Overview of the model structure

Transcription

1 Overview of the model structure MURE household is a simulation tool for the ex ante assessment of energy policy scenarios (or energy efficiency intervention scenarios) in the residential sector. As shown in the following the model is composed of two main modules: a bottom-up simulation tool for the assessment of the policy interventions in the building sector, including the calculation of the energy consumption of all the end uses directly or indirectly related to the building utilization: heating, sanitary hot water and lighting a stock model for the calculation of the main electricity end uses, like the large appliances, the IT devices and the air conditioning systems. Figure 1: Structure of the MURE residential tool Both modules are in turn composed of i) two or more input databases containing the main exogenous drivers and referring to the baseline scenario, and to one or more energy policy scenarios in accordance with the simulation exercise requirements and ii) a calculation and database management engine. The run results are provided in detailed output tables, one per each input database, in terms of unitary and total energy consumption. Two main categories of exogenous drivers can be distinguished in the input databases: the activity drivers, referring to the household demographic and socio-economic trends (i.e. the extensive components of the household energy consumption) and the energy-intensive and market drivers, referring to the energy efficiency performances of the buildings shell and the household thermal and electricity devices. With reference in particular to the building section of the model these drivers are:

2 The Activity Drivers Number of Households Dwelling surface Litres of water consumption for sanitary uses. The main Energy-intensive and Market Drivers Unitary heating demand per building type and retrofit level (kwh/m 2 ). Decimal energy efficiency of the main heating devices (i.e: boilers per type of fuel including biomass, heat pumps, distric heating/micro cogeneration) and rate of heating contribution from solar systems Decimal energy efficiency of the main SHW devices (i.e: electric and gas water heaters, heat pumps, SHW provided by central heating boilers) and rate of SHW contribution from solar systems Stock shares of the heating and SHW devices by technology/fuel type Specific energy consumption of lighting devices Lighting technology diffusion Important note: The building types are parameterized by size/shape and age and can be set in accordance with the available data. This provides a big flexibility to the tool as it can be used either at macroaggregated level or at micro level if the average dwelling surface, unitary consumption and the share of the heating and SHW devices by building type are known As the existing model has been used for macro analysis at country level, there have been set only two types of buildings: single family houses and multi-familiar houses in turn divided by existing (existing before 1975 and after 1975) and new (future) buildings. The Building Module This module provides the energy consumption trends of the heating, sanitary hot water (SHW) and lighting end uses. The calculation of the SHW and lighting consumption is rather straightforward. In the first case the tool calculates the SHW overall demand on the basis of the sanitary water consumption and then provides the final SHW energy consumption trend in accordance with a given hypothesis of the diffusion by country of the water heater technologies and the corresponding energy efficiencies. In the case of the lighting systems, the starting figures are represented by the lighting energy consumption, the lighting points per household and their share in terms of CFL and LEDS lamps versus incandescent lamps. The lighting energy consumption trend is then calculated in accordance with exogenous hypothesis of diffusion of the CFL (and LEDs) lighting points by scenario.

3 The heating energy consumption calculation is more structured. First of all, MURE calculates the building energy demand (or useful energy) on the basis of the following exogenous drivers: the building unitary energy consumption figures, broken down by building type and energy efficiency (EE) classes as shown in Figure 2 diffusion rates of these building s EE classes within their corresponding markets. Figure 2: Household sector: structure of the building stock Note: this figure shows the current model structure. It is possible to further broken down the building types in accordance with the project requirements. Figure two depicts the situation of the analyzed building stock in a given year of the scenario simulation. At the year 0 (reference year) the future building stock is 0 and the energy demand is provided only by the existing stock. In the following years it starts the calculation of the future building stock in accordance with the annual building stock grow rate (or number of new dwellings built and sold by year). At the same time the existing stock decreases in accordance with the building stock knocking down rate (or other dynamic concerning the existing stock evolution). At the year t the building energy demand is thus provided by the sum of the average unitary consumption time the dwelling surface per each building type. The average unitary consumption

