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Storage","sub_category":"Hydrogen","monthly_downloads":623,"total_dependent_repos":0,"total_dependent_packages":1,"readme":"# GasModels.jl\n\n\u003cimg src=\"https://lanl-ansi.github.io/GasModels.jl/dev/assets/logo.svg\" align=\"left\" width=\"200\" alt=\"GasModels logo\"\u003e\n\nStatus:\n[![CI](https://github.com/lanl-ansi/GasModels.jl/workflows/CI/badge.svg)](https://github.com/lanl-ansi/GasModels.jl/actions?query=workflow%3ACI)\n[![codecov](https://codecov.io/gh/lanl-ansi/GasModels.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/lanl-ansi/GasModels.jl)\n[![Documentation](https://github.com/lanl-ansi/GasModels.jl/workflows/Documentation/badge.svg)](https://lanl-ansi.github.io/GasModels.jl/stable/)\n\u003c/p\u003e\n\nGasModels.jl is a Julia/JuMP package for Steady-State Gas Network Optimization.\nIt is designed to enable computational evaluation of emerging Gas network formulations and algorithms in a common platform.\nThe code is engineered to decouple problem specifications (e.g. Gas Flow, Expansion planning, ...) from the gas network formulations (e.g. CWP, DWP, CRDWP, ...).\nThis enables the definition of a wide variety of gas network formulations and their comparison on common problem specifications.\n\n**Core Problem Specifications**\n* Gas Flow (gf)\n* Expansion Planning (ne)\n* Load Shed (ls)\n\n\n* ## Supported Formulations\n\nAll formulation names refer to how underlying physics of a gas network is modeled. For example, the `LRWP` model uses a linear representation of natural gas physics. If a model includes valves, then the resulting mathematical optimization problems will be mixed integer since valve controls are discrete.\n\n| Formulation      | Steady-State         | Transient             | Description           |\n| ---------------- | -------------------- | --------------------- | --------------------- |\n| WP               |       Y              |          Y            | Physics is modeled using nonlinear equations. |\n| DWP              |       Y              |          N            | Physics is modeled using nonlinear equations. Directionality of flow is modeled using discrete variables |\n| CWP              |       Y              |          N            | Physics is modeled using nonlinear equations. Pipe flow in each direction is modeled by a nonnegative continuous variable. Complementarity constraints are used to ensure that flow is zero in at least one direction. |\n| CRDWP            |       Y              |          N            | Physics is modeled using convex equations. Directionality of flow is modeled using discrete variables |\n| LRDWP            |       Y              |          N            | Physics is modeled using linear equations. Directionality of flow is modeled using discrete variables |\n| LRWP             |       Y              |          N            | Physics is modeled using linear equations. |\n\n## Basic Usage\n\nNote: Different problem formulations require different types of solvers. For continuous models such as CWP, WP, and LRWP, it is recommended to use Ipopt. For formulations that involve discrete variables—such as DWP, CRDWP, and LRDWP—a mixed-integer nonlinear programming (MINLP) solver like Juniper is required. See the second example for details on configuring Juniper.\n\nOnce GasModels is installed, a optimizer is installed, and a network data file  has been acquired, a Gas Flow can be executed with,\n```\nusing GasModels, Ipopt\nipopt_solver = optimizer_with_attributes(Ipopt.Optimizer)\n#CASE6PATH is exported by GasModels as a utility - this is the recommended default case\nresult = solve_ogf(CASE6PATH, WPGasModel, ipopt_solver)\n```\n\nFor discrete problems requiring the Juniper optimizer, refer to the following example:\n```\nusing GasModels, Ipopt, Juniper, HiGHS\n# Create Juniper solver for MINLP\nnl_solver = optimizer_with_attributes(Ipopt.Optimizer, \"tol\" =\u003e 1e-4)\nmip_solver = optimizer_with_attributes(HiGHS.Optimizer)\njuniper_solver = optimizer_with_attributes(\n    Juniper.Optimizer,\n    \"nl_solver\" =\u003e nl_solver,\n    \"mip_solver\" =\u003e mip_solver\n)\nresult = solve_ogf(CASE6PATH, DWPGasModel, juniper_solver)\n```\n\nSimilarly, an expansion optimizer can be executed with,\n```\nsolve_ne(casepath, FooGasModel, FooSolver())\n```\n\nwhere FooGasModel is the implementation of the mathematical program of the Gas equations you plan to use (i.e. DWPGasModel) and FooSolver is the JuMP optimizer you want to use to solve the optimization problem (i.e. IpoptSolver).\n\n\n## Acknowledgments\n\nThis code has been developed as part of the Advanced Network Science Initiative at Los Alamos National Laboratory.\nThe primary developers are Russell Bent and Kaarthik Sundar, with significant contributions from Skyler Reid, Conrado Borraz-Sanchez, Hassan Hijazi, and Pascal van Hentenryck.\n\nSpecial thanks to Miles Lubin for his assistance in integrating with Julia/JuMP.\n\n\n## License\n\nThis code is provided under a BSD license as part of the Multi-Infrastructure Control and Optimization Toolkit (MICOT) project, C15024.\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["optimal-power-flow","power-network","water","global-optimization","minlp","minlp-solver","mixed-integer-nonlinear-programming","mixed-integer-programming","non-convex-optimization","nonlinear-optimization"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/136","html_url":"https://ost.ecosyste.ms/projects/136"}