| s |
powerPrice |
3 |
years (31) |
|
€/MWh |
|
interpolating the price of electricitry between 2020 and 2050 for industry, commercial, and household |
\(p^{el}_{t,i}\) |
MATLAB\setCountryData.m |
|
| s |
heatPrice |
1 |
31 |
|
€/MWh |
|
interpolating the price of heat between 2020 and 2050 |
\(p^{th}_t\) |
MATLAB\setCountryData.m |
|
| f |
cropLandUseInit |
1 |
numCrop(10) |
|
ha |
|
Arable land use in 2020 (2017 actually). It can be defined in scenarios. |
\(\Lambda_{2020,f}\) |
MATLAB\setCountryData.m |
|
| f |
resPotImport |
numCrops + numResidue(21) |
years (31) |
|
PJ_feed |
|
Maximum imported crops and residues through time. The maximum is set to the curent level through time |
\(\phi^{imp}_{t,r}\) |
MATLAB\setCountryData.m |
|
| g |
resImportMax |
1 |
years (31) |
|
PJ |
0 |
The current value is set to zero, which is determined by scenarios. |
\(\phi^{imp}_{t}\) |
MATLAB\setCountryData.m |
|
| g |
landMax |
1 |
31 |
|
ha |
|
Maximum land used for each year |
\(\Lambda_t\) |
MATLAB\setData.m |
|
| g |
H2MaxShipping |
1 |
31 |
|
% |
0..0.1 |
H2 Share of market for shipping 0 .. 0.1 (10%) |
\(\dfrac{\delta^{H2}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
H2maxGoods |
1 |
31 |
|
% |
0..0.3 |
H2 Share of market for goods 0 .. 0.3 |
\(\dfrac{\delta^{H2}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
H2maxAviation |
1 |
31 |
|
% |
0..0.1 |
H2 market share for aviation |
\(\dfrac{\delta^{H2}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
CH4maxLand |
1 |
31 |
|
% |
0 |
CH4 max land - for passenger transportation |
\(\dfrac{\delta^{CH4}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
CH4maxShipping |
1 |
31 |
|
% |
0 |
CH4 max for for marine sector |
\(\dfrac{\delta^{CH4}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
CH4maxGoods |
1 |
31 |
|
% |
0..0.3 |
CH4 max ratio for Freight transportation |
\(\dfrac{\delta^{CH4}_{t,s}}{\delta_{t,s}}\) |
MATLAB\setData.m |
|
| g |
LCH4maxLand |
1 |
31 |
|
% |
0 |
Liquified methane (for road transport) |
\(\dfrac{\delta^{LCH4}_{t,s}}{\delta{t,s}}\) |
MATLAB\setData.m |
|
| g |
LCH4maxShipping |
1 |
31 |
|
% |
0..0.3 |
Liquified methane |
\(\dfrac{\delta^{LCH4}_{t,s}}{\delta{t,s}}\) |
MATLAB\setData.m |
|
| g |
LCH4maxGoods |
1 |
31 |
|
% |
0..0.3 |
Liquified methane |
\(\dfrac{\delta^{LCH4}_{t,s}}{\delta{t,s}}\) |
MATLAB\setData.m |
|
| s |
co2source |
1 |
31 |
|
MtCO2 |
60 |
CO2 limitations |
\(\hat{\dot{m}}^{CO2}_t\) |
MATLAB\setData.m |
|
| s |
co2price |
1 |
31 |
|
€/tCO2 = Mil €/MtCO2 |
50 |
price of CO2 |
\(p_t^{CO2}\) |
MATLAB\setData.m |
|
| s |
ghgRefCO2 |
|
|
|
MtCO2/ktCO2 |
0 |
GHG Reference for CO2 - It is either 1000 or 0. We set it to zero, which means we are going to use CO2 from renewables for PtX as input |
\(\varepsilon_{in}^{CO2}\) |
MATLAB\setData.m |
|
| s |
powerMixMax |
|
|
|
PJ |
277 |
Maximal usage of power mix (15%) for EVs or hydrogen (if power mix is checked in the input file for these usages) |
\(\hat{E}^{mix}_{el}\) |
