newTRENDs Modeling Suite
The FORECAST model is part of a larger energy systems model family operated by Fraunhofer ISI29 designed as a tool that can be used to support strategic decision making.
Its main objective is to support the scenario design and analysis for the long-term development of energy demand and greenhouse gas emissions for the industry, residential and tertiary sectors on EU MS level.
FORECAST considers a broad range of mitigation options to reduce CO2 emissions (e.g. incremental energy efficiency improvements, fuel switch to RES, innovative process technologies, CCS), combined with a high level of technological detail (e.g. FORECAST Industry models more than 200 saving options for more than 70 industrial processes). It is based on a bottom-up modelling approach considering the dynamics of technologies and socio-economic drivers. Technology diffusion and stock turnover are explicitly considered to allow insights into transition pathways.
The model further integrates different energy efficiency and decarbonisation policy options and allows to address research questions related to energy demand including the analysis of scenarios for the future demand of individual energy carriers like electricity or natural gas, calculating energy saving potentials and the impact on greenhouse gas (GHG) emissions as well as abatement cost curves, ex-ante policy impact assessments and the investigation of long-term sustainable energy transition scenarios (www.forecast- model.eu, Jakob et al. 2013, Fleiter et al. 2018, Herbst et al. 2017).
Within the project, the FORECAST model was used in WP3, WP5, WP6 and WP7 to calculate the decarbonisation pathways for the tertiary and the industrial sector, as well as for electric appliances considering new trends in prosumaging, digitalization and circular economy. newTRENDs applied the models belonging to the FORECAST family to different sectors and different focus studies: FORECAST-Industry for the modelling of circular economy in the industry sector and FORECAST-Tertiary for digitalization and shared economy in the tertiary sector.
Invert/EE-Lab is a dynamic bottom-up techno-socio-economic discrete choice simulation tool that evaluates the effects of different policy packages on the total energy demand, energy carrier mix, CO2 reductions and costs for space heating, cooling, hot water preparation and lighting in buildings. The model is based on highly disaggregated data of the building stock. Each building segment is described by geometry data, U-values of building components, construction period, age and type of installed heating and hot water system etc.
The core of the tool is a nested logit approach, which optimizes objectives of agents under imperfect information conditions and by that represents the decisions maker concerning building related decisions.
In over 40 projects and studies for more than 30 countries, the model has been extended and applied to different regions within Europe, see e.g. (Kranzl et al., 2012), (Kranzl et al., 2013), (Biermayr et al., 2007), (Haas et al., 2009), (Kranzl et al., 2006), (Kranzl et al., 2007), (Nast et al., 2006), (Müller, 2010), (Müller, 2015).
Within the project, the Invert/EE-Lab model was used in WP3 and WP5 to analyse decarbonisation pathways for the building sector, considering new trends in prosumaging.
The PRIMES (Price-Induced Market Equilibrium System) is a large-scale applied energy system model providing detailed projections of energy demand, supply, prices and investment to the future, covering the entire energy system including emissions. The distinctive feature of PRIMES is the combination of behavioural modelling (following a micro-economic foundation) with engineering aspects, covering all energy sectors and markets. The model has a detailed representation of policy instruments related to energy markets and climate, including market drivers, standards, and targets by sector or overall (over the entire system). It handles multiple policy objectives, such as GHG emission reductions, energy efficiency and renewable energy targets, and also provides a pan-European simulation of internal markets for electricity and gas.
PRIMES offers the possibility of handling market distortions, barriers to rational decisions, behaviours, as well as and market coordination issues and includes a complete accounting of costs (CAPEX and OPEX) and investment expenditure on infrastructure needs. PRIMES is designed to analyse complex interactions within the energy system in a multiple agent – multiple markets framework. Decisions by agents are formulated based on a microeconomic foundation (utility maximization, cost minimization and market equilibrium) embedding engineering constraints, behavioural elements and an explicit representation of technologies and vintages and optionally perfect or imperfect foresight for the modelling of investments in all sectors. PRIMES is well-placed to simulate medium and long- term transformations of the energy system (rather than short-term ones) and includes non-linear formulation of potentials by type (resources, sites, acceptability etc.) and technology learning.
The PRIMES model runs in 5-year time steps from 2020 to 2070; the years 1990 to 2015 are calibrated to statistics. Yearly resolution can be made available upon request. The PRIMES model covers all 28 EU Member States individually with country specific models; the model is further available for 10 other European countries.
Specifically, the following parts of the model30 were improved in the course of the planned project notably in WP3, WP5 and WP7:
- PRIMES-TREMOVE transport model: recently enhanced to include linkage to synthetic fuels and hydrogen and to detailed spatial projections of transport activity and route assignment by the forthcoming TRIMODE model31 (used notably in WP7); recent publications include (Siskos et al. 201932, Siskos et al. 201833)
- PRIMES BuiMo residential and services model: new model with high resolution representation of the housing and office building stock, embedded in an economic-engineering model of multi-agent choice of building renovation, heating system and equipment/appliances by energy use (used in WP5); recent publications include (Fotiou et al. 201934)
- PRIMES Electricity and Heat/Steam supply and market model: fully new model version which includes the hourly unit commitment model – with a pan-European market simulation over the grid constraints and detailed technical operation restrictions – the long-term power system expansion model, the costing and pricing electricity and grid model, the integration of heat supply and industrial steam supply with synchronised hourly operation. This part of the PRIMES model allows analysing impacts of Prosumagers on the supply sector in WP5.
