Energy and environmental impacts of biomass use in the residential Sector: a case-study for Italy

543 views

Published on

Energy and environmental impacts of biomass use in the residential Sector: a case-study for
Italy

Published in: Data & Analytics
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
543
On SlideShare
0
From Embeds
0
Number of Embeds
123
Actions
Shares
0
Downloads
12
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Energy and environmental impacts of biomass use in the residential Sector: a case-study for Italy

  1. 1. Energy and environmental impacts of biomass use in the residential sector: a case-study for Italy Maria Rosa Virdis, Maria Gaeta, Umberto Ciorba and Ilaria D’Elia ENEA ETSAP Workshop - Cork, 30 May 2016
  2. 2. 2 Overview UC-Studi e Strategie • The policy context • Objective of the analysis • Methodology • Energy scenarios • Emission scenarios • Pollutant concentration maps to 2030 • Concluding remarks
  3. 3. The policy context • The EU “20-20-20” policy Package in 2009 sets targets to 2020, requiring: • Transposition of Directive 2009/28/EC on renewables. • the preparation of National Action Plans for Renewable Energy (NAP) and for Energy Efficiency (EEAP). • the development of strategies and measures to support renewable sources. • In Italy the 2013 National Energy Strategy to 2020 identifies renewable energy sources (RES) among priorities for action, setting more ambitious targets than the ones in the NAP for the power sector and promoting thermal renewables. • 19-20% RES share in gross final consumption by 2020 (>17%). • Part of this contribution to be supplied by thermal renewables (about 11 Mtoe/year) including, besides solar thermal and heat pumps, also biomass used in boilers, stoves and closed fireplaces. • In residential heating, substitution of biomass (conventionally considered carbon neutral) for fossil fuels is encouraged by incentives. 3 UC-Studi e Strategie
  4. 4. • In Italy biomass consumption for residential heating much larger than thought. o The ISTAT survey on Households energy consumption (2014), estimates for 2013 the amount of biomass used in the residential sector at about 19 Mt (of which 17.5 Mt wood and 1.5 Mt pellets). About half of it escapes formal market circuits. • Air pollutant concentrations (including PM) are still too high in some parts of Italy, despite adoption of fairly stringent national and European regulations on the emissions of industrial plants and transport vehicles. • The impact of air pollutant concentrations on the diffusion of respiratory diseases in humans is widely acknowledged in many epidemiological studies at global level (WHO). • The VIIAS project results estimate fine particulates to be responsible in Italy for about 30 000 premature deaths each year. • Wood biomass and pellets use receives a more favorable fiscal treatment in Italy (no excise tax, 10% VAT for wood). Some facts UC-Studi e Strategie 4
  5. 5. PM2.5 concentration map in 2010* Air quality: present situation 2010 UC-Studi e Strategie • Sensitive areas particularly in Po Valley, Lazio e Campania * ENEA estimates with GAINS_IT (20kmx20km), based on PM 2.5 emissions in 2010 5
  6. 6. • EU and national energy policies pursue objectives of energy dependence reduction, energy efficiency and climate change mitigation. • Energy related GHG abatement policies encouraging the use of renewable sources often have positive synergies w.r. to energy dependence reduction, mitigation of environmental impacts and development of innovative sectors. • Such synergies cannot be taken for granted and need to be analyzed with integrated tools • This study aimed at assessing the impacts on the energy system and on the environment in terms of CO2 emissions and air quality of new energy and decarbonization policies in the residential sector in Italy. • The focus was on policies that support thermal uses of biomass in buildings. UC-Studi e Strategie Aims of the study 6
