4.1 Motivos de aumento de la tele audiencia
4.1.2 Aspectos a mejorar
A classification of the reviewed integrated energy and transport models (E+T), according to
geographic scope, time horizon, mathematical method and modelling approach is presented in Table 5.2.
The vast majority of the studies are used for long-term analyses with a time horizon of 50-100 years, as evident from Table 5.2. This observation is in line with the fact that energy and transport models are often developed to assess optimal long-term pathways towards a certain environmental goal and to inform decision makers early in advance of policies and measures which can be efficient and effective in the long run. On the other hand, sectoral transport models (T) often focus on traffic assignment in a shorter run, due to e.g. the underlying uncertainties on the future development of the road infrastructure.
With regards to the geographical scope, 12 out of the 27 studies reviewed have a country scope, 10 have a global outlook, 3 are developed at regional level, and 1 at city level. However, many of these models are adaptable to different geographical contexts (see the open source energy system model OSeMOSYS (Howells et al., 2011)) and can be applied to perform comparative studies for different countries (Mittal et al., 2016, Zhang et al., 2016). Some of the models (e.g. PRIMES-TREMOVE (E3MLab/ICCS, 2014), TRAVEL (Girod et al., 2012) and UKTCM (Brand et al., 2012)) are detailed representations of the transport sector, which can be linked or integrated within a wider energy system model. In this latter case, mathematical method and modelling approach refer to the more detailed transport module.
Table 5.2: Classification of integrated energy and transport models Model Name Geographic
Scope
Time Horizon Mathematical Method Modelling Approach Focus Reference AIM/End-use China India
2010-2050 O BU Comparison of low carbon
urban transport scenarios for China and India
(Mittal et al., 2016)
Balmorel Nordic and Baltic countries
Year 2030 with hourly resolution
O BU Creation of a road transport add-on to traditional Balmorel
model, to assess integrated power and transport systems
and vehicle to grid
(Juul and Meibom, 2011)
BLUE United
Kingdom
2010-2050 S H Dynamic stochastic simulation
of technology diffusion, energy and emissions
(Li and Strachan, 2016)
CIMS Canada 2005-2035 S H Modelling technological changes
in a more behaviourally realistic manner in order to facilitate
policy analysis for a greater range of technologies
(Horne et al., 2005)
COCHIN- TIMES
California 2005-2050 O BU Demonstration of a practical approach for incorporating behavioural effects from vehicle
choice models into E4 models
(Bunch et al., 2015)
ECLIPSE Global 2000-2100 CGE H Development of an integrated
energy-economy model with a detailed transport sector
representation
(Turton, 2008)
EnergyPLAN Denmark Year 2020 with hourly resolution
S BU Integration of renewable energy into the transport and electricity sectors through vehicle-to-grid
technology
(Lund and Kempton, 2008)
EPPA Global 2005-2050 CGE TD Disaggregation of the passenger
vehicle transport sector in a CGE model
(Karplus et al., 2013)
ESME United
Kingdom
2010-2050 O BU Representation of endogenous
mode shift for urban passenger travel in a whole energy system
model
(Pye and Daly, 2015)
ExSS Ahmedbad, India
2015-2035 S BU Analysis of co-benefits of low- carbon passenger transport
actions in an Indian city
(Pathak and Shukla, 2016)
GCAM Global 2005-2095 S BU Long-term effect of alternative
vehicles on greenhouse gas emissions and energy demand
(Kyle and Kim, 2011, Mishra et al., 2013)
GET-R Global 2010-2100 O BU Analysis of fuel and vehicle
technology choice for passenger transport under CO2 targets
(Grahn et al., 2013)
IMACLIM-R Global 2001-2100 CGE H Implications of modelling non-
price determinants of mobility
(Waisman et al., 2013)
Irish TIMES CA-TIMES
Ireland, California
2005-2050 O BU Incorporating modal choice
within passenger transport in a TIMES model
MESSAGE Global 2005-2100 O BU Introduction of consumers’ heterogeneity and non-
monetary parameters (McCollum et al., 2016) OSeMOSYS User- dependent User- dependent
