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Commit ce0ca7f8 authored by Markus Millinger's avatar Markus Millinger
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Minor corrections

parent 5cb69828
......@@ -80,7 +80,7 @@
%% \address{Address\fnref{label3}}
%% \fntext[label3]{}
\title{A model for cost- and greenhouse gas optimal material and energy deployment}
\title{A model for cost- and greenhouse gas optimal material and energy allocation}
\author[ufz]{M.~Millinger\corref{cor1}}
\ead{markus.millinger@ufz.de}
......@@ -223,7 +223,7 @@ The model includes modules for crop price developments (based on the premise tha
BENOPT contains sectors for transport (road passenger, road goods, shipping and aviation), power and heat (industry, household and commercial). The model functions on a yearly resolution (with the exception of the power sector, which can be broken down to an hourly resolution) and is not spatially explicit. Detailed input-output, capex and opex data are integrated for feedstocks, conversion and supply, which allows detailed cost analyses and combined with relevant emission factors also GHG analyses.
The model process is as follows (Figure \ref{fig:pathways}). Data setting is mainly performed in the Excel-sheet, for the conversion technologies and feedstocks. These as well as the VRE data for the base years are imported and converted to mat-files. The data is attributed to the specific variables, as well as additional data set. With these data, GHG emissions are calculated for the feedstocks and processes. Scenarios are set by setting chosen scenario specific variables. The future VRE development is calculated and based on this the ERE data are aggregated. Feedstock prices are calculated, as well as opex and capex costs of the processes. The data ensemble required for GAMS is set in the correct format and sent to GAMS, where developments are optimized. The results can then be plotted in the chosen format. The process chain can also be parallelized in a Monte-Carlo sensitivity analysis, where the complete process is repeated a set number of times with variations in chosen variables.
The model process is as follows (Figure \ref{fig:pathways}). Data setting is mainly performed in the Excel-sheet, for the conversion technologies and feedstocks. These as well as the VRE data for the base years are imported and converted to mat-files. The data is attributed to the specific variables, as well as additional data set. With these data, GHG emissions are calculated for the feedstocks and processes. Scenarios are set by setting chosen scenario specific variables. The future VRE development is calculated and based on this the ERE data are aggregated. Biomass crop and residue prices are calculated, as well as opex and capex costs of the processes. The data ensemble required for GAMS is set in the correct format and sent to GAMS, where developments are optimized. The results can then be plotted in the chosen format. The process chain can also be parallelized in a Monte-Carlo sensitivity analysis, where the complete process is repeated a set number of times with variations in chosen variables.
\begin{figure*}[!htb]
\includegraphics[trim=20 730 285 45,clip,width=1.0\textwidth]{fig/processBENOPT.pdf}
......@@ -267,7 +267,7 @@ The key model functions are presented here, with a mathematical formulation of t
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.
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 are determined endogenously, based on overall optimal system GHG emissions and costs. In this example, 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}
......@@ -276,16 +276,16 @@ This results in a pareto assessment of cost-optimal fuel deployment at different
\begin{figure*}[h!]
\centering
\includegraphics[width=1\textwidth]{fig/productionFuelPareto2.eps}
\caption{\footnotesize{Cost-optimal transport energy carrier deployment at different GHG-abatement budget targets.}}
\caption{\footnotesize{Cost-optimal transport energy carrier deployment at different GHG-abatement budget targets. Electric=electric vehicles, PtL=Power-to-Liquid, CH4=methane (from different conversion pathways), LCH4=liquefied methane, PBtL\_FT=Power-to-Biomass-to-Liquid (Fischer-Tropsch), FT=Fischer-Tropsch-diesel, LignoMeOH=lignocellulose based methanol, LignoEtOH=lignocellulose based ethanol, HVO=Hydrogenated vegetable oils, FAME=Fatty-acid methyl esters, StarchEtOH=starch crop based ethanol, BeetEtOH=sugar beet based ethanol.}}
\label{fig:prodPareto}
\end{figure*}
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}). Depending on which resources are used at different time-points and whether there are over-capacities compared to the produced amounts, the costs of technology options may differ over time, as can be seen for HVO in the figure. The usage of electrofuels expands the possible GHG abatement in 2050 compared to 2030, while biofuels remain largely with the same GHG abatemenr but with different end products.
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}). Depending on which resources are used at different time-points and whether there are over-capacities compared to the produced amounts, the costs of technology options may differ over time, as can be seen for for instance HVO in the figure. The usage of electrofuels expands the possible GHG abatement in 2050 compared to 2030, while biofuels remain largely with the same GHG abatemenr but with different end products.
\begin{figure*}[h!]
\centering
\includegraphics[width=\textwidth]{fig/meritOrderGHG_S1_2050.eps}
\caption{\footnotesize{Merit order of fuel options in two years.}}
\caption{\footnotesize{Merit order of fuel options in two years. HVO=Hydrogenated vegetable oils, BME=biomethane, LignoMeOH=lignocellulose based methanol, FCEV=fuel cell electric vehicles, BeetEtOH=sugar beet based ethanol, BtL=Biomass-to-Liquid, FAME=Fatty-acid methyl esters, PtL=Power-to-Liquid, SNG=Substitute natural gas.}}
\label{fig:meritOrderGHG_S1}
\end{figure*}
......
......@@ -55,7 +55,7 @@ elseif contains(type,'set')
gamsVarOut.val = inputVar;
end
if isequal(size(y),[1 1]) %if y is not a vector, no uels
if isequal(size(y),[1 1]) %if y is not a vector, make uels one dimensional
gamsVarOut.uels = {x};
elseif isequal(size(z),[1 1]) %if z is not a vector, make uels two dimensional
gamsVarOut.uels = {x, y};
......
......@@ -28,13 +28,13 @@ indexCountry = find(contains(s.countryNames,{countryID}));
s.discountRateInvest = techData.data.countryData(1,indexCountry);
s.labourCostStart = techData.data.countryData(2,indexCountry); %/h
s.powerLoad = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_load_actual_entsoe_power_statistics']))};
s.PVcap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_solar_cap']))};
s.PVgen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_solar_gen']))};
s.WindOnCap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_onshore_cap']))};
s.WindOnGen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_onshore_gen']))};
s.WindOffCap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_offshore_cap']))};
s.WindOffGen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_offshore_gen']))};
s.powerLoad = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_load_actual_entsoe_power_statistics']))};
s.PVcap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_solar_cap']))};
s.PVgen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_solar_gen']))};
s.WindOnCap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_onshore_cap']))};
s.WindOnGen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_onshore_gen']))};
s.WindOffCap = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_offshore_cap']))};
s.WindOffGen = powerDataCountry{:,contains(powerDataCountry.Properties.VariableNames,join([countryID,'_wind_offshore_gen']))};
s.windOnShoreInit = techData.data.countryData(11,indexCountry);
s.windOnShoreEnd = techData.data.countryData(12,indexCountry);
......
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2016-2020 Philip Tafarte, Markus Millinger
% Copyright (C) 2016-2020 Philip Tafarte
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
......
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