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Commit 7ca8a6f3 authored by Markus Millinger's avatar Markus Millinger
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Moved data from different sheets to countryData to slim the Excel file, and... 

Moved data from different sheets to countryData to slim the Excel file, and adapted the data reading
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data/techData.mat
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src/main.m
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......@@ -54,24 +54,42 @@ for scenario = 1:noScenarios
clear s;
%Set some dimensions
s.numSectors = 11;
f.numResidue = 12;
s.weatherYear = {'2018'};
s.numTech = size(techData.data.techInputData(1,:),2);
s.numSectors = 11;
f.numResidue = 12;
f.numCrop = size(techData.data.feedstockInputData(1,1:end),2);
%Some general variables
s.inflation = 0;
s.plantPayBackTime = 20;
s.dieselCostStart = 0.9; %/l
s.referenceGHGemission = 94.1; %kgCO2/GJ
s.priceDevFactor = 0.04; % frac/a
f.wheatPriceStart = 189;% %/tFM
f.rapeSeedPriceStart = 381;% %/tFM
s.runTime = 2051-2020;
%How many time steps within each year - up to 8760 possible (heavy on run-time)
g.timeStepsIntraYear = 1:50;
%Set country specific data
countryID = 'SE';
countryID = 'DE';
s.weatherYear = {'2018'};
% powerDataCountry = ...
% powerData(contains(powerData.cet_cest_timestamp,s.weatherYear),...
% contains(powerData.Properties.VariableNames,countryID));
% save(['../data/powerData' countryID '.mat'],'powerDataCountry'); %Save to .mat-file - comment out if the .mat-file is up to date
powerDataCountry = ...
powerDataCountry = ...
importdata(['../data/powerData' countryID '.mat']); %reads data from .mat file
[f,g,s] = ...
setCountryData(techData,countryID,powerDataCountry,s,f); %Set country specific data
[f,g,s] = ...
setCountryData(techData,countryID,powerDataCountry,s,f,g); %Set country specific data
%Set variables
[f,g,s] = setData(techData,f,g,s);
[f,g,s] = setData(techData,f,g,s);
%% Monte Carlo sensitivity analysis example of VRE module
% monteCarlo(1000,s,f,g,techData);
......
src/setCountryData.m
View file @ 7ca8a6f3
......@@ -21,7 +21,7 @@
% Function for setting the country specific data
%
function [f,g,s] = setCountryData(techData,countryID,powerDataCountry,s,f)
function [f,g,s] = setCountryData(techData,countryID,powerDataCountry,s,f,g)
s.countryNames = techData.textdata.countryData(1,3:end);
indexCountry = find(contains(s.countryNames,{countryID}));
......@@ -62,14 +62,30 @@ s.EVstart = techData.data.countryData(45,indexCountry).*10^6
g.demandStart = techData.data.countryData(51:51+s.numSectors-1,indexCountry);
g.demandEnd = techData.data.countryData(71:71+s.numSectors-1,indexCountry);
%Power price & heat price for required input
for i=1:3
s.powerPrice(i,:) = linspace(techData.data.countryData(99+i,indexCountry),techData.data.countryData(106+i,indexCountry),s.runTime).*10^3.*10^-2'; %[ct/kWh -> /MWh]
end
s.heatPrice = linspace(techData.data.countryData(103,indexCountry),techData.data.countryData(109,indexCountry),s.runTime).*10^3.*10^-2; %[ct/kWh -> /MWh]
s.plantCap = techData.data.techInputData(198+indexCountry,1:end);%*10^6;
f.cropYieldFMlow = techData.data.feedstockInputData(40+indexCountry,1:end); %GJ/tFM
f.cropYieldFMhigh = techData.data.feedstockInputData(70+indexCountry,1:end); %GJ/tFM
f.cropLandUseInit = techData.data.feedstockInputData(100+indexCountry,1:end); % [ha]
f.resPot = techData.data.biomassResPot(1:f.numResidue,indexCountry);
s.ghgEFPowerData = techData.data.powerEmissions(1+indexCountry,[2017 2018 2030 2050]-2016); %kgCO2eq/kWh interp1([2015 2020 2025 2030 2035 2040 2045 2050],co2sourceData,2020:1:2050,'linear');
% Residue potential data [PJ_feed]
f.resPot = techData.data.countryData(120:120+f.numResidue-1,indexCountry).*ones(1,s.runTime); %PJ_feed
f.resPotImport = ...
techData.data.countryData(140:140+f.numResidue+f.numCrop-1,indexCountry).*ones(1,s.runTime);
% Residue price start [/GJ]
f.resPriceIniMin = techData.data.countryData(170:170+f.numResidue-1,indexCountry);
f.resPriceIniMax = techData.data.countryData(190:190+f.numResidue-1,indexCountry);
g.resImportMax = techData.data.countryData(202,indexCountry).*ones(1,s.runTime);
s.ghgEFPowerData = techData.data.countryData(210:213,indexCountry)'; %kgCO2eq/kWh
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']))};
......