O BU Description of the open source energy system model
(Howells et al., 2011)
PET36 Europe 2005-2050 O BU Assess the cost-effectiveness of
electric vehicles in European countries
(Seixas et al., 2015)
PRIMES- TREMOVE
Europe 2005-2050 S BU Advanced transport module for
scenario and policy analysis of the European transport sector, stand-alone or fully linked with
PRIMES energy model
(E3MLab/ICCS, 2014)
ReMIND Global 2005-2100 O H Analysis of technology and
mode shift as different mitigation options for the
transport sector
(Pietzcker et al., 2010)
SATIM South Africa 2006-2050 O BU Describing the TIMES model of the entire energy system in
South Africa
(Merven et al., 2012)
TIAM-UCL Global 2010-2100 O BU Explore the competitive and/or
complementary relationship between hydrogen and electricity, with endogenous
technological learning
(Anandarajah and McDowall, 2015)
TIMES California
California 2005-2050 O BU Assess least-cost mitigation options required to meet California’s long-term 80%
greenhouse gas emission reduction goal, by considering
all the energy sectors
(McCollum et al., 2012)
TIMES Canada
Canada 2007-2050 O BU Perform policy analysis for
promoting electrification of road transport in Canada
(Bahn et al., 2013)
TRAVEL Global 2010-2100 S BU Predict global travel demand,
modal split shifts, and changes in technology and fuel choice
(Girod et al., 2012)
UKTCM United
Kingdom
2010-2050 S BU Policy analyses and low carbon strategy development for the
transport sector (Brand et al., 2012) US-TIMES China-TIMES US China 2010-2050 O BU Comparison of transport
scenarios between China and US, with focus on technological
shift
(Zhang et al., 2016)
WITCH Global 2005-2100 O H Review of the electrification of
light duty vehicles within a model that utilizes a learning-
by-researching structure
(Bosetti and Longden, 2013)
Notes: S: simulation, O: optimization, CGE: computable general equilibrium, BU: bottom-up, TD: top-down, H: hybrid
Figure 5.2 reports a cross classification for the 27 reviewed studies, according to modelling approach and mathematical method. Most ‘E+T’ models considered fall in the category of optimization models (16), while amongst the remaining, 8 are simulation models and 3 are CGE. Among optimization
models, the majority are BU models (14), while 2 belong to the hybrid type. Once more, the TD approach is traditionally used in macroeconomic models, where the energy and transport systems appear at a more aggregated level. Hence, an ‘E+T’ model with a detailed representation of the transport system is often not possible or not pursued.
Transport models ‘T’ and ‘T+’, focusing on the factors that affect mobility decisions, are mainly based on a simulation method. On the other hand, the review highlights that most of the ‘E+T’ models adopt an optimization method. Therefore, when the aim is to incorporate transport behavioural features in energy models, the challenge of combining a simulation approach within a traditional optimization model structure needs to be considered. For instance, the structure of the nested multinomial logit model MA3T (Lin, 2015, Lin and Greene, 2010) is replicated in the optimization model COCHIN-TIMES (Bunch et al., 2015).
Figure 5.2: Mathematical method and modelling approach of the reviewed E+T models
Six out of the 27 reviewed references are hybrid models, which combine the top-down with the bottom-up approach. As further highlighted in Section 5.4, hybrid models better allow introducing a detailed modelling of technological, macroeconomic and microeconomic characteristics of the energy system. Nevertheless, modelling and computational difficulties may arise when introducing several parameters and constraints in one single model framework. Therefore, most attention has been set on integrating the various approaches through model linking with the aim to harness the richness of each model type through the creation of an interaction. Section 5.3.4 provides a
comprehensive review of the model linking techniques used between energy models with a focus on the linkage between energy and transport models.