Scalable Integrated Infrastructure Planning Model
NREL created the Scalable Integrated Infrastructure Planning (SIIP) modeling framework to effectively build, solve, and analyze the scheduling problems and dynamic simulations of quasi-static infrastructure systems.
To meet the needs of evolving energy infrastructure systems, the SIIP model develops a foundation to fundamentally advance the nation's ability to model individual and integrated infrastructure systems at a range of spatial and temporal scales.
The SIIP model applies NREL's capabilities in advanced computer science, visualization, applied mathematics, and computational science to create a first-of its-kind flexible modeling framework that incorporates new solution algorithms, advanced data analytics, and scalable high-performance computing.
Open-Source Software Suite
The first and most mature application deployed in the SIIP model is SIIP::Power, which consists of a suite of open-source software packages available on GitHub:
Provides an efficient power systems data specification along with support for parsing standard power systems data file formats and basic data transformations and calculations.
Enables a range of quasi-static power systems scheduling problem specifications, which includes unit commitment and economic dispatch, automatic generation control, and nonlinear optimal power flow—along with sequential problem specifications to enable production cost modeling techniques.
Allows for the simulation of power system dynamics.
Offers analytical capabilities to visualize simulation results.
Future Development Plans
Ongoing development will integrate problem decomposition and parallel optimization capabilities to further enhance the computational performance of SIIP tools. Enhanced connections to NREL's WIND Toolkit and National Solar Radiation Database renewable energy data will ease the lift to create new datasets.
NREL is also working to expand the SIIP framework to represent other infrastructure systems and sectoral interdependencies in the future.
Computational Experiment Design for Operations Model Simulation, Electric Power Systems Research (2020)