src/setData.m
View file @ 7ca8a6f3
......@@ -24,7 +24,6 @@
function [f,g,s] = setData(techData,f,g,s)
%Technology variables
s.numTech = size(techData.data.techInputData(1,:),2);
s.techNames = techData.textdata.techInputData(2,3:end);
s.techNamesLegend = s.techNames;
......@@ -36,8 +35,7 @@ s.sectorNamesLegend = s.sectorNames;
s.fuelNames = techData.textdata.techInputData(136:142,2)';
%Feedstock variables
f.residueNames = techData.textdata.biomassResPot(2:f.numResidue+1,1)';
f.numCrop = size(techData.data.feedstockInputData(1,1:end),2);
f.residueNames = techData.textdata.countryData(121:121+f.numResidue-1,1)';
f.cropNames = techData.textdata.feedstockInputData(1,3:f.numCrop+2);
f.cat = 3; %For dividing e.g. biomass types into 3 equal shares with different prices
f.numPowerSource = 2;
......@@ -46,24 +44,12 @@ f.feedNames = [f.cropNames f.residueNames f.powerNames];
%% Definition of Input Data s= Technology specific f=feedstock specific
%General variables
s.inflation = 0;
s.plantPayBackTime = 20;
s.dieselCostStart = 0.9; %/l
s.referenceGHGemission = 94.1; %kgCO2/GJ
f.wheatPriceStart = 189;% %/tFM
f.rapeSeedPriceStart = 381;% %/tFM
s.runTime = 2051-2020;
f.runTime = s.runTime;
s.year = linspace(2020,2050,s.runTime);
s.hourYear = linspace(1,8760,8760);
s.dayYear = linspace(1,365,365);
%% Set parameters for power sector
%How many time steps within each year - up to 8760 possible (heavy on run-time)
g.timeStepsIntraYear = 1:50;
%Onshore wind capacity [GW] from start year to end year in 5-year steps
s.onShore = round(linspace(s.windOnShoreInit,s.windOnShoreEnd,7));
......@@ -99,7 +85,6 @@ for i=1:7
end
%Set parameters for crops
s.priceDevFactor = 0.04; % frac/a
g.landMax = 10^6.*linspace(g.landUseInit,g.landUseEnd,s.runTime); %ha
%Set restrictions for fuel types in sectors
......@@ -133,13 +118,13 @@ for j=1:s.numSectors
interp1([2020 2050],[g.demandStart(j) g.demandEnd(j)],2020:1:2050,'linear');
end
%% System data
%Power price & heat price for required input
s.powerPrice = 10^3.*10^-2.*techData.data.demandData(10:12,1:s.runTime);%[/MWh]
s.powerPriceDevNormed = techData.data.demandData(8,1:s.runTime);%[/MWh]
s.heatPrice = 10^3.*10^-2.*techData.data.demandData(15,1:s.runTime)';%[/MWh]
% %% System data
% %Power price & heat price for required input
% s.powerPrice = 10^3.*10^-2.*techData.data.demandData(10:12,1:s.runTime);%[/MWh]
% % s.powerPriceDevNormed = techData.data.demandData(8,1:s.runTime);%[/MWh]
% s.heatPrice = 10^3.*10^-2.*techData.data.demandData(15,1:s.runTime)';%[/MWh]
%- for process heat, see feedCost function. Possibly differentiate input and output heat price - district heating could be output heat price reference
s.GHGabateTarget = 160*10^9*linspace(0.05,0.08,s.runTime); %kgCO2eq/a
% s.GHGabateTarget = 160*10^9*linspace(0.05,0.08,s.runTime); %kgCO2eq/a
%% Definition of technology data / Life Time
% Technology Lifetime (tech) [a]
......@@ -233,19 +218,6 @@ f.cropMachineVarHa = techData.data.feedstockInputData(9,1:f.numCrop);
f.cropServiceCostsHa = techData.data.feedstockInputData(10,1:f.numCrop);
f.cropDirectCostsHa = techData.data.feedstockInputData(11,1:f.numCrop);
% Residue potential data [PJ_feed]
f.resPot = f.resPot.*ones(1,s.runTime);
f.resPotImport = ...
techData.data.biomassResPot(30:30+f.numResidue+f.numCrop-1,1:s.runTime);
% Residue price start [/GJ]
f.resPriceIniMin = techData.data.biomassResPot(16:16+f.numResidue,1);
f.resPriceIniMax = techData.data.biomassResPot(16:16+f.numResidue,2);
g.resImportMaxIn = techData.data.biomassResPot(59,[1 4 14 24 34]);
g.resImportMax = ...
interp1([2017 2020 2030 2040 2050],g.resImportMaxIn,2020:1:2050,'linear');
%% Definition of technology data
......
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