@@ -244,7 +244,7 @@ The key model functions are presented here, with a mathematical formulation of t

\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 GHGabatement 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{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 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. 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 (Figure \ref{fig:monteCarloScatter}).

...

...

@@ -259,7 +259,15 @@ 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 (Figure \ref{fig:prodPareto}).

%\textbf{Background+Research question}

We show the main functions of the model through an application for the transport sector. As all sectors, transport needs to be increasingly covered by renewable options in order to achieve climate targets \cite{IPCC.2018}. The available transport options and sectors show very different characteristics in terms of technology availability and resource use \cite{Millinger.2020}. Therefore, A holistic assessment of resources, conversion options and end-use technologies is required in order to capture the system from source to end-service, and to assess the competition for resources.

%\textbf{Materials and methods}

Technology data, sector service demands and process dependencies are input in the excel file, based on which the model calculates costs, GHG emissions and optimal deployment of the given options across the given sectors. The other sectors (power, heat) are turned off for the deployment analysis, but excess electricity can still be used for producing hydrogen, which can be used directly or optionally combined with a carbon source to produce hydrocarbon fuels. Biofuels produced from crops grown from a set arable land area and biomass residues are also included. The crops grown on the arable land is determined endogenously, based on system GHG emissions and costs. The GHG abatement is first maximized given the restrictions for resources and demands, as well as fuel-specific deployment restrictions. Then, the possible GHG abatement is set as a target which is step-wise reduced and fulfilled while minimizing costs.

%\textbf{Results}

This results in a pareto analysis of cost-optimal fuel deployment at different GHG-budget targets (Figure \ref{fig:prodPareto}).

\begin{figure*}[h!]

\centering

...

...

@@ -286,10 +294,12 @@ A global Monte Carlo sensitivity analysis can be performed, which provides a sol

\caption{\footnotesize{Example Monte Carlo sensitivity analysis of the VRE module [maybe change to show sensitivity of whole model?].}}

\caption{\footnotesize{Example Monte Carlo sensitivity analysis of the VRE module.}}

\label{fig:monteCarloScatter}

\end{figure*}

\textbf{Conclusions}

\section{Impact}

\label{sec:impact}

...

...

@@ -306,7 +316,7 @@ Compared to IAMs, an increased level of detail regarding VRE, sector coupling an

%\textbf{Indicate how widespread the use of the software is within and outside the intended user group.}

The model has been used for numerous analyses. Assessments of biofuels regarding costs \cite{Millinger.2017b,Millinger.2018d} and greenhouse gas emissions \cite{Millinger.2018b}, as well as optimal biofuel deployment \cite{Millinger.2019} and renewable fuel policy analysis for the BMEL \cite{Meisel.2020,Meisel.2019} have been published, as well as analyses on biochemicals \cite{Musonda.2020}. An analysis on biomass use across transport, heat and power sectors within long-term scenarios for the BMWi has been performed \cite{Thran.2019b}. Coupling with a general equilibrium agricultural model and a land use model \cite{Thran.2016,Thran.2017} have been performed, as well as an analysis of electrofuels/power-to-X \cite{Millinger.2020}.

The model has been used for numerous analyses. Assessments of biofuels regarding costs \cite{Millinger.2017b,Millinger.2018d} and greenhouse gas emissions \cite{Millinger.2018b}, as well as optimal biofuel deployment \cite{Millinger.2019} and renewable fuel policy analysis for the BMEL \cite{Meisel.2020,Meisel.2019} have been published. An analysis on biomass use across transport, heat and power sectors within long-term scenarios for the BMWi has been performed \cite{Thran.2019b}. Coupling with a general equilibrium agricultural model and a land use model \cite{Thran.2016,Thran.2017} have been performed, as well as an analysis of electrofuels/power-to-X \cite{Millinger.2020}.

\textit{Future Work.} Stand-alone versions focusing on chemical products \cite{Musonda.2020} as well as on the heat sector \cite{Jordan.2020, Jordan.2019} have been developed, with details being planned for integration into the main model. Aspects concerning sustainable agriculture, nutrition and industry are underway. Sector coupling and (renewable) power based options as well as carbon capture are increasingly included in the modelling, which enables a holistic analysis of renewable futures in the sectors considered. Increasing the temporal resolution in the heat and transport sectors to better analyse sector coupling is planned. An increased geographical scope can be undertaken, depending on available data.

...

...

@@ -364,35 +374,35 @@ This work was funded by the Helmholtz Association of German Research Centers and

%

%\end{thebibliography}

%Please add the reference to the software repository if DOI for software is available.

\section*{Current executable software version}

\label{}

Ancillary data table required for sub version of the executable software: (x.1, x.2 etc.) kindly replace examples in right column with the correct information about your executables, and leave the left column as it is.

\begin{table*}[!h]

\begin{tabular}{|l|p{6.5cm}|p{6.5cm}|}

\hline

\textbf{Nr.}&\textbf{(Executable) software metadata description}&\textbf{Please fill in this column}\\

\hline

S1 & Current software version & 2.1 \\

\hline

S2 & Permanent link to executables of this version &$https://git.ufz.de/millinge/benopt$\\

\hline

S3 & Legal Software License & GNU-GPL 3.0 \\

\hline

S4 & Computing platforms/Operating Systems & BSD, Linux, OS X, Microsoft Windows, Unix-like \\

S6 & If available, link to user manual - if formally published include a reference to the publication in the reference list & - \\

\hline

S7 & Support email for questions & Markus Millinger \href{mailto:markus.millinger@ufz.de}{markus.millinger@ufz.de}\\

\hline

\end{tabular}

\caption{Software metadata (optional)}

\label{}

\end{table*}

%

%\section*{Current executable software version}

%\label{}

%

%Ancillary data table required for sub version of the executable software: (x.1, x.2 etc.) kindly replace examples in right column with the correct information about your executables, and leave the left column as it is.

%

%\begin{table*}[!h]

%\begin{tabular}{|l|p{6.5cm}|p{6.5cm}|}

%\hline

%\textbf{Nr.} & \textbf{(Executable) software metadata description} & \textbf{Please fill in this column} \\

%\hline

%S1 & Current software version & 2.1 \\

%\hline

%S2 & Permanent link to executables of this version & $https://git.ufz.de/millinge/benopt$ \\

%\hline

%S3 & Legal Software License & GNU-GPL 3.0 \\

%\hline

%S4 & Computing platforms/Operating Systems & BSD, Linux, OS X, Microsoft Windows, Unix-like \\