Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parallelization scheme

We address the long-term planning of electric power infrastructure under uncertainty. We propose a Multistage Stochastic Mixed-integer Programming formulation that optimizes the generation expansion to meet the projected electricity demand over multiple years while considering detailed operational c...

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Vydáno v:	Optimization and engineering Ročník 21; číslo 4; s. 1243 - 1281
Hlavní autoři:	Lara, Cristiana L., Siirola, John D., Grossmann, Ignacio E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2020 Springer Nature B.V
Témata:	Algorithms Control Electric power Electric power demand Electricity consumption Electricity distribution Engineering Environmental Management Financial Engineering Infrastructure Integer programming Mathematics Mathematics and Statistics Mixed integer Natural gas Natural gas prices Operations Research/Decision Theory Optimization Parallel processing Power flow Research Article Scale models Systems Theory Uncertainty Generation expansion planning Stochastic dual dynamic integer programming Multistage stochastic programming
ISSN:	1389-4420, 1573-2924
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Abstract	We address the long-term planning of electric power infrastructure under uncertainty. We propose a Multistage Stochastic Mixed-integer Programming formulation that optimizes the generation expansion to meet the projected electricity demand over multiple years while considering detailed operational constraints, intermittency of renewable generation, power flow between regions, storage options, and multiscale representation of uncertainty (strategic and operational). To be able to solve this large-scale model, which grows exponentially with the number of stages in the scenario tree, we decompose the problem using Stochastic Dual Dynamic Integer Programming (SDDiP). The SDDiP algorithm is computationally expensive but we take advantage of parallel processing to solve it more efficiently. The proposed formulation and algorithm are applied to a case study in the region managed by the Electric Reliability Council of Texas for scenario trees considering natural gas price and carbon tax uncertainty for the reference case, and a hypothetical case without nuclear power. We show that the parallelized SDDiP algorithm allows in reasonable amounts of time the solution of multistage stochastic programming models of which the extensive form has quadrillions of variables and constraints.
AbstractList	We address the long-term planning of electric power infrastructure under uncertainty. We propose a Multistage Stochastic Mixed-integer Programming formulation that optimizes the generation expansion to meet the projected electricity demand over multiple years while considering detailed operational constraints, intermittency of renewable generation, power flow between regions, storage options, and multiscale representation of uncertainty (strategic and operational). To be able to solve this large-scale model, which grows exponentially with the number of stages in the scenario tree, we decompose the problem using Stochastic Dual Dynamic Integer Programming (SDDiP). The SDDiP algorithm is computationally expensive but we take advantage of parallel processing to solve it more efficiently. The proposed formulation and algorithm are applied to a case study in the region managed by the Electric Reliability Council of Texas for scenario trees considering natural gas price and carbon tax uncertainty for the reference case, and a hypothetical case without nuclear power. We show that the parallelized SDDiP algorithm allows in reasonable amounts of time the solution of multistage stochastic programming models of which the extensive form has quadrillions of variables and constraints.
Author	Siirola, John D. Lara, Cristiana L. Grossmann, Ignacio E.
Author_xml	– sequence: 1 givenname: Cristiana L. surname: Lara fullname: Lara, Cristiana L. email: cristianallara@gmail.com organization: Carnegie Mellon University – sequence: 2 givenname: John D. surname: Siirola fullname: Siirola, John D. organization: Center for Computing Research, Sandia National Laboratories – sequence: 3 givenname: Ignacio E. orcidid: 0000-0002-7210-084X surname: Grossmann fullname: Grossmann, Ignacio E. organization: Carnegie Mellon University
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Snippet	We address the long-term planning of electric power infrastructure under uncertainty. We propose a Multistage Stochastic Mixed-integer Programming formulation...
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SubjectTerms	Algorithms Control Electric power Electric power demand Electricity consumption Electricity distribution Engineering Environmental Management Financial Engineering Infrastructure Integer programming Mathematics Mathematics and Statistics Mixed integer Natural gas Natural gas prices Operations Research/Decision Theory Optimization Parallel processing Power flow Research Article Scale models Systems Theory Uncertainty
Title	Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parallelization scheme
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