FORECAST/eLOAD

 
 

 Fraunhofer Institute for Systems and Innovation Research (ISI)

 
 

Institute for Resource Efficiency and Energy Strategies

 
 

      TEP Energy GMBH

Methodology

 
 
FORECAST 
FORECAST-Regional
eLOAD

annual energy demand

Overview

The FORECAST modelling platform aims to develop long-term scenarios for future energy demand of individual countries and world regions until 2050. It is based on a bottom-up modelling approach considering the dynamics of technologies and socio-economic drivers. The model allows to address various research questions related to energy demand including 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 and ex-ante policy impact assessments.

Model structure

The FORECAST platform comprises four individual modules, each representing one sector according to the Eurostat (or national) energy balances: industry, services/tertiary, residential and others (agriculture and transport). While all sector modules follow a similar bottom-up methodology, they also consider the particularities of each sector like technology structure, heterogeneity of actors and data availability.

Figure: Definition scenariozoom
Figure: Definition scenario

The list of selected input data as shown in the following table provides a broad idea of the level of detail of each module. Each sector requires sector specific activity data, like industrial production in the industry sector and the number of households in the residential sector. Furthermore, end-consumer energy prices play an important role in each sector as they are distinguished by energy carrier. The third group of input data, the technology characterisation also reflects data availability of the individual sectors. While in the industry and tertiary sector the model works with so-called energy-efficiency measures (EEMs), which represent all kinds of actions that reduce specific energy consumption, in the residential sector the stock of alternative appliances and the market share of different efficiency classes is explicitly modelled. In all cases, energy savings can be calculated and traced back to technological dynamics including cost considerations.

Table zoom

Modeling investment decisions

The bottom-up approach, which distinguishes individual technologies, allows modeling the diffusion of technologies as the result of individual investment decisions taken over time. For all types of investment decisions, the model follows a simulation approach rather than optimization in order to better capture the real-life behavior of companies and households.

Whenever possible, the investment decision is modeled as a discrete choice process, where households or companies choose among alternative technologies to satisfy a certain energy service. It is implemented as a logit-approach considering the total cost of ownership (TCO) of an investment plus other intangible costs. This approach ensures that even if one technology choice is more cost-effective than the others, it will not gain a 100% market share. This effect reflects heterogeneity in the market, niche markets and non-rational behavior of companies and households, which is a central capability to model policies. Still, the resulting technology development (and energy demand) is price sensitive.

The replacement of equipment/buildings/technologies is based on a vintage stock approach allowing to realistically model the replacement of the capital stock considering its age distribution. Some parts of the industrial and the tertiary sector are not using a vintage stock approach, due to the huge heterogeneity of technologies on the one hand and data scarcity on the other. Technology diffusion, however, is modeled based on a similar simulation algorithm taking heterogeneity and non-rational behavior into account.

Modeling policies

Modeling energy-efficiency policies is a core feature of the FORECAST model. The simulation algorithm and the vintage stock approach are well suited to simulate most types of policies. 

Minimum energy performance standards (MEPS), e.g. for appliances or buildings, can easily be modeled by restricting the market share of new appliances starting in the year the standards come into force. See Elsland et al. (2013) and Jakob et al. (2013) for examples of ex-ante impact assessments of the EU-Ecodesign Directive.

Energy taxes for end-consumers can be modeled explicitly on the basis of more than 10 individual energy carriers (electricity, light fuel oil, heavy fuel oil, natural gas, lignite, hard coal, district heating, biomass, etc.).

Information-based policies are generally the most complicated to model due to their rather “qualitative character”. The discrete-choice approach, however, allows to consider such qualitative factors. E.g. labeling of appliances resulting from the EU Labeling Directive can be modeled by adjusting the logit parameters and thus assuming a less heterogeneous market, in which a higher share of consumers will select the appliance with the lowest total cost of ownership. See for example Elsland et al. (2013).

