USENSYS is a linear programming optimization model for the United States energy system. It belongs to a class of models known as Macro Energy Models, also called Reference Energy System Models or Capacity Expansion Models (particularly in the context of the electric power sector). USENSYS optimizes the long-term development of the energy system considering alternative technological options, available resources, and policy options. Its primary objective is to determine the cost-optimal capacity expansion of technological chains while accounting for the current state of the system, resource- and policy-constraints.

The usensys package provides an R interface for this model, offering functions to either design new versions or utilize a pre-configured one. It enables users to set up inputs for scenarios, run simulations, and analyze outcomes. This framework can be particularly useful for academic researchers and consultants specializing in energy systems and policy analysis, who is familiar with R, as it integrates seamlessly with existing data analysis and visualization tools. [Native Python and Julia interfaces with equivalent features are under consideration.]

The usensys project is driven by the following goals:

• A universal interface for a versatile, scalable modeling framework
• Open-source accessibility, free use, and development
• Transparency in modeling assumptions and results
• Community building to foster collaboration
• More and better research through collective efforts.

As open-source models are increasingly becoming the standard and best practice in energy modeling, USENSYS aims to contribute to this trend by providing transparency and accessibility.

As a Macro/Reference Energy System model, USENSYS can be used to simulate energy pathways by optimizing energy investments over multiple decades under various conditions. It enables scenario analysis involving different technological options, costs, policy measures, energy demand, economic structures, and other key assumptions.

The package emphasizes flexibility in both data and model structure, adopting a modular approach that facilitates customization of data sources and model components. The model is built on the energyRt R-package, a model generator that includes a framework of approximately one hundred pre-built constraints, providing significant flexibility in designing technologies, sectors, and linkages. These constraints are activated based on model or scenario-specific data. Users can also add custom constraints via the R interface, allowing further adaptability. No operational research programming is required to run the suite of models.

Several versions of USENSYS are available, each at different stages of development. All versions are included in the package and differ in their geographical scope, regional aggregation, data sources, or in some cases, replicate existing models developed by institutions like EPA and NREL. Currently, USENSYS focuses on the electric power sector, but future versions will extend to other economic sectors.

The following versions are in preparation for release:

• 26-zones: Divides the United States into 26 zones based on NERC sub-regions. This version is part of the Multimodel Intercomparison Project (MIP) and uses PowerGenome data.
• 50-states: Covers all 50 U.S. states, including Alaska and Hawaii, with data from EIA and NREL ATB. This version facilitates integration with other sectors and supports state-level policy analysis.
• IPM: Based on the IPM model developed by EPA, with data sourced from IPM and NREL ATB. - IPMX: An extension of the IPM model that includes Mexican states.
• ReEDS: A replica of the ReEDS model by NREL, with data imported from the ReEDS repository.

The data options include several datasets, EIA, NREL, IPM, ReEDS, MERRA2, also data assemblies/processing tools such as PUDL and PowerGenome. The package provides functions to load and preprocess these datasets, making it easier to set up scenarios and run simulations. The package is designed to be flexible, allowing users to incorporate their own data sources and customize the model to their needs.

To run the USENSYS model, you need one of the following optimization software packages:

• Julia with JuMP
• Python with Pyomo
• GLPK (with MathProg/CMPL – a subset of AMPL)
• GAMS

The first three options are open-source and free, while GAMS is commercial software. Large problems typically require a commercial linear solver, such as CPLEX or Gurobi, connected to the optimization software. Smaller models can be solved with open-source solvers like HiGHS, CBC, or GLPK.

The hardware requirements depend on the size of the model and the solver used:

• Small problems: Can be solved on a personal computer with 8 GB of RAM and a modern processor using solvers like HiGHS or CBC, usually within minutes.
• Large problems: Such as those involving numerous zones, technologies, or time periods, may require a high-performance multicore computer or cloud service with 200 GB or more of RAM, with computation times ranging from hours to days.

You can install the development version of usensys from GitHub with:

# install.packages("pak")
pak::pkg_install("energyRt/energyRt")
pak::pkg_install("usensys/usensys")

This is a basic example which shows you how to solve a common problem:

library(usensys)
## basic example code

🚧 This document is under construction.