Conference paper
Stochastic Unit Commitment via Progressive Hedging - Extensive Analysis of Solution Methods
Department of Electrical Engineering, Technical University of Denmark1
Center for Electric Power and Energy, Centers, Technical University of Denmark2
Energy Analytics and Markets, Center for Electric Power and Energy, Centers, Technical University of Denmark3
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark4
Department of Applied Mathematics and Computer Science, Technical University of Denmark5
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark6
Owing to the massive deployment of renewablepower production units over the last couple of decades, the useof stochastic optimization methods to solve the unit commitmentproblem has gained increasing attention. Solving stochastic unitcommitment problems in large-scale power systems requires high computational power, as stochastic models are dramaticallymore complex than their deterministic counterparts.
This paperprovides new insight into the potential of Progressive Hedgingto decrease the solution time of the stochastic unit commitmentproblem with a relatively small trade-off in terms of thesuboptimality of the solution. Computational studies show thatthe run-time is at most half of what is needed to solve theoriginal extensive formulation of the problem, when more thanten wind power scenarios are utilized.
These studies demonstrategreat potential for solving real-world stochastic unit commitmentproblems using the Progressive Hedging algorithm.
Language: | English |
---|---|
Publisher: | IEEE |
Year: | 2015 |
Pages: | 1-6 |
Proceedings: | 2015 IEEE Eindhoven PowerTech |
ISBN: | 1479976938 , 1479976954 , 9781479976935 , 9781479976959 , 147997692X and 9781479976928 |
Types: | Conference paper |
DOI: | 10.1109/PTC.2015.7232629 |
ORCIDs: | Ordoudis, Christos and Pinson, Pierre |
Electricity market operations Progressive hedging SDG 7 - Affordable and Clean Energy Stochastic unit commitment Wind power
Computational modeling Linear programming Production Programming Stochastic processes Uncertainty Wind power generation computational power extensive analysis large-scale power systems power generation dispatch power generation scheduling power system simulation progressive hedging solution methods solution suboptimality stochastic unit commitment stochastic unit commitment problems wind power