ETP-TIMES supply model
The global ETP-TIMES Supply model is a bottom-up, technology-rich model that depicts a technologically detailed supply side of the energy system. It models from primary energy supply and conversion to final energy demand up to 2060. It is based on the TIMES (The Integrated MARKAL-EFOM System) model generator, which was developed by the Energy Technology Systems Analysis Programme (ETSAP) Technology Collaboration Programme (TCP)2 of the International Energy Agency (IEA) and allows an economic representation of local, national and multiregional energy systems on a technologically detailed basis (Loulou et al., 2005).
The model covers 28 regions, representing either individual countries, such as the People’s Republic of China (hereafter China) or India, or aggregates of several countries, such as the Association of Southeast Asian Nations (ASEAN). The model regions are linked by trade in fossil energy carriers (crude oil, petroleum products, coal, pipeline gas, liquefied natural gas [LNG]), biofuels (biodiesel, bioethanol) and electricity.
FIGURE A.2 - Structure of the ETP-TIMES model for the conversion sectoRegional
Key Point: ETP-TIMES determines the least-cost strategy using supply-side technologies and fuels to cover the final energy demand from the end-use sector models.
Starting from the current situation in the conversion sector (e.g. existing capacity stock, operating costs and conversion efficiencies), the model integrates the technical and economic characteristics of existing technologies that can be added to the energy system. The model can then determine the least-cost technology mix needed to meet the final energy demand calculated in the ETP end-use sector models for agriculture, buildings, industry and transport (Figure A.2).
Technologies are described by their technical and economic parameters, such as conversion efficiencies or specific investment costs. Learning curves are used for new technologies to link future cost developments with cumulative capacity deployment. Overall, around 550 technologies are considered in the conversion sector. Electricity demand is divided into non-urban and urban, with the latter further divided into five city classes by population size to reflect local differences in the technical potential for rooftop solar photovoltaics (PV) and municipal solid waste (IEA, 2016a; IEA, 2016b). Renewable energy sources – onshore and offshore wind, solar PV and solar thermal electricity (STE) – are differentiated according to their potential, based on their capacity factor (in addition for offshore wind by water depth and distance to the coast) and by their distance to the city classes (five distance categories) as an approximation for the transmission costs needed to use these resources. The ETP-TIMES model also takes into account additional constraints in the energy system (such as emissions reduction goals), and its results provide detailed information on future energy flows and their related emissions impacts, required technology additions and the overall cost of the supply-side sector.
To capture the impact on investment decisions of variations in electricity and heat demand, as well as the variation in generation from certain renewable technologies, a year is divided into four seasons, with each season being represented by a typical day, which again is divided into 12 daily load segments of two hours’ duration.
For a more detailed analysis of the operational aspects of the electricity sector, the long-term ETP-TIMES Supply model has been supplemented with a linear dispatch model. This model uses the outputs of the ETP-TIMES Supply model to generate the electricity capacity mix for a specific model region and year. This allows for detailed analysis of an entire year with one-hour time resolution using datasets for wind production, solar PV production and hourly electricity demand.
Figure A.3 - Dispatch in the United States over a two-week period in 2050 in the 2DS
Key Point: The linear dispatch model analyses the role of electricity storage, flexible generation and demand response.
Given the hourly demand curve and a set of technology-specific operational constraints, the model determines the optimal hourly generation profile, as illustrated in Figure A.3 for the 2DS in 2050 over a two-week period. To increase the flexibility of the electricity system, the linear dispatch model can invest in electricity storage or additional flexible generation technologies (such as gas turbines). Demand response from electricity use in the transport and buildings sectors is a further flexibility option included in the dispatch model analysis.
This linear dispatch model represents storage in terms of three steps: charge, store, discharge. The major operational constraints included in the model are capacity states, minimum generation levels and time, ramp-up and -down, minimum downtime hours, annualised plant availability, cost considerations associated with start-up and partial-load efficiency penalties, and maximum storage reservoir capacity in energy terms (megawatt hours [MWh]).
Model limitations include challenges associated with a lack of comprehensive data on storage volume (MWh) for some countries and regions. Electricity networks are not explicitly modelled, which precludes the study of the impacts of spatially dependent factors, such as the aggregation of variable renewable outputs with better interconnection.
2. Further information on the TIMES model generator, its applications and typical energy technology input data assumptions can be found on ETSAP’s website, www.iea-etsap.org.