ETP-TIMES model for the energy conversion sector
The global ETP-TIMES model is a bottom-up, technology-rich model that covers 28 regions and depicts a technologically detailed supply side of the energy system. It models from primary energy supply and conversion to final energy demand up to 2075. The model is based on the TIMES (The Integrated MARKAL EFOM System) model generator, which has been developed by the Energy Technology Systems Analysis programme (ETSAP) implementing agreement of the International Energy Agency (IEA) and allows an economic representation of local, national and multi-regional energy systems on a technology-rich basis (Loulou et al., 2005).
Starting from the current situation in the conversion sectors (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 industry, transport and buildings (Figure A.2).
Figure A.2 Structure of the ETP-TIMES model for the conversion sector
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.
The ETP-TIMES model also takes into account additional constraints in the energy system (such as fossil fuel resource constraints or emissions reduction goals) and provides 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 of variations in electricity and heat demand, as well as in the generation from some renewable technologies on investment decisions, a year is divided into four seasons, with each season being represented by a typical day, which again is divided into eight daily load segments of three hours' duration.
For a more detailed analysis of the operational aspects in the electricity sector, the long-term ETP-TIMES model has been supplemented with a linear dispatch model. This model uses the outputs of the ETP-TIMES model for the 2050 electricity capacity mix for a specific model region and analyses an entire year with one-hour time resolution using datasets for wind production, solar photovoltaic production, and hourly electricity demand for a year. 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 (gas turbines). Demand response by modifying the charging profile of electric vehicles (EV) is a further option depicted in the model in order to provide flexibility to the electricity system.
Figure A.3 Electricity dispatch in the United States over a two-week period in 2050 in the 2DS
This linear dispatch model represents storage in terms of three steps: charge, store, discharge. The major operational constraints included in the model are 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 terms of energy (megawatt hours [MWh]).
Model limitations include challenges due to a lack of comprehensive data with respect to 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. Further, it is assumed that future demand curves will have the same shape as current curves. A bottom-up approach starting from individual energy service demand curves by end-use technology would be useful in refining this assumption, but is a very data-intensive undertaking that faces the challenge of a lack of comprehensive data.