  7. 7. 7 Methodological Approach NEB Energy Mix RES Technologies CO2 * www.etsap.org; Gaeta, M., Baldissara, B., 2011. Il modello energetico Times-Italia: struttura e dati versione 2010. **www.minni.org; D’Elia, I., Peschi, E., 2013. Lo scenario emissivo nazionale nella negoziazione internazionale PM 2.5 SO2 NOx NMVOC PM 2.5 NO2 ENERGY SCENARIO EMISSION SCENARIO Energy activity Scenario by techs GDP Fuel price Resource Potential Emission Inventory Non-energy activity Control Strategy TIMES_IT* GAINS_IT** OUTPUT The analysis was carried out using two models linked together: • The TIMES-Italy energy system model, for energy scenarios • The IAM GAINS-Italy, estimating the emission trajectories of SO2, NOx, NMVOC, PM2.5, and corresponding pollutant concentration maps with a spatial resolution of 20 kmx20 km.
  8. 8. TIMES e GAINS are both technology models, using different classification systems but the same activity sectors. A link between TIMES and GAINS is created via Excel. To connect TIME and GAINS need to establish a precise mapping between the various sectors (technologies, fuels…) of power generation and end-use (transport, residential – services, industry, agriculture) in each of the two models as well as a correspondence between sectors in GAINS and emission inventories in order to verify emissions for a base year. Important: calibrate the models using official sources both for energy and emissions data in the same base year. This harmonization process is a very important step to guarantee model validation and projection robustness. Model integration The TIMES-GAINS linkage
  9. 9. Power generation example: natural gas in TIMES-Italy and Gains-Italy TECHNOLOGIES in GAINS-Italy Boiler (PP_EX_OTH) Turbine (PP_NEW) Ciclo combinato(PP_MOD) Tecnologia Turbina a gas < 80 MW con vapore Turbina a gas < 300 MW Ciclo combinato (turbogas-2006) < 3000 MW Turbina a gas cogenerativa Centrale a ciclo combinato cogenerativo spill Ciclo con turbina a vapore in contropressione cogenerativo Ciclo con turbina a vapore con spillam. e cond. cogenerativo Motori a combustione interna industria Turbina a gas ciclo semplice Industria Turbine a vapore Industria Motori a comb. interna Res Motori a comb. Interna Terziario Microturbine Cog Residenziale Microturbine Cog Terziario Ciclo combinato Cogenerazione Residenziale Ciclo combinato Cog Terziario Motore Stirling Res TECHNOLOGIES in TIMES-Italy Establish a correspondence between technologies in the two models, cross- checking with official energy data (National Energy Balances, EUROSTAT, Terna) and energy consumption used in the emissions inventory. The TIMES-GAINS linkage
  10. 10. DATI IN Mtep YEAR 2010 TIMES-Italia INV ISPRA 2010 BEN 2010 Carbone HC 11.47 9.00 9.40 Derivati Carbone DC 0.00 0.00 0.00 Biomassa OS1 2.22 1.10 1.77 Rifiuti OS2 1.62 1.57 1.42 Olio comb HF 1.71 3.45 3.52 Gasolio MD 0.57 0.14 0.14 Benzina GSL 0.16 0.36 0.36 GPL LPG 0.00 0.00 0.00 Gas GAS 24.44 26.10 25.89 42.19 41.73 42.51 Harmonization of energy consumption data Comparison for the same base year (2010) across energy consumption estimates produced by the TIMES-Italy model, the NEB, the national emissions inventory, aggregated according to the classification of consumption used by GAINS-Italy. Example of fuel consumption in thermal power-plants – year 2010 (Survey data for INV e BEN, scenario results for TIMES-Italy) Convergence to the same value Different classification of consumption data causes significant discrepancies in emission terms. Comparison of different data sources allowed a readjustment of the output from the TIMES- Italy model and a of convergence of consumption data. The TIMES-GAINS linkage
  11. 11. 11 11 The TIMES-GAINS linkage: power sector UC-Studi TIMES GAINS
  12. 12. 12 UC-Studi e Strategie Three scenarios were considered for this analysis: • Reference Scenario (RIF) projects the evolution of the energy system based on current legislation and present demographic, technological and economic trends. Assumes compliance with 2020 European energy and climate targets, constraints in the ETS sectors, and with the National Energy Strategy (NES) goals to 2020. • Constant Biomass Scenario (BIOcost). designed to achieve the same CO2 emissions reduction as the Reference scenario but with a slightly different energy mix, where biomass consumption is no higher than the one estimated by the 2014 ISTAT survey on Households energy consumption (about 19 Mt of biomass). In all other respects the BIOcost scenario follows the path set by the National Energy Strategy . • Decarbonization Scenario (DEC) Represents the impact on the energy system of a 36% CO2 emissions reduction compared to 2005 (based on the Impact Assessment of the 2014 EC Communication “A policy framework for climate and energy in the period from 2020 up to 2030”). Energy scenario impacts