MATLAB\setData.m |
|
| g |
Demand |
numSectors (11) |
years (31) |
|
PJ |
|
It linearly change the energy demand from g.demandStart to g.demandEnd. It is available in techData.data.countryData |
\(\delta_{t,s}\) |
MATLAB\setData.m |
|
| s |
convEtaBiomSpec |
years (31) |
numCrops + numResidues (24) |
numTech (40) |
% |
|
The efficiency of each technology to convert the crop and residues into energy (H2 or electricity) from 2020..2050 |
\(\eta_{t,f,i}\) |
MATLAB\setData.m |
|
| s |
plantConvEta |
years (31) |
numTech (40) |
|
% |
|
efficiency of the technology from 2020 to 2050 |
\(\eta_{t,i}\) |
MATLAB\setData.m |
|
| s |
plantCapacityFactor |
years (31) |
numTech (40) |
|
% |
|
It is dividing plantFullLoadHours by 8760 = capacity factor |
\(CF_{t,i}\) |
MATLAB\setData.m |
|
| f |
resPot |
numResidue(12) |
years (31) |
|
PJ_feed |
|
Interpolates between f.resPotInit and f.resPotEnd(i) |
\(\phi_{t,r}\) |
MATLAB\setData.m |
|
| s |
heatInput |
1 |
numTech (40) |
|
kWh/GJ |
|
The required heat |
\(\dot{m}^{th}_i\) |
MATLAB\setData.m |
|
| s |
powerInput |
1 |
numTech (40) |
|
kWh/GJ |
|
The required power for each technology - Why don't we see any inputs (heat or power) for PtG? -> there iis no heat and power required as utilities. power is only used as feedstock |
\(\dot{m}^{el}_i\) |
MATLAB\setData.m |
|
| s |
feed2ndCO2Amount |
1 |
numTech (40) |
|
t/GJ = Mt/PJ |
|
|
\(\dot{m}^{CO2}_i\) |
MATLAB\setData.m |
|
| s |
heatByprod |
years (31) |
numTech (40) |
|
GJ/GJ = PJ/PJ |
|
Interpolation from Init to High |
\(\dfrac{\eta_{t,i}^{th}}{\eta_{t,i}}\) |
MATLAB\setData.m |
|
| s |
feed2ndH2in |
years (31) |
numTech (40) |
|
kWh/GJ or GJ/GJ |
|
Interpolation from High to low |
\(\dot{m}^{H2}_{t,i}\) |
MATLAB\setData.m |
|
| s |
ghgEFPower |
1 |
years (31) |
|
kgCO2eq/kWh |
|
The initial values were interpolated based on values for (2017,2018,2030,2050); however, the corresponidg years in the excel file are 2020; 2030; 2040; and 2050. I changed for better consistency. |
\(\varepsilon_{t}^{elMix}\) |
MATLAB\setData.m |
|
| s |
relativeFuelEconomy |
numTech (40) |
numSectors (11) |
|
GJ_out/GJ_in - it is calculated based on GJ/vkm or GJ/tkm |
|
The fuel economy (comparing the TTW) of all sectors compare to petrol/otto process. The tank-to-wheel (TTW) efficiency is defined as the ratio between energy output from the wheels and the energy content of the fuel in the tank. In here 1 means they will have normal performance. But for some sectors such as passenger transportation, some fuel can bring more energy like electricity or biodiesel from HVO and FAME |
\(w_{is}\) |
MATLAB\setData.m |
|
| s |
MJperKMavgICEV |
1 |
years (31) |
|
MJ/vkm = PJ/Bvkm |
|
MJ per vehicle km considering future efficieny. from 1.9947 to 1.1968 MJ/km |
\(\tau_{t}\) |
MATLAB\setData.m |
|
| s |
passengerVehicleKMtot |
1 |
years (31) |
|
vkm |
|
Vehicle-km needed to fulfill the demand BPkm |
\(\epsilon_t^{v-km}\) |
MATLAB\setData.m |
|
| s |