EU emissions trading can be modeled in the form of a CO 2 tax for energy-intensive industries. The detailed technology disaggregation in the industrial sector considering more than 60 individual products allows to consider the scope of the EU ETS on a very detailed level (examples of products are: clinker, flat glass, container glass, primary and secondary aluminium, oxygen steel, electric steel, coke, sinter, paper, ceramics, ammonia, adipic acid, chlorine). See Fleiter et al. (2012) for a case study on the German paper industry taking EUA prices into account.

Database

The FORECAST database has improved continuously incorporating the results/extensions from the above-mentioned studies.

The main economic input like energy balances, employment, value added or energy prices are calibrated to most recent EUROSTAT statistics whenever possible. When such data was not available (prices for certain energy carriers) IEA data was used to fill the gaps.

In the following an overview of the main sources is provided by model segment for technology-related data not available in EUROSTAT :

Buildings and heating systems: Buildings Performance Institute Europe (BPIE), IEE project TABULA, IEA Building Energy Efficiency Policies (BEEP), IEE project EPISCOPE, ODYSSEE database, country specific research e.g. for heat pumps 

Appliances residential sector: Ecodesign Directive preparatory studies, ODYSSEE database, market research data from GfK 

Appliances tertiary sector : Ecodesign Directive preparatory studies and additional individual technology studies.

Industrial production : PRODCOM when possible, UN commodity production database, US geological survey, UNFCCC, industry organizations (World steel organization, CEPI, Cembureau, Eurochlor, etc.) 

Industry cross-cutting technologies : various technology studies of which many are EU projects 

Industry process technologies : IPPC BREF studies, numerous technology/sectoral studies

Besides these sources, many more, even country specific sources, statistics and reports are used to feed the model database.

spatially distributed annual electricity demand

Overview

FORECAST-Regional is applied in a subsequent step to the FORECAST model. In this way, the granularity of FORECAST results is used to generate downstream a spatial resolution of electricity demand, which generates differentiated conclusions about the individual regional units. Hence, this regionalisation approach is implemented as a two-step-process: (i) the national electricity demand is calculated based on FORECAST and (ii) a regional allocation is estimated by applying sectoral distribution keys via FORECAST-Regional.

Model structure

The national electricity demand is spatially resolved by FORECAST-Regional in a step subsequent to the FORECAST model. Besides the national electricity demand the data input of FORECAST-Regional is based on the Regional Database, which represents the numerical framework for the spatial resolution. In terms of methodology, sectoral distribution keys are applied to split electricity demand into regional units (e.g. gross value added per sub-sector, population density per region). This approach leads to a breakdown of the national electricity demand by districts/municipalities. In parallel with this, the drivers and electricity demand are compared with official statistics at a regional level for selected large German cities and on a district level by state: the so-called Multilevel Validation. The results of FORECAST-Regional are calculated as total consumption, potentials and indicators. Subsequently, a clustering of the regions by structural characteristics and control zone of the transmission system operator (TSO) is accomplished to provide a sufficient framework for result analysis.

Figure: Structure of the FORECAST-Regional approachzoom
Figure: Structure of the FORECAST-Regional approach

Sectoral distribution keys

National electricity demand is broken down by region using sectoral top-down distribution keys, which are derived from demand theory. The objective is to transform the heterogeneous composition of national electricity demand into regional structures. Hence, given the modelling of national electricity demand in FORECAST, and considering sectoral differences, a unified methodical approach is applied. Examples include the differentiation of electricity demand of the household sector by appliances/lighting, electricity demand of heating systems attributed to hot water generation, electricity demand of heating systems attributed to space heating and electricity demand attributed to small appliances. This distinction is based on existing studies and allows effects such as the share of electricity based heating systems to be examined. Within FORECAST-Regional the following parameters differentiated by electricity demand categories are used for the sectoral distribution keys:

