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MESSAGEix Integrated Assessment Model Framework

repository·main·Indexed 41 days ago

https://github.com/iiasa/message_ix

A modeling framework for energy-engineering-economy-environment (E4) systems research. It provides Python and R APIs to build and run complex optimization models, including MESSAGE (linear programming) and MACRO (economic general equilibrium), and integrates with the ixmp modeling platform for scenario analysis and GAMS execution.

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What's inside MESSAGEix

  1. Overview of the MESSAGEix framework

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    MESSAGEix is a versatile, dynamic modeling framework designed for energy-engineering-economy-environment (E4) systems research. It provides tools for strategic energy planning and integrated assessment of E4 systems.

    The framework includes:

    • Implementations of the MESSAGE (linear programming optimization model) and MACRO (economic general equilibrium model) models, including their linkage, in GAMS.
    • APIs and tools for model building and scientific programming in Python and R.
    • A data warehouse feature for numerical scenario analysis, built on top of IIASA's ixmp (ix modeling platform).

    Developers can use message_ix to build complex E4 models, perform scenario analysis, and link optimization models with economic equilibrium models.

  2. Explore MESSAGEix usage in publications and research

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    MESSAGEix is a flexible framework used for a wide variety of energy systems research. Because the model formulation is highly adaptable, it is applied across different spatial scales (from local sites like Malé, Maldives, to global scales) and domains (energy-water nexus, climate mitigation, socioeconomic scenarios, and renewable energy integration).

    Key research applications include:

    • Energy-Water Security: Modeling regional systems with sub-annual time slices (e.g., Central Asia).
    • Climate Mitigation: Capturing global economy and energy dynamics, including responses to external shocks like COVID-19.
    • Sustainable Development: Calculating investment needs for the Paris Agreement and Sustainable Development Goals (SDGs).
    • Renewable Integration: Evaluating the role of seasonal storage (like pumped hydro) in high-renewable energy sectors (e.g., Brazil).

  3. Use LPdiag for LP problem diagnostics

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    The LPdiag tool provides diagnostics for Linear Programming (LP) problems defined in MPS format. It focuses on identifying numerical outliers to help ensure the stability of optimization solvers. A common rule of thumb is that the maximum and minimum orders of magnitude of LP matrix coefficients should differ by at most four.

    LPdiag helps you identify outliers to decide if you should:

    • Reconsider measurement units for variables and relations.
    • Replace very small elements with zero.
    • Split rows or columns.
    • Verify coefficient values.
    message-ix lp-diag

  4. Understand MESSAGEix versioning and compatibility

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    MESSAGEix follows semantic versioning. Understanding the version increments helps you manage dependencies and code stability:

    • Major version increments (e.g., 3.5 to 4.0): Indicate backwards-incompatible changes. Code written for a previous major version may break. Always check the /whatsnew documentation during a major upgrade for migration notes.
    • Minor version increments (e.g., 3.5 to 3.6): Indicate bug fixes or new features that do not change existing functionality. Code written for an older minor version will continue to work with a newer one.

    Note that message_ix and ixmp releases are generally synchronized in version numbers, but each version of message_ix requires a specific minimum version of ixmp to function correctly.

  5. Understand LPdiag analysis results

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    The output of LPdiag provides a detailed numerical profile of the LP problem, including:

    • Problem Characteristics: Number of rows, columns, non-zero coefficients, and value distributions.
    • Coefficient Distributions: Distributions of objective function coefficients, absolute values of matrix elements, and orders of magnitude (int(log10(abs(values)))).
    • Outlier Analysis: For both lower and upper tails (defined by magnitude orders):
      • Distribution of values (value, absolute value, and log10 magnitude).
      • Count of elements per order of magnitude.
      • Row-wise location: Identifies outliers in rows and provides context on other coefficients in that row and the row's LHS/RHS magnitudes.
      • Column-wise location: Identifies outliers in columns and provides context on other coefficients in that column and the column's lower/upper bound magnitudes.
    • Execution Metadata: Processing start and end times.

  6. How to parameterize technology efficiency in MESSAGEix

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    MESSAGEix uses an agnostic formulation for technology efficiency, meaning you do not define a single 'efficiency' parameter. Instead, you define input and output parameters to determine whether a technology is input-defined or output-defined.

    Key Rules:

    • The decision variables CAP_NEW, CAP, and ACT, as well as all bounds, are always understood to be in the same units as the chosen parametrization.
    • All cost parameters must be provided in monetary units corresponding to those same units.
    • There is no automatic rescaling performed by the ixmp API or the GAMS implementation.

    Parametrization Strategies:

    1. Output-defined (e.g., Power Plants): Use this when technical specs are given per unit of output (e.g., per MWh of electricity).

      • Set output = 1 for the primary output commodity.
      • Set input = 1 / efficiency.