newVehicSharePass |
1 |
years (31) |
|
% |
7% |
share of new ICEV vehicles in each year |
\(S^{+v}_{t}\) |
MATLAB\setData.m |
|
| s |
cap0 |
years (31) |
numTech (40) |
|
TW |
|
Trimming the matrix for just 31 years; therefore, at the end of 30 years we have still some functioning technologies ->here the starting capacity linearly decomissions until its end of life. The current capacities are as a result of yearly capacity additions since 2000s. We assume that those yearly additions since then reach their end of life and decomission based on (capacity/lifetime). |
\(k^{0}_{t,i}\) |
MATLAB\setData.m |
|
| - |
surplusPowerVar |
timeslices(50) |
years (31) |
|
TWh |
|
This variable cuts the sorted -demand on each timeslice. It is cumulative load in the each time slices (sum of 175 hours with maximum demand). Focuses on negative load only (i.e., excess power). |
\(\hat{E}_{j,t}\) |
MATLAB\surplusPower.m |
|
| - |
posResLoadVar |
timeslices(50) |
years (31) |
|
TWh |
|
It focuses on positive load and not excess power - demand for dispachable power |
\(\epsilon^{el}_{j,t}\) |
MATLAB\surplusPower.m |
|
| f |
cropPriceGJ |
years (31) |
numCrop(10) |
numCat(3) |
€/GJ |
|
Three categories have similar values. They are redundent; however, to prevent GAMS from throwing errors, it is defined in this way. |
\(p_{t,f=Cr,c}\) |
MATLAB\feedCost.m |
|
| f |
resPrice |
years (31) |
numResidue(12) |
numCat(3) |
€/GJ = Mil €/PJ |
|
Minimum, Average and Maximum prices for each residue type |
\(p_{t,f=Re,c}\) |
MATLAB\feedCost.m |
|
| f |
powerPrice |
years (31) |
numType(2) |
numCat(3) |
€/GJ = Mil €/PJ |
|
1 - power mix (only industry) 2- excess electricity which is 30% cheaper |
\(p_{t,f=el,c}\) |
MATLAB\feedCost.m |
|
| f |
landReqGJFuel |
years (31) |
numCrop(10) |
|
ha/GJ |
|
Land demand / biomass |
\(Y_{t,f}\) |
MATLAB\feedCost.m |
|
| f |
ghgCultivationTotGJfeed |
years (31) |
numCrop(10) |
|
kgCO2eq/GJ = KtCO2eq/PJ |
|
ghg emissions per tFM (kgCO2eq/tFM) / crop FM energy Content (GJ/tFM) |
\(\varepsilon_{t,f}\) |
MATLAB\ghgEmissions.m |
|
| f |
ghgTransport1GJfeed |
years (31) |
numCrop(10) |
|
kgCO2eq/GJ = KtCO2eq/PJ |
|
The unit is ghgTransport1 (kgCO2eq/tFM) / crop FM Energy contents (GJ/tFM) |
\(\varepsilon_t^{trans}\) |
MATLAB\ghgEmissions.m |
|
| s |
fuelGHGemission |
years (31) |
numTech (40) |
|
kgCO2eq/GJ = KtCO2eq/PJ |
|
equal to Cultivation emission (0) + Transport1 (0)+ P1 emissions+ P2 emissions - P2 byprod emissions + Transport 2 emissions. In here, the unit for Transport2 is not correct. |
\(\varepsilon_{t,i}\) |
MATLAB\ghgEmissions.m |
|
| s |
plantInvCostLevel |
years (31) |
numTech (40) |
|
Mil €/GW |
|
Plant cost per year if we distribute it to its life span |
\(I^{+}_{t,i}\) |
MATLAB\costDevNotLearning.m |
|
| s |
fuelMargCost |
years (31) |
numTech (40) |
|
€/GJ = Mil €/PJ |
|
Infrastructure cost (storage and transport) + O&M cost + labour + feedCost2nd - byproducts |
\(mc_{t,i}\) |
MATLAB\costDevNotLearning.m |
|