  1. Residential sector:
    Appliances/lighting:
    specific electricity demand per size of household, households by number of inhabitants, disposable income
    Hot water
    : specific electricity demand per size of household, households by number of inhabitants, disposable income
    Space heating
    : specific electricity demand per size of household, number of households living area per household, disposable income, climate factor (primarily capturing temperature and radiation), binary variable if an electricity-based heating system is available in a household
    Residual
    : specific electricity demand per inhabitant, disposable income, number of inhabitants
  2. Tertiary sector:
    Healthcare
    : specific electricity demand per process, employee by subsector, floor area per employee by subsector, gross value added by subsector, hospital beds
    Education
    : specific electricity demand per process, employee by subsector, floor area per employee by subsector, gross value added by subsector, number of students
    Residual subsectors : specific electricity demand per process, employee by subsector, floor area per employee by subsector, gross value added by subsector
  3. Industry sector:
    Energy intensive industry:
    specific electricity demand per process, gross value added by subsector, locations, production capacity
    Non-energy intensive industry:
    specific electricity demand per process, gross value added by subsector
  4. Agricultural sector: specific electricity demand per gross value added, gross value added
  5. Electro mobility: specific electricity demand per capita, certifications of electric vehicles, population, surface per regional unit
  6. Others (mainly railroad transport): specific electricity demand per gross value added, gross value added  

The data basis with the highest granularity available is used to transform this parameter set into sectoral distribution keys. However, since the data availability is restricted for some subsectors, ratios of national levels are used to fill the data gap. This provides consistent values from a top-down perspective, however, from a bottom-up perspective this approach can lead to regional miscalculations. Furthermore, if there is no location-specific data for the electricity consumption or any sufficient basis for analogous assumptions available, the approach is based on the assumption that the technological structure within the considered category is homogeneous. This means, that in the case of the production of one ton of steel in plant A, the same specific consumption would be used to decompose electricity demand for plant B. This procedure accounts for the restricted availability of data.

Multilevel validation

The multilevel validation is an approach to establish a correct balance on various administrative levels of structural data and sectoral electricity demand. Balancing means that a comparison between regionalised electricity demand, resulting from a distribution key-based break down, and historical data for large cities, federal states and on a national level is accomplished. On the one hand, this would allow the detection of possible reasons for the deviations caused by the top-down approach differentiated by input and output data. On the other hand, the Multilevel Validation approach can be used to recalibrate the distribution keys. This method of validation, compared to the conventional regionalisation approaches discussed in section  II is an essential feature of this new approach; the validation is usually based on a comparison to the national electricity demand without considering the consistency of levels in between. To generate complete data consistency with the national FORECAST data, any remaining deviation needs to be eliminated by normalisation. This normalisation is accomplished in a two-step-process: (i) for the drivers, to eliminate the driver-attributed deviations and, (ii) for the electricity demand. A comparison of the various data sources used shows that even in a theoretical case of perfectly calibrated distribution keys a normalisation is still needed. This is because the sources are usually based on different methodological approaches for balancing and data collection and so minimal deviations will always occur.

Database

The Regional Database contains data for all parameters needed for the spatial resolution. The selection of data is oriented towards the typical drivers that are used for ex ante analysis in bottom-up energy demand models. The selection is therefore consistent with the activity drivers of FORECAST . According to FORECAST, a differentiation is made by cross-sectoral and sector-specific drivers; these are primarily:

  • cross-sectoral: population and gross domestic product (GDP)
  • sector-specific: gross value added (GVA), employees, living area, number of electric vehicles, disposable income, industry locations and capacities, hospital beds, students, etc. 