GEM-E3-FIT is an advanced and detailed CGE model version of the standard GEM-E3 model, and improves it in the following ways:
- it represents the financial sector explicitly
- it represents policy-induced technical change and innovation-induced growth by two-factor learning curves (learning by doing and learning by research)
- it represents household decisions on education affecting the human capital
- it links the human capital with the creation of knowledge
- it links the human capital with the ability to absorb knowledge spill-overs
- it has an explicit representation of infrastructure
- it provides built-in options for Monte Carlo simulations to perform sensitivity analysis
- it includes a detailed representation of transport (freight and passenger by mode)
- it includes a discrete representation of sectors producing clean energy technologies (wind, PV, CCS, electric vehicles, biofuels, batteries, insulating materials)
- it is calibrated to the most recent version of GTAP 9 and the years 2004, 2007 and 2011
- it has detailed inter-institutional transactions precisely determining the surplus/deficit position of each agent
- it has a high degree of sectoral (economy is disaggregated into 53 productive sectors) and regional resolution (46 countries/regions are represented) it has a new calibration of energy volumes that combine in a consistent way data from energy balances and IO tables
- it has detailed data on energy subsidies globally based on IEA dataset
- it includes learning by doing and learning by research associated with knowledge spill-over matrices based on patent citations data
- accounts for the number of firms by economic activity and calculates profitability rates of each activity
A new modelling feature that allows households to endogenously decide upon the optimal schooling-education years is introduced. Skills representation is extended to five skill levels based on the GTAP classification and linked to decisions of households regarding education, which then influenced the labour productivity of skills.
The GEM-E3-FIT model runs in 5-year time steps from 2020 to 2070 ; the years 1990 to 2015 are calibrated to statistics. The GEM-E3-FIT is a global model with detailed country resolution for several countries including all EU28 Member States.
This model is used to study macro-economic impacts of New Societal Trends, while also investigating the needs for model adaptations to follow adequately their impacts on the overall economy.
OTHERS
The newTRENDs partners have developed FLEX, an open-source modeling suite for the operation and energy consumption of households and energy communities. It is now freely available to all on Github.
How will prosumers and energy communities alter the way we produce, manage and use energy in the future? newTRENDs also developed the third module of the suite: FLEX-Behaviour.
New modules, e.g. FLEX, PRTRANS, PRIMES-Prosumager, flow, were developed as part of the project. See more details in the project final report.
Available on Github.
Objectives
The aim of newTRENDs (New trends in energy demand modeling) was to increase the qualitative and quantitative understanding of impacts of New Societal Trends on energy consumption and to improve the modelling of energy demand, energy efficiency and policy instruments. Through this, the ability of policy makers to guide those trends in the light of the Paris Agreement and the long-term climate and energy targets of the European Union was increased.
Derived from this overall objective, the project newTRENDs has the three detailed sub goals.
- The first goal aims at identifying and quantifying how New Societal Trends affect energy demand (its structure and patterns, including cross-sectoral interdependencies).
- The subsequent goal aims to investigate how energy demand models are to be improved to represent New Societal Trends and to represent policies that can influence such trends in the light of the Energy Efficiency First Principle in energy demand models.
- The final goal aims for integrating recent empirical findings on the impacts of New Societal Trends as well as information from detailed data sources such as smart meter data available from recent technical advances into energy demand models, in order to improve the empirical basis for such investigations. Special care is given to uncertainties that are inherent when assessing New Societal Trends.
Methods
From a methodological perspective, three major aspects characterise the newTRENDs project. Firstly, the combination of foresight methods with quantitative model runs is implemented to select appropriate trends and work out, how such trends can be quantified. For this purpose relevant trends are selected and their relevance for the energy system is assessed during a deep dive analysis. A condensation of those trends in clusters as well as the translation to model parameters and modelling gaps is carried out.
Secondly, it is investigated how existing, well-known energy demand models are to be improved to represent New Societal Trends, e.g. through agent-based and cross-sectoral approaches and how policies are represented in the demand models. For this, an initial scenario run of the existing demand models is carried out. Based on this a gap analysis of modelling structure as well as empirical data and an analysis of necessary model adaption is implemented. After realizing the model adaptions, a second scenario run is carried out for the comparison with the initial results.
The third methodological aspects focuses on the data perspective and aims to integrate recent empirical findings on consumption patterns and policy impacts. Those data will be analysed statistically and integrated in the models focussing on prosumager behaviour. In addition, the data can be used for policy analysis.
