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Model Description

The purpose of MUSE 2.0 (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

MUSE 2.0 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.

MUSE 2.0 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.

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 framework is designed to operate sequentially across several distinct milestone years (MSY). For each MSY, it endogenously determines asset decommissioning (both scheduled and economically-driven) and guides new capacity investments. The overarching objective is to simulate agent decision-making to serve commodity (and service) demand.

A fundamental premise for the investment appraisal is that prices for balanced commodities (Supply Equals Demand: SED) from the previous milestone year (\( \pi_{prevMSY} \)) are considered reliable for economic evaluations. Service demand commodity (SVD) prices from \( MSY_{prev} \) may or may not be reliable (as defined by the user), guiding the choice of appraisal method for assets producing them. It is designed as a recursive dynamic model with imperfect foresight.

The workflow is structured as follows:

  1. Dispatch is executed for a calibrated base year. Dispatch is executed using the formulation shown in Dispatch Optimisation Formulation. All existing assets are included, and all candidate assets for the \( MSY_{next} \) are included with capacities set to zero. This ensures that all commodity shadow prices and reduced costs for candidate asset are generated for use in the \( MSY_{next} \). \( VoLL \) load shedding variables should be zero after completion, and if they are non-zero then throw an error as the model is not properly calibrated.

  2. Time-travel loop: Move to the next milestone year (\( MSY \)).

    1. Decommission assets that reached their end of life. This establishes the existing asset fleet for the current \( MSY \) by accounting for initial retirements. \( ExistingCapacity \) is the set of existing assets and their capacities available after EOL decommissioning.

    2. Determine SVD demand profiles and run investment appraisal tools for them. SVD demand profiles for the \( MSY \) are determined from user input data. The investment appraisal tools shown in part 2 are applied to determine portfolios of existing and new assets to meet demand for each SVD commodity. Prices of input commodities are known from the final dispatch of the previous milestone year. This finalises \( NewCapacity \) and \( AssetChosen \) for each SVD.

    3. Build System Layer-by-Layer loop: Completes the investment pass for the milestone year, progressively adding commodities layer by layer. This step determines new asset capacities (\( NewCapacity \)) and selects existing assets that remain competitive (\( AssetChosen \)). Other existing assets are decommissioned if they are not utilised for \( MothballYears \) years (a user-input asset parameter). This inner loop continues until no different SED commodities (that have not been processed using the investment appraisal tools) are added as commodities of interest for this iteration.

      1. Determine the commodities of interest for investment. In the base year this is all service demand (SVD) commodities. After the base year, the commodities of interest are determined dynamically; they are the set of commodities that are consumed by the assets invested/chosen in the last iteration (layer) of this loop.

      2. Dispatch to determine commodity of interest demand profile. Dispatch is executed using the formulation shown in Dispatch Optimisation Formulation, but only including system elements downstream of the commodities of interest. Commodity prices for upstream/unknown inputs/outputs from assets serving the commodities of interest and assets downstream of the commodities of interest with unknown commodity prices (if any) are assumed to take on commodity prices from the previous MSY. Care must be taken to avoid any double-counting of prices and e.g. commodity levies. Demand profiles for commodities of interest are recorded (\( D[c,r,t] \)).

      3. Run investment appraisal tools for each commodity of interest. The investment appraisal tools shown in part 2 are applied to determine portfolios of existing and new assets to meet demand for each commodity of interest. It is necessary to consider the complete demand profile of each commodity of interest, as even where demand can be served with existing assets in the MSY without new investment, economic decommissioning is still possible.

      4. Finalise new capacity and retained assets that produce the commodities of interest. \( NewCapacity \) and \( AssetChosen \) are finalised for the layer.

      5. Check if there are SED commodities that the investment appraisal tools have not been run for. If yes, move to next layer of the layering loop at step 2(c). If no, layering loop ends, break and continue at step 2(d).

    4. Ironing-out loop, with iteration limit \( k_{max} \). For each \( k \):

      1. Execute dispatch as per Dispatch Optimisation Formulation with the complete system, with all candidate assets for the \( MSY_{next} \) included with capacities set to zero to generate prices and reduced costs for the \( MSY_{next} \).

      2. Check if load-weighted average prices for any SED commodity has changed (or changed more than a tolerance) since the last loop (also, possibly check if the 95th percentile of price has changed more than a tolerance). If yes, continue at 2(d)iii. If no, this MSY is complete, and if further MSY exist continue time-travel loop from step 2, or if no further MSY exist then go to step 3.

      3. If \( k = k_{max} \) break with a warning telling the user that this loop did not converge, identifying out-of-balance commodities. If further MSY exist continue time-travel loop from step 2, or if no further MSY exist then go to step 3.

      4. Re-run investment appraisal tools for the assets and commodities that are contributing to the price instability.

  3. Outer loop ends when no further milestone years exist.