Furthermore, the climate conditions are captured by the climate factor that comprises the combined impact of weather-caused effects on energy demand. The data collection is primarily based on public sources such as the so-called ‚Regionendatenbank‘ and ‚Gemeindeverzeichnis-Informationssystem‘ of the Federal Bureau of Statistics (DESTATIS), statistical offices of the federal states, German Meteorological Service (DWD), Federal Motor Transport Authority (KBA), Federal Office of Economics and Export Control (BAFA) and commercial sources such as the ene’t database. The data are provided in different levels of granularity for districts (NUTS 3), municipalities (LAU 2) or postal code areas. There are particular challenges in the assumptions needed for the distribution of NUTS 3 data to a LAU 2 level. To use the parameters of the Regional Database for an ex ante analysis, the data needs to be available for the entire horizon of analysis. The projection of data into the future in the Regional Database is primarily based on the so-called ‘Raumordnungsprognose 2035’ of the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR), which particularly focuses on the questions of high spatially resolved structural changes. The ‘Raumordnungsprognose 2035’ provides data for the following parameters: population (by age groups), employees (by age groups and sex) and households (by household size) on a district level for the period 1990-2035. As these parameters are projected at a district level, they already include the net effect of interregional structural change. The other parameters captured in the Regional Database, which are not included in the ‘Raumordnungsprognose 2035’, are extrapolated either by specific indicators (e.g. GDP per capita) or by correlation analysis (e.g. there is a strong correlation between GVA and the number of employees or between the GDP per capita and disposable income).

hourly electricity system load curves

Overview

The eLOAD (electricity LOad curve ADjustment) model aims to estimate the long-term evolution of electricity system load curves on a national level. Based on appliance specific hourly load profiles and annual demand projections from the FORECAST model eLOAD assesses the transformation of the load curve due to structural changes on the demand side and the introduction of new appliances. Analysing the future shape of the load curve gives insights into the development of peak load, load levels and load ramp rates that are required for investment decisions about new electricity generation capacity and grid infrastructure. Apart from that, eLOAD allows to analyse load flexibility, i.e. demand response (DR). Based on a mixed-integer optimisation the model determines cost-optimal load shifting activities of suitable appliances such as electric vehicles or storage heaters.

Figure: Structure of the FORECAST and eLOAD couplingzoom
Figure: Structure of the FORECAST and eLOAD coupling

Model structure

eLOAD aims to estimate the future shape of the national electricity system load curves. It is available for all countries of the EU27 until the year 2050. eLOAD consists of two modules. The first module addresses the deformation of the load curve due to structural changes on the demand side and the introduction of new appliances (such as electric vehicles) by applying a partial decomposition approach. The technology specific annual demand projection from the FORECAST model serves for the identification of all “relevant appliances” that feature a significant increase or decrease in electricity consumption over the projection horizon. By using appliance specific load profiles, a load curve can be generated for all relevant appliances for the base year, according to the respective annual demand in the base year. These load curves are deduced from the system load curve of the base year. The resulting remaining load curve and the appliance specific load curves are then scaled for all projection years according to the demand evolution. Reassembling the scaled remaining load and the scaled load curves give the load curve of the projection year.

By using this approach of partial decomposition, characteristic outliers and irregularities can be conserved in the load curve while the general shape of the load curve is adjusted according to the changes on the demand side. Figure 1 shows an exemplary four-day extract of the German load curve for the years 2008 and 2050.

Figure 1: Four-day extract of the German electricity load curve in the year 2008 and 2050
Figure 1: Four-day extract of the German electricity load curve in the year 2008 and 2050

The second module of eLOAD addresses the active adjustment of the load curve by means of demand response activities. Based on the load curve of appliances that are suitable for DR and taking into account techno-economic parameters and restrictions of the appliances, a mixed-integer optimization is carried out which determines the least-cost scheduling of the appliances in order to smooth the net load (as the difference of the system load and the generation of renewable energy sources).

Database

The database includes four types of data: hourly load profiles, historic load curves and a temperature time series as well as appliance specific demand response parameters.

The load profile data base comprises more than 500 hourly load profiles from various types of industrial, commercial and residential appliances or processes.   The load profiles are available either for the length of an entire year (8760 hours) or as average profiles for typical days (distinguished by weekday, season and in the case of heating and cooling technologies by temperature). The data originate from various national and international surveys and field tests. Some national profiles are transferred to other countries by means of time use surveys.

Historic load curves are provided by Entso-e, temperature time series come from the MERRA data base of the NASA. 

The demand response parameters, required for the optimization part of eLOAD, include information on the availability and restrictions of appliances suitable for demand response activities as well as system information e.g. on the tarification scheme.

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