    2. Input-defined (e.g., Refineries): Use this when costs and emissions are scaled per unit of input (e.g., per barrel of crude oil).

      • Set input = 1 for the primary input commodity.
      • Set output parameters for each output product accordingly.

    3. Multi-mode/Multi-output (e.g., Combined Heat and Power): You can define multiple modes of operation by setting different input and output ratios for each mode. Note that in these cases, the activity level represents the amount of the primary output generated if that output were maximized.

  7. Understand the differences between MESSAGE, MACRO, and MESSAGE_MACRO

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    MESSAGEix provides three distinct model classes depending on the scope of the simulation:

    1. message_ix.message.MESSAGE: The core mathematical formulation.

      • Key option: cap_comm (bool). If True, includes commodity flows associated with capacity changes of technologies in the commodity balance equations. This can increase LP size and solve time.

    2. message_ix.macro.MACRO: Solves the MACRO model in "standalone" mode (without MESSAGE).

      • Key option: concurrent (bool). If False (default), MACRO is solved in a loop for each node. If True, it is solved once for all nodes simultaneously.

    3. message_ix.message_macro.MESSAGE_MACRO: An iterative solver that connects MESSAGE and MACRO. It iterates until the two models reach a stable point (convergence).

      • max_adjustment (float, default 0.2): Limits the relative change in final demands between iterations.
      • convergence_criterion (float, default 0.01): The threshold for relative change in final demands to signal completion.
      • max_iteration (int, default 50): The maximum number of iterations allowed before the run fails.

  8. How the MESSAGEix framework components work together

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    MESSAGEix is a framework for developing and running energy system models. It consists of several interconnected components:

    • Mathematical Formulations: Includes MESSAGE (strategic energy planning), MACRO (general-equilibrium model), and the MESSAGE-MACRO link. These are implemented in the GAMS mathematical programming language.
    • ixmp (Modeling Platform): A data warehouse software that provides the foundation for numerical scenario analysis. It manages the workflow between input data, mathematical formulations, and numerical results.
    • Interfaces: The platform is accessible via Python and R APIs, as well as a web-based user interface.
    • Data Handling: The platform handles outputting model data to the GAMS .gdx file format and invoking GAMS code.

  9. Represent policies and regulatory measures in MESSAGEix

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    The MESSAGEix framework allows for the modeling of various mitigation options and policies to analyze transformation pathways. Common implementations include:

    • Emissions constraints: Defining bounds or taxes on emissions to influence the technology mix toward low-emission options.
    • Technology deployment constraints: Setting upper or lower bounds on the deployment of new technologies.

  10. How the `add_year` tool processes data

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    The add_year tool follows a specific workflow to extend a scenario's temporal dimension:

    1. Set Modification: It adds new years to the year and type_year sets. It also updates firstmodelyear, lastmodelyear, baseyear_macro, initializeyear_macro, and cat_year if necessary.
    2. Parameter Extension: It identifies year-related indices (e.g., year_vtg, year_act) for each non-empty parameter.
    3. Interpolation: New years are added to the parameter's index sets, and missing data values are calculated using interpolation of adjacent data points from the reference scenario.
    4. Vintaging Check: For parameters with two year-related indices (like output), the tool performs a final check to ensure vintaging remains correct based on technology lifetimes.
    5. Commit: All changes are committed and saved to the new scenario.

  11. How years and periods are represented

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    In MESSAGEix, the year set indexes parameter dimensions (e.g., year, year_act, year_vtg).

    Key concepts:

    • Representative Years: Each element in the year set is a representative year that marks the final year of a contiguous period.
    • Periods: Periods are contiguous blocks of time. If years are non-consecutive, the period for a year element covers the span from the end of the previous period to the end of that year.
    • Duration: The parameter duration_period stores the length of each period in years. MESSAGEix requires year elements to be integers, meaning all periods must have a duration that is a whole number of years.

    Example of non-consecutive years: If the year set is [1000, 1010, 1020], the element 1010 refers to the period from 1001-01-01 to 1010-12-31 (a 10-year duration).

  12. How the MESSAGE-MACRO algorithm works

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    MESSAGE-MACRO is an iterative algorithm that links the MESSAGE and MACRO models to model demand elasticity. It creates a feedback loop where:

    1. MESSAGE provides energy prices (PRICE_COMMODITY) and total system costs (COST_NODAL_NET) to MACRO.
    2. MACRO provides endogenous demand and GDP data back to MESSAGE.

    The process continues until a convergence criterion (equilibrium) is reached, meaning the MACRO demand output varies minimally between two iterations.

    You can activate this coupled mode by calling .Scenario.solve(model='MESSAGE-MACRO') or by running the GAMS script MESSAGE-MACRO_run.gms directly.

    # To solve a scenario using the MESSAGE-MACRO coupled mode
scenario.solve(model='MESSAGE-MACRO')