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    Model Description

    The purpose of MUSE2 (ModUlar energy systems Simulation Environment) is to provide users with a framework to simulate pathways of energy system transition, usually in the context of climate change mitigation.

    Model Concept

    MUSE2 is a bottom-up engineering-economic modelling framework that computes a price-induced supply-demand equilibrium on a set of user-defined commodities. It does this for each milestone time period within a user-defined time horizon. This is a “partial equilibrium” in the sense that the framework equilibrates only the user-defined commodities, as opposed to a whole economy.

    MUSE2 is data-driven in the sense that model processing and data are entirely independent, and user-defined data is at the heart of how the model behaves. It is also “bottom-up” in nature, which means that it requires users to characterise each individual process that produces or consumes each commodity, along with a range of other physical, economic and agent parameters.

    MUSE2 does not require a single set of physical units, but the units used within each model must be consistent. See Units and Dimensions for how capacity, activity, commodity flows, and monetary values are related.

    At a high level, the user defines:

    1. The overall temporal arrangements, including the base time period, milestone time periods and time horizon, and within-period time slice lengths.

    2. The service demands for each end-use (e.g. residential heating, steel production), for each region, and how that demand is distributed between the user-defined time slices within the year. Service demands must be given a value for the base time period and all milestone time periods in each region.

    3. The existing capacity of each process (i.e. assets) in the base time period, and the year in which it was commissioned or will be decommissioned.

    4. The techno-economic attributes (e.g. capital cost, operating costs, efficiency, lifetime, input and output commodities, etc) of each process. This must include attributes of processes existing in the base time period (i.e. assets) and possible future processes that could be adopted in future milestone time periods.

    5. The agents that choose between processes by applying search spaces, objectives and decision rules. Portions of demand for each commodity must be assigned to an agent, and the sum of these portions must be one.

    Framework Overview

    The model operates sequentially across a series of milestone years (MSYs). For the base year, existing assets are commissioned and a dispatch optimisation is run to establish commodity prices for the first investment year. For each subsequent MSY, the model performs the following steps:

    1. Decommission end-of-life assets

    Assets whose scheduled decommissioning year has been reached are removed from the active pool.

    2. Agent investment

    Agents select assets to meet commodity demand for the current MSY. Investment decisions follow a pre-computed investment order derived from the topology of the commodity graph: markets are processed deepest-first (most downstream to most upstream), grouped into layers of independent markets at the same depth. See Investment Appraisal for full details.

    After all markets in each layer are settled, a partial system dispatch is run over all assets selected so far. This propagates demand upstream: input commodity flows consumed by newly committed assets become demand targets for the markets not yet invested in.

    3. Mothballing and decommissioning

    Previously commissioned assets that were not selected for retention are mothballed. Any asset that has remained mothballed for longer than mothball_years (defined in model.toml) is permanently decommissioned.

    4. Ironing-out loop

    Investment and dispatch are repeated iteratively to resolve any price instability introduced by new capacity commitments. Each iteration runs the full agent investment pass followed by a full system dispatch. The loop terminates when time-slice-weighted market prices have converged within price_tolerance, or when max_ironing_out_iterations is reached (both defined in model.toml).

    5. Final dispatch

    A full dispatch optimisation is run over the settled asset pool. The resulting flows and commodity prices are written to the output files and carried forward as the price basis for the next MSY’s investment appraisal.