Energy demand - TIAM-UCL: Difference between revisions
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Demand drivers (population, GDP, family units, etc.) are obtained externally, via other models or from accepted other sources. Energy-service demands and respective drivers in the TIAM-UCL are presented in Table '''2-1'''. The demands for energy services are linked to the drivers' projections via elasticities. These elasticities of demands are intended to reflect changing patterns in energy service demands in relation to socio-economic growth, such as saturation in some energy end-use demands, increased urbanization, or changes in consumption patterns once the basic needs are satisfied. The energy-service demands for future years are projected using the following relationship: | Demand drivers (population, GDP, family units, etc.) are obtained externally, via other models or from accepted other sources. Energy-service demands and respective drivers in the TIAM-UCL are presented in Table '''2-1'''. The demands for energy services are linked to the drivers' projections via elasticities. These elasticities of demands are intended to reflect changing patterns in energy service demands in relation to socio-economic growth, such as saturation in some energy end-use demands, increased urbanization, or changes in consumption patterns once the basic needs are satisfied. The energy-service demands for future years are projected using the following relationship: | ||
[[ | [[File:35815634.png]] | ||
Where, ''k'' is a constant; it is one for most of the energy services demand. The constant ''k'' is population and number of households when the driver is GDPP and GDPPHOU respectively. | Where, ''k'' is a constant; it is one for most of the energy services demand. The constant ''k'' is population and number of households when the driver is GDPP and GDPPHOU respectively. | ||
Revision as of 14:06, 24 August 2016
| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | University College London (UCL), UK, https://www.ucl.ac.uk. |
| Solution concept | Partial equilibrium (price elastic demand) |
| Solution method | Linear optimisation |
| Anticipation | Perfect Foresight
(Stochastic and myopic runs are also possible) |
Demand drivers (population, GDP, family units, etc.) are obtained externally, via other models or from accepted other sources. Energy-service demands and respective drivers in the TIAM-UCL are presented in Table 2-1. The demands for energy services are linked to the drivers' projections via elasticities. These elasticities of demands are intended to reflect changing patterns in energy service demands in relation to socio-economic growth, such as saturation in some energy end-use demands, increased urbanization, or changes in consumption patterns once the basic needs are satisfied. The energy-service demands for future years are projected using the following relationship:
Where, k is a constant; it is one for most of the energy services demand. The constant k is population and number of households when the driver is GDPP and GDPPHOU respectively.
Table 2-1: Energy-services demand and respective drivers
Code
Description
Unit
Driver
ICH
Chemicals
PJ
PCHEM
IIS
Iron and Steel
Mt
PISNF
INF
Non-ferrous metals
Mt
PISNF
INM
Non Metals
PJ
POEI
ILP
Pulp and Paper
Mt
POEI
IOI
Other Industries
PJ
POI
I00
Other Industrial consumption
PJ
Constant
NEO
Industrial and Other Non Energy Uses
PJ
GDP
ONO
Other non-specified consumption
PJ
GDP
AGR
Agricultural demand
PJ
PAGR
CC1
Commercial Cooling - Region 1
PJ
PSER
CCK
Commercial Cooking
PJ
PSER
CH1
Commercial Space Heat - Region 1
PJ
PSER
CHW
Commercial Hot Water
PJ
PSER
CLA
Commercial Lighting
PJ
PSER
COE
Commercial Office Equipment
PJ
PSER
CRF
Commercial Refrigeration
PJ
PSER
RC1
Residential Cooling - Region 1
PJ
HOU/GDPPHOU*
RCD
Residential Clothes Drying
PJ
HOU/GDPPHOU*
RCW
Residential Clothes Washing
PJ
HOU/GDPPHOU*
RDW
Residential Dishwashing
PJ
HOU/GDPPHOU*
REA
Residential Other Electric
PJ
HOU/GDPPHOU*
RH1
Residential Space Heat - Region 1
PJ
HOU
RHW
Residential Hot Water
PJ
POP
RK1
Residential Cooking - Region 1
PJ
POP
RL1
Residential Lighting - Region 1
PJ
GDPP
RRF
Residential Refrigeration
PJ
HOU/GDPPHOU*
NEU
Non Energy Uses
PJ
GDP
TAD
Domestic Aviation
PJ
GDP
TAI
International Aviation
PJ
GDP
TRB
Road Bus Demand
Bv-km
POP
TRC
Road Commercial Trucks Demand
Bv-km
GDP
TRE
Road Three Wheels Demand
Bv-km
POP
TRH
Road Heavy Trucks Demand
Bv-km
GDP
TRL
Road Light Vehicle Demand
Bv-km
GDP
TRM
Road Medium Trucks Demand
Bv-km
GDP
TRT
Road Auto Demand
Bv-km
GDPP
TRW
Road Two Wheels Demand
Bv-km
POP
TTF
Rail-Freight
PJ
GDP
TTP
Rail-Passengers
PJ
POP
TWD
Domestic Internal Navigation
PJ
GDP
TWI
International Navigation
PJ
GDP
- Driver is GDPPHOU for AFR, CHI, CSA, EEU, FSU, IND, MEA, MEX, ODA and SKO
Driver Elasticity
Driver elasticities determine the sensitivity of changes in energy-service demand to changes in the underlying driver. An elasticity of 1 means that a change of the underlying driver is exactly reflected in the energy-service demand. Energy-service demands with an elasticity below 1 are demand inelastic, while those with an elasticity of one or higher are demand elastic. In general it is assumed that energy-service demands grow slower than the underlying driver, such as GDP, GDP per capita or number of household. This decoupling of energy demand and economic growth is expected to increase during the 21st century so that all elasticities fall. Residential space heating (RH1), for example, has an elasticity of 0.8 in 2010, which drops to 0.5 in 2100. This means that initially the energy demand for space heating increases at 80% of household number growth, the specific underlying driver, and in the 2nd half of the century at only 50% of the household number growth rate.
