The JRC-EU-TIMES model - Assessing the long-term role of the SET Plan Energy technologies

The JRC-EU-TIMES model is one of the models currently pursued in the JRC under the auspices of the JRC Modelling Taskforce. The model has been developed over the last years in a combined effort of two of the JRC Institutes, IPTS and IET. The JRC-EU-TIMES model is designed for analysing the role of energy technologies and their innovation for meeting Europe's energy and climate change related policy objectives. It models technologies uptake and deployment and their interaction with the energy infrastructure including storage options in an energy systems perspective. It is a relevant tool to support impact assessment studies in the energy policy field that require quantitative modelling at an energy system level with a high technology detail. This report aims at providing an overview on the JRC-EU-TIMES model main data inputs and major assumptions. Furthermore, it describes a number of model outputs from exemplary runs in order to display how the model reacts to different scenarios. The scenarios described in this report do not represent a quantified view of the European Commission on the future EU energy mix.JRC.F.6-Energy systems evaluatio

Technology Learning Curves for Energy Policy Support

The European Commission's Joint Research Centre and the Energy Research Centre of the Netherlands (ECN) organised an expert workshop on 'Learning Curves for Policy Support' in Amsterdam on 8 March 2012. It aimed to assess the challenges in the application of the two-factor learning curve, or alternative solutions in supporting policy decision making in the framework of the European Strategic Energy Technology Plan, and explored options for improvement. The workshop gathered distinguished experts in the field of scientific research on learning curves and policy researchers from the European Commission and ECN to assess the challenges in the application of the two-factor-learning curve, or alternative solutions in supporting policy decision making, and to provide options for improvement. This paper forms the summary of outcomes from the workshop. Due to the very different nature of the One-Factor-Learning concept and the Two-Factor-Learning concept, these are discussed in separate parts. In each of these parts the context and the methodology are introduced, methodological and data challenges are described and the problems associated with the application of the concept in models is discussed.JRC.F.6-Energy systems evaluatio

Assessing the impacts of technology improvements on the deployment of marine energy in Europe with an energy system perspective

Marine energy could play a significant role in the long-term energy system in Europe, and substantial resources have been allocated to research and development in this field. The main objective of this paper is to assess how technology improvements affect the deployment of marine energy in the EU. To do so the linear optimization, technology-rich model JRC-EU-TIMES is used. A sensitivity analysis is performed, varying technology costs and conversion efficiency under two different carbon-emissions paths for Europe: a current policy initiative scenario and a scenario with long-term overall CO2 emission reductions. We conclude that, within the range of technology improvements explored, wave energy does not become cost-competitive in the modelled horizon. For tidal energy, although costs are important in determining its deployment, conversion efficiency also plays a crucial role. Ensuring the cost-effectiveness of tidal power by 2030 requires efficiency improvements by 40% above current expectations or cost reductions by 50%. High carbon prices are also needed to improve the competitiveness of marine energy. Finally, our results indicate that investing 0.1–1.1 BEuro2010 per year in R&D and innovation for the marine power industry could be cost-effective in the EU, if leading to cost reduction or efficiency improvements in the range explored.JRC.F.6-Energy Technology Policy Outloo

The savings of energy saving: interactions between energy supply and demand-side options - quantification for Portugal

Focusing on supply-side energy policies, like supporting large renewable electricity plants, without simultaneously looking for opportunities in the demand-side, may generate avoidable costs. Reducing demand by increasing energy efficiency may have advantages in reducing fossil dependency and greenhouse gases (GHG) emissions. This paper addresses interactions between energy supply and demand side policies, by estimating the impact of end-use energy-efficiency & renewables applications in terms of (i) avoided electricity generation capacity, (ii) final energy consumption, (iii) share of renewables in final energy and, (iv) reduction of GHG emissions. The Portuguese energy system is used as a case-study. The bottom-up model TIMES_PT was used generate four scenarios till 2020 corresponding to different levels of efficiency of equipment in buildings, transport and industry. In the current policy scenario, the deployment of end-use equipments follows the 2000-2005 trends and the National Energy Efficiency Action Plan targets. In the efficient scenarios, all equipments can be replaced with more efficient ones. Results show that aggressive industry and buildings demand-side measures can make the increase in renewable electricity capacity with approximately 4.7 GW as discussed by policy-makers superfluous. These measures reduce only 0-2% of total final energy, but this represents reduction of 11-14% in the commercial sector, with savings in total energy system costs of approximately 3000 M€2000 - roughly the equivalent to 2% of the 2010 GDP. The cost-effectiveness of policy measures should guide choices between supply shifts and demand reduction. Such balanced policy development can lead to substantial cost reductions in climate and energy policy.JRC.F.6-Energy Technology Policy Outloo

The impact of location on competitiveness of wind and PV power plants – case study for Austria

The generation potential of renewable energy sources and the time profile of production depends on geographic characteristics. Furthermore, the integration of renewable energy technologies into the energy system is constrained by current infrastructure. The paper does a preliminary assessment on how different choices of locations for the development of wind and photovoltaic power plants in Austria could affect the national energy system by 2020 and 2050. We use highly spatially and temporally resolved synthetic time series for potential sites of wind power and PV. We use the technology bottom-up JRC EU TIMES optimization model to assess how the choice of different locations for renewables affects the power system in terms of costs and CO2 emissions. Results show that disaggregating RES locations significantly affects results especially for wind leading higher electricity generation from wind and reduced electricity trade with other countries.JRC.F.6-Energy Technology Policy Outloo