4 in turn obtained by summing the product of each unitary consumption class (without refurbishment, refurbishment 0, etc.) time its percentage with respect the total number of dwelling per building type: unitary consumption without refurbishment x 90% + refurbishment 0 x 8% + refurbishment 1 x 2%. The building unitary energy consumption data and the dwelling surfaces are provided by the technical database and do not change over the scenario. The calculation of the energy demand by the scenario steps is provided by simulating the penetration of the different unitary consumption classes in accordance with the type of interventions carried out on the existing buildings and the dynamic of the new buildings insulation levels in accordance with the country standards. The final heating energy consumption trend is finally obtained by dividing, by each scenario step, the building demand (useful energy) so far calculated by the weighted averages of the energy efficiencies heating technologies used in the considered building stock. These weighted averages are in turn obtained by multiplying the heating technologies energy consumption shares by the corresponding decimal efficiencies The Stock Model In MURE a stock model is used to calculate the energy consumption of the more important household electricity end uses (apart from the lighting system, as outlined in the previous paragraph). The end uses taken into account in the stock model are: The main large appliances (cold and wash appliances plus driers) The IT appliances (television sets screens, desktops and laptops, modem and set-top boxes) The room air conditioners. The main extensive drivers are the number of households and the ownership rates of these appliances. The multiplication of the ownership rate multiplied with the household number gives the appliances stock. The ownership rate trend is calculated through a logistic function1 starting from the known (or estimated) data. Through these drivers plus the appliances' lifetime2 and the lifetime standard deviation, the model estimates the appliances' annual sales3. Other extensive drivers are the number of hours or numbers of times (i.e. the washing cycles) the considered appliances are used. Standby energy hours and power are also taken into account for the wash and the ITs appliances to calculate the corresponding standby energy consumption The main intensive drivers are the appliances unitary consumption (by year, by cycle, by hour). In the case of large appliances and the air conditioning, the unitary consumption is provided by energy labeling classes. In this case, the estimation of the trends of the energy labeling classes 1 In particular the Gompertz function was used. 2 The average lifetime is the duration when 50 % of the devices sold in a given year are no longer in the stock 3 In the stock model the annual sales are a function of the ownership rate, the household number and the remain function. This last function provides the share of the appliances sold in the year j that are still working in the year k. To calculate it, the model assumes the appliances' average lifetime as a normal distribution with average and standard deviation known.

5 shares are provided for each of these appliances in order to calculate the corresponding annual weighted average unitary consumption. The total appliances' energy consumption is finally calculated through a function that, for each year of the scenario span, opportunely sums up the consumption of the appliances sold in the previous years, starting from the year in which the ownership is deemed to be zero, and that are still working in the considered year, plus the consumption of the yearly sales. MURE Household, References General reference Adam Project: "Adaptation and Mitigation Strategies Supporting European Climate Policy", Deliverable M1.2 (Eberhard Jochem and Wolfgang Schade, FHG/ISI), May 2009 The PRIMES model (2011): E3MLab, National, Technical University of Athens EUROSTAT data (up to 2011) Large Appliances EuP Preparatory Studies "Domestic Refrigerators & Freezers" (Lot 13) Contractor ISIS, DG TREN Final Report December EuP Preparatory Studies "Domestic Washing Machines and Dishwahers" (Lot 14) Contractor ISIS, DG TREN Final Report December EuP Preparatory Studies "Laudry Dryers" (Lot 16) Contractor Price Waterhouse and Cooper, DG TREN Final Report March GfK data Televisions and ITs EuP Preparatory Studies "Personal Computers (desktop and laptops) and Computer Monitors" (Lot 3). IVF Industrial Research and Development Corporation for the European Commission DG TREN. Final Report. 27 August EuP Preparatory Studies "Televisions" (Lot 5). Contractor: Fraunhofer IZM, Berlin for the European Commission DG TREN. Final Report. Berlin, 2nd August Lighting Bertoldi, P. and Atanasiu, B. (2006): Residential Lighting Consumption and Saving Potential in the Enlarged EU. European Commission DG Joint Research Centre, Institute for Environment and Sustainability, Ispra. Proceedings of EDAL'06 Conference, London, UK, Heating - Buildings

6 Note: the data on the building energy efficiency was initially provided by the Wuppertal Institute in the framework of the "Study on the Energy Savings Potentials in EU Member States, Candidate Countries and EEA Countries", FHG/ISI and oth The papers used by Wuppertal for its contribution to the model has been: EURIMA, ECOFYS (2005a): Cost-effective Climate Protection in the Building Stock of the New EU Member States. Beyond the EU Energy Performance of Buildings Directive. European Insulation Manufacturers Association (EURIMA). Brussels, Cologne. EURIMA, ECOFYS (2005b): Cost-Effective Climate Protection in the EU Building Stock. European Insulation Manufacturers Association (EURIMA). Brussels, Cologne. Heating - heating devices René, Martijn van Elburg; William Li; Rob van Holsteijn (2007): Eco-design of Boilers. Task 3 Report. Consumer Behaviour and Local Infrastructure. Van Holsteijn en Kemna reported for EC. Delft.