, that is Stochastic Programming techniques have to be considered. Anyway, numerical simulation of Power Systems also involves a very large number of variables, some of which are of integer nature: a large scale mixed integer stochastic problem has thus to be solved, but the requirement in computational power and time could reveal to be far too large. Thus, heuristic procedures must be introduced. In this paper it is introduced the sMTSIM model, based on a Stochastic continuous relaxation of the mixed integer Unit Commitment problem and on an heuristic procedure capable of re-introducing the mixed integer constraints.[PUBLICATION ABSTRACT]
Author Siface, D.
Vespucci, M. T.
Gelmini, A.
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  surname: Vespucci
  fullname: Vespucci, M. T.
  organization: Universitá degli Studi di Bergamo
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  surname: Gelmini
  fullname: Gelmini, A.
  organization: RSE SpA
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CitedBy_id crossref_primary_10_1007_s12667_017_0255_7
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Cites_doi 10.1109/59.535691
10.1109/PMAPS.2010.5528999
10.1109/PMAPS.2006.360195
10.1109/TPWRS.2010.2048133
10.1109/TPWRS.2006.876672
10.1109/TPWRS.2003.821611
10.1109/TPWRS.2010.2048345
10.1007/978-3-642-45767-8_2
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Medium term market simulator
Unit commitment
Stochastic programming
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Issue Title: Managing Uncertainty in Energy Markets (pp 209-369) Power Systems all around the World faced in the last decade a large increase of penetration of...
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SubjectTerms Alternative energy sources
Computer simulation
Economics and Management
Electric utilities
Electricity distribution
Energy
Energy Policy
Energy resources
Energy Systems
Generators
Heuristic
Integer programming
International
Linear programming
Mathematical analysis
Mathematical models
Mixed integer
Numerical analysis
Operations Research/Decision Theory
Optimization
Original Paper
Power plants
Renewable energy sources
Renewable resources
Simulation
Solar power generation
Stochastic models
Stochasticity
Studies
Uncertainty
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