Table 2-2: Driver elasticities for the United Kingdom
| Energy-service demand | 2010 | 2020 | 2030 | 2040 | 2050 | 2100 |
| AGR | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.6 |
| CC1 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.4 |
| CCK | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| CH1 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.3 |
| CHW | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| CLA | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| COE | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| COT | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| CRF | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.4 |
| I00 | 0.6 | 0.6 | 0.6 | 0.6 | 0.6 | 0.5 |
| ICH | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.5 |
| IIS | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.5 |
| ILP | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.5 |
| INF | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.5 |
| INM | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.5 |
| IOI | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.6 |
| NEO | 0.6 | 0.6 | 0.6 | 0.6 | 0.6 | 0.5 |
| NEU | 1 | 1 | 1 | 1 | 0.9 | 0.5 |
| ONO | 0.6 | 0.6 | 0.6 | 0.6 | 0.6 | 0.5 |
| RCD | 1 | 1 | 1 | 1 | 1 | 0.8 |
| RCW | 1 | 1 | 1 | 1 | 1 | 0.8 |
| RDW | 1 | 1 | 1 | 1 | 1 | 0.8 |
| REA | 1 | 1 | 1 | 1 | 1 | 0.8 |
| RH1 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.5 |
| RK1 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.5 |
| RL1 | 1 | 1 | 1 | 1 | 0.9 | 0.7 |
| ROT | 1 | 1 | 1 | 1 | 1 | 0.8 |
| RRF | 1 | 1 | 1 | 1 | 1 | 0.8 |
| RHW | 1 | 1 | 1 | 1 | 1 | 0.8 |
| TAD | 1.2 | 1.2 | 1.1 | 1.1 | 0.9 | 0.1 |
| TAI | 1.2 | 1.2 | 1.1 | 1.1 | 0.9 | 0.1 |
| TRB | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.8 |
| TRC | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.4 |
| TRE | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
| TRH | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.4 |
| TRL | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.4 |
| TRM | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.4 |
| TRT | 1.2 | 1.2 | 1.2 | 1.2 | 1 | 0.5 |
| TRW | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
| TTF | 1 | 1 | 1 | 0.8 | 0.6 | 0.1 |
| TTP | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 |
| TWD | 0.8 | 0.8 | 0.8 | 0.6 | 0.5 | 0.1 |
| TWI | 0.8 | 0.8 | 0.8 | 0.6 | 0.5 | 0.1 |
Non-energy demands are not explicitly considered.
Regional GDP per capita is a driver for the model, but there are no income distribution within a given region. Access issues are not considered either.
Regions can be split to additional subregions for the demand level, thus allowing to model demand separately for, for example, urban and rural areas in the Residential sector. Currently, USA and CAN have four and three geographic regions, respectively, while AFR, CHI, IND, MEA and MEX each have two ?sub-regions?, corresponding to rural and urban areas.
Behavioural change
Behaviour and heterogeneous agents and mostly not explicitly considered and is only really determined through price mechanisms e.g. there is no modal shift in the transport sector.
The exceptions from this are technology and region specific hurdle rates and price responsive energy service demands i.e. see Residential sector.
Diffusion constraints can be interpreted to simulate also behaviour related inertia (among the other barriers that are not explicitly included in the model).