\address[ufz]{Department of Bioenergy, Helmholtz Centre for Environmental Research - UFZ, Permoserstra{\ss}e 15, 04318 Leipzig, Germany}

\address[ufzoekon]{Department of Economics, Helmholtz Centre for Environmental Research - UFZ, Permoserstra{\ss}e 15, 04318 Leipzig, Germany}

\address[multiplee]{Research Group MultiplEE, Faculty of Economics and Management Science, Institute for Infrastructure and Resources Management, University of Leipzig, Ritterstra{\ss}e 12, 04109 Leipzig, Germany}

@@ -236,17 +238,17 @@ The key model functions are presented here, with a mathematical formulation of t

\textit{Process data, opex and capex costs}. The process data includes CAPEX data, infrastructure, operation and maintenance cost, personnel cost and inputs and outputs, including by-products and secondary feedstocks. The input and output data enables a detailed calculation of the costs and is elaborated in \citet{Millinger.2017b}. Within the excel-file, process and feedstock data can be adapted, and allowable feedstock-technology and technology-market combinations set (through which also technologies can be completely excluded from the modelling).

\textit{GHG function}. The agricultural and conversion process input and output data combined with emission factors enable the calculation of detailed pathway GHG emissions including the allocation of emissions to the by-products, as well as the sensitivity analysis thereof \cite{Millinger.2018b}.

\textit{GHG emissions}. The agricultural and conversion process input and output data combined with emission factors enable the calculation of detailed pathway GHG emissions including the allocation of emissions to the by-products, as well as the sensitivity analysis thereof \cite{Millinger.2018b}.

\textit{Feedstock cost module}. Feedstock price developments are calculated by adding the per hectare profit of a benchmark crop (wheat) to the per hectare production cost of each energy crop \cite{Millinger.2018d}. The future price developments are calculated based on a set yearly development of the wheat price. The price of biomass residues is likewise tied to the set development, while the power price is set according to assumptions based on e.g. literature.

\textit{Feedstock price module}. Feedstock price developments are calculated by adding the per hectare profit of a benchmark crop (wheat) to the per hectare production cost of each energy crop \cite{Millinger.2018d}. The future price developments are calculated based on a set yearly development of the wheat price. The price of biomass residues is likewise tied to the set development, while the power price is set according to assumptions based on e.g. literature.

\textit{VRE and excess electricity modules}. Variable renewable electricity generation and power load in the baseline year is scaled according to the scenario specific future wind and solar PV capacity expansion and electricity demand development, resulting in VRE share and excess electricity developments. Electricity storage is included and other (fossil or renewable) must-run generation can be added \cite{Tafarte.2019}. The hourly data can be subsequently aggregated depending on the task, by sorting the data to residual load duration curves (RLDC) and dividing the data into a set number of slices (50 in the standard version), which reduces the computational burden substantially \cite{Millinger.2020}

\textit{Optimization module}. Input data for the optimization is formatted to suit the GAMS data format and transferred to the optimization module in GAMS. The data is transferred back to Matlab for data handling and plotting. Investment and dispatch/deployment is optimized, with the fuel demand in the transport sector endogenously determined through vehicle fleet adaptations. In the standard setting the total GHG abatement is first maximised and then a value less than the maximum is set as a target for cost-minimization. The target sets a GHG abatement budget which needs to be met in sum over the whole analysed time span, but no targets for individual years are set.

\textit{Sensitivity analysis module}. Monte Carlo sensitivity analysis \cite{Millinger.2018d,Millinger.2018b} can be performed with parallel computing, enabling faster runs. Any parameter can be added for variation and plotting related to individual parameters can be performed. The analysis can be done for both singular functions as well as for the whole model process chain (Fig.\ref{fig:monteCarloScatter}).

\textit{Sensitivity analysis module}. Monte Carlo sensitivity analysis \cite{Millinger.2018d,Millinger.2018b} can be performed with parallel computing, enabling faster runs. Any parameter can be added for variation and plotting related to individual parameters can be performed. The analysis can be done for both singular functions as well as for the whole model process chain (Figure\ref{fig:monteCarloScatter}).

\textit{Plotting}. Extensive plotting can be performed and more added, with examples shown in Figures 1-3.

\textit{Plotting}. Extensive plotting can be performed and more added, with examples shown in Figures 1-3 and more can be found in the body of literature.

@@ -257,7 +259,7 @@ The key model functions are presented here, with a mathematical formulation of t

%\textbf{Provide at least one illustrative example to demonstrate the major functions.}

Given the set data and dependencies for each process input in the excel file, the model calculates costs, GHG emissions and optimal deployment of the given options across the given sectors. In the standard set-up, the GHG-abatement over the whole time-span is first maximized without cost restrictions. Then, the GHG-abatement is set as a target that has to be achieved while minimizing costs. Any level below this target can be set, also in a step-wise approach for a pareto analysis (Fig.\ref{fig:prodPareto}).

Given the set data and dependencies for each process input in the excel file, the model calculates costs, GHG emissions and optimal deployment of the given options across the given sectors. In the standard set-up, the GHG-abatement over the whole time-span is first maximized without cost restrictions. Then, the GHG-abatement is set as a target that has to be achieved while minimizing costs. Any level below this target can be set, also in a step-wise approach for a pareto analysis (Figure\ref{fig:prodPareto}).

\begin{figure*}[h!]

\centering

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@@ -268,7 +270,7 @@ Given the set data and dependencies for each process input in the excel file, th

As can be seen in the example for the transport sector, the model chooses at what time-point changes between runs at different targets occur. For instance, at the maximal GHG-target, some capacities of BeetEtOH (sugar beet-based bioethanol) are only used for a few years. At a slightly lower target, these overcapacities do not emerge. Also, PBtL (Power-to-Biomass-to-Liquid) is less deployed, in order to fully disappear at lower targets. With decreasing targets, the diversity of options decreases, and currently common options are less deployed. Electric vehicles appear across all targets and can thus be seen as the most robust option in the example.

The combination of detailed cost and GHG emission calculations as well as system competition enables a systems perspective on different options, as the resource use is optimized taking all renewable competitions into account. A merit order plot shows the resulting GHG abatement costs and potentials of different options given feedstock and demand restrictions under competition (Fig.\ref{fig:meritOrderGHG_S1})

The combination of detailed cost and GHG emission calculations as well as system competition enables a systems perspective on different options, as the resource use is optimized taking all renewable competitions into account. A merit order plot shows the resulting GHG abatement costs and potentials of different options given feedstock and demand restrictions under competition (Figure\ref{fig:meritOrderGHG_S1})

\begin{figure*}[h!]

\centering

...

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@@ -279,7 +281,7 @@ The combination of detailed cost and GHG emission calculations as well as system

%[show choice of intra-year steps vs. diff to total residual load and run-time?]

A global Monte Carlo sensitivity analysis can be performed, which provides a solid basis for analysing the effect of different parameter values on the results, and thus on the robustness of results (Fig.\ref{fig:monteCarloScatter}). Both biomass usage and PtX are coupled with large uncertainties across the pathways, which are important to assess in order to get a thorough understanding of the analysed systems.

A global Monte Carlo sensitivity analysis can be performed, which provides a solid basis for analysing the effect of different parameter values on the results, and thus on the robustness of results (Figure\ref{fig:monteCarloScatter}). Both biomass usage and PtX are coupled with large uncertainties across the pathways, which are important to assess in order to get a thorough understanding of the analysed systems.