Decarbonised pathways for a low carbon EU28 power sector until 2050

The linear optimization bottom-up technology model JRC-EU-TIMES is used to assess how different decarbonisation pathways affect the power sector's technological deployment till 2050. The model represents the EU28 energy system from 2005 to 2050, where each country is one region. We model eight scenarios, two of which are the "reference" complemented by six decarbonized pathways. The two "reference" scenarios are the Current Policy Initiatives scenario, including the 20-20-20 policy targets, and the Cap85 scenario with a CO2 reduction of 85% below 1990 values in 2050. The six decarbonized pathways are built over Cap85 as follows: smaller contribution of CCS; higher social acceptance and facilitated permitting of RES plants; higher social acceptance of nuclear plants; stricter and more effective end-use energy efficiency requirements; lower biomass availability for the energy system; and higher concerns with ensuring the reliability of transmission and distribution, reducing the share of intermittent variable solar and wind electricity.JRC.F.6-Energy Technology Policy Outloo

Assessing the role of electricity storage in EU28 until 2050

The linear optimization bottom-up technology model JRC-EU-TIMES is used to assess the cost-effectiveness of electricity storage technologies in the EU28 energy system. The model represents the EU28 energy system from 2005 to 2050, where each country is one region. We model five scenarios, one of which is the "reference" and four decarbonized scenarios that include the 20-20-20 policy targets and a CO2 reduction of 40% and 85% below 1990 values in 2030 and 2050 respectively. The introduction of a CO2 cap leads in 2050 to a 25-50% share of total electricity that is variable. Up to 20% of this renewable variable electricity is stored in batteries and CAES storage technologies.JRC.F.6-Energy Technology Policy Outloo

THE EFFECT OF LIMITED RENEWABLE RESOURCES ON THE ELECTRICITY GENERATION IN A LOW-CARBON EUROPE

Renewable-based electricity generation technologies are recognized to be a major-prerequisite to reach a sustainable and secure energy system by 2050 [1]. From an energy-systems analysis perspective, a significant increase in the relevance of renewable electricity generation technologies is foreseen [2] both in current policies scenarios and in those targeting over 80% CO2 emission reductions in 2050. Usually the impact of current and future technology cost on their competitiveness is analysed [2], while less attention has been given to investigate the total amount of renewable resources available. Work has been already conducted to evaluate these renewable potentials [3], but still substantial uncertainties remain due to methodological challenges or due to coarse measuring resolution [4]. Moreover, technological improvements may shift the boundaries of exploitable renewable sources. It is therefore critical to understand, within an energy system perspective, how uncertainties and future changes in resource availability could influence the transition to a low-carbon system in Europe. In this paper we will assess the sensitivity of the electricity mix evolution to the available renewable energy sources potentials by analysing the response of the partial equilibrium energy model JRC-EU-TIMES [5] in the 2020-2050 time frame. Results unveil the relation between the renewable potential available for a given technology and its window of opportunity amongst its competing technologies. For instance, wind onshore shows to be more responsive to potential variations in the midterm than wind offshore. This research will help to prioritize which resource potentials may require more precise quantification, or to quantify the economic impact in the future energy system that persisting uncertainties in such potentials may have. Understanding how changes in the potential impact the energy will also help inform R&D priority setting, with a focus on those technological improvements that, other things being equal, will allow a higher share of the renewable source to be harnessed.JRC.F.6-Energy Technology Policy Outloo

Top-down and bottom-up modelling to support low carbon scenarios: climate policy implications

Bottom-up (BU) and top-down (TD) models have been supporting climate policies, identifying the options required to meet greenhouse gases (GHG) abatement targets and evaluating its economic impact. Some studies have shown that GHG mitigation options from economic TD and technology BU models tend to vary, being different baseline scenarios one recognised divergence factors. This article explores the use of TIMES_PT (BU) and the general equilibrium GEM-E3_PT (TD), assessing the extent of their differences in mitigation options when calibrated to a common baseline scenario and how different outcomes are relevant for domestic climate policy making. Three low carbon scenarios were generated until 2050, with different GHG reduction targets for the case study of Portugal. The models present close mitigation options, allocating the larger mitigation potential to energy supply. However, they suggest different in mitigation options for end-use sectors. GEM-E3_PT focus more on energy efficiency, while TIMES_PT relies on carbon intensity decrease by shifting to electricity. Common baseline scenario cannot be ignored but the models inherent characteristics’ are the key factor for different outcomes, highlighting different mitigation recommendations.JRC.F.6-Energy systems evaluatio

Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria

This paper assesses how different levels of geographical disaggregation of wind and photovoltaic energy resources could affect the outcomes of an energy system model by 2020 and 2050. Energy system models used for policy making typically have high technology detail but little spatial detail. However, the generation potential and integration costs of variable renewable energy sources and their time profile of production depend on geographic characteristics and infrastructure in place. For a case study for Austria we generate spatially highly resolved synthetic time series for potential production locations of wind power and PV. There are regional differences in the costs for wind turbines but not for PV. However, they are smaller than the cost reductions induced by technological learning from one modelled decade to the other. The wind availability shows significant regional differences where mainly the differences for summer days and winter nights are important. The solar availability for PV installations is more homogenous. We introduce these wind and PV data into the energy system model JRC-EU-TIMES with different levels of regional disaggregation. Results show that up to the point that the maximum potential is reached disaggregating wind regions significantly affects results causing lower electricity generation from wind and PV.JRC.C.7-Knowledge for the Energy Unio