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

摘要

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 multi-scale 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.