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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Veröffentlicht in:Optimization and engineering Jg. 21; H. 4; S. 1243 - 1281
Hauptverfasser: Lara, Cristiana L., Siirola, John D., Grossmann, Ignacio E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2020
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-019-09471-0