Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parall
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Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parallelization scheme Cristiana L. Lara1 · John D. Siirola2 · Ignacio E. Grossmann1 Received: 1 May 2019 / Revised: 10 August 2019 / Accepted: 29 September 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019
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. Keywords Generation expansion planning · Multistage stochastic programming · Stochastic dual dynamic integer programming
* Cristiana L. Lara [email protected] Ignacio E. Grossmann [email protected] 1
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA
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Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, USA
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1 Introduction Changes in electricity demand, together with the wear-and-tear and retirement of old generators, and the advances in the technology pool for electricity generation and storage, make it necessary to expand or adapt the electric power infrastructure. Generation expansion planning (GEP) models can be used to support these investment decisions, as well as to study the impact of new technology developments, resource cost trends, and policy shifts (e.g. carbon tax, minimum renewable generation quota) (Sadeghi et al. 2017; Koltsaklis and Dagoumas 2018; Babatunde et al. 2018; Gacitua et al. 2018). Power systems are subject to a variety of systematic uncertainties such as fuel prices, load demand, renewable generation, disruptive technologies, and future policies. However, because of the computational expense of combining uncertainty with a complete representation of the grid, and integrating detailed operating decisions with investment decisions over long planning horizons (Shortt and O’
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