  13. 13. 13 UC-Studi e Strategie Energy scenario impacts Sectors contribution to CO2 emissions reduction (DEC vs RIF) The BIOcost scenario is constructed to meet the same emission levels as the RIF scenario 200 250 300 350 400 450 500 550 1990 1995 2000 2005 2010 2015 2020 2025 2030 MtCO2 RIF Transport sector Domestic & Tertiary Industry Power sector DEC 42% 12% 23% 23%
  14. 14. 14 UC-Studi e Strategie Energy scenario impacts Total Primary Energy Supply by energy source, Mtoe Renewables reach an overall share of TPES between 22% and 28% by 2030 0 20 40 60 80 100 120 140 160 180 200 storico storico RIF BIOcost DEC 2010 2013 2030 Mtoe Electr. net import Renewable Biomass & Waste Hydro Nuclear Natural Gas Oil prod. Solid fuel
  15. 15. 15 UC-Studi e Strategie Final consumption by sector, 2010-2030, Mtoe 0 20 40 60 80 100 120 140 Historical data RIF Biocost DEC 2010 2030 Mtoe Transport Tertiary Domestic Industry Energy scenario impacts
  16. 16. 16 UC-Studi e Strategie Residential and Tertiary sector Energy consumption by fuel in Residential and Tertiary sector*, 2010-2030 – Mtoe *Oil product consumption in agriculture is not included Year 2010: At the time of this study, revised consumption data for biomass was not available yet, hence residential sector energy consumption was estimated based on results of the ISTAT survey. 2010 storico 2013 RIF 2030 BIOcost 2030 DEC 2030 District Heating 0.20 0.85 0.56 0.59 0.49 RES 0.30 0.34 1.35 1.40 1.9 Biomass 3.40 6.63 7.10 6.40 6.77 LPG 2.03 1.70 0.90 0.94 0.8 Heating gas oil 2.33 1.81 0.22 0.24 0.28 Other oil prod. 0.11 0.06 0.01 0.02 0.01 Nat. Gas 27.5 25.5 23.3 23.4 19.4 Electricity 13.8 13.9 17.1 17.2 14.0 Coal 0.00 0.00 0.00 0.00 0.00 0.00 10.00 20.00 30.00 40.00 50.00 60.00 Mtoe
  17. 17. 17 UC-Studi e Strategie Control strategy GAINS-Italy generates corresponding emission scenarios for atmospheric pollutants (PM2.5, SO2, NOX, NMVOC). All emission scenarios in the residential-services sector assume a diffusion of abatement technologies at current legislation (CLE -Current LEgislation) as in the GAINS-It scenario elaborated for the revision of the Goteborg protocol. Domestic sector 2010 2015 2020 2025 2030 Fireplaces 63 63 63 63 63 Stoves 37 37 37 37 37 Technology % 2010 2015 2020 2025 2030 Open Fireplace 68 53 45 42 39 Improved Fireplace 32 47 55 58 61 Traditional stove 76 60 50 43 36 Improved stove 10 18 21 24 27 Pellet stove 14 22 29 33 37 Distribution of wood biomass technologies(%) CLE Control Strategy – wood biomass combustion (%) Source: ENEA, ISPRA Source: ENEA, ISPRA The CLE control strategy represents the set of abatement technologies measures that are expected to be introduced during the projection horizon. It is expressed in terms of market share of each control technology by sector, fuel and energy or production technology. Emission scenario impacts
  18. 18. 18 0.07 0.06 0.12 0.15 0.05 0.03 0.05 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 coal wood and similar m. waste residual oil kerosene natural gas LPG kg/GJ NOx 0.005 0.638 0.049 0.012 0.003 0.005 0.002 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 coal wood and similar m. waste residual oil kerosene natural gas LPG kg/GJ NMVOC 0.682 0.013 0.042 0.146 0.018 0 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 coal wood and similar m. waste residual oil kerosene natural gas LPG kg/GJ SO2 219.5 400.2 9.6 47 3.6 0.2 2 0 50 100 150 200 250 300 350 400 450 coal wood and similar m. waste residual oil kerosene natural gas LPG g/GJ PM2.5 Source: ISPRA http://www.sinanet.isprambiente.it/it/sia-ispra/serie-storiche-emissioni/fattori-di-emissione-per-le-sorgenti-di-combustione- stazionarie-in-italia/view Average Emission Factors by fuel– national inventory ISPRA Non Industrial Combustion Emission scenario impacts
  19. 19. 19 UC-Studi e Strategie PM 2.5 emissions by sector, SNAP, 2010 - 2030 2010 emission figures are estimated by the model, according to biomass consumption hypotheses in line with the 2014 ISTAT survey By 2030 the residential and services sector produces between 59% and 62% of total fine particulate emissions Emission scenario impacts 0 20 40 60 80 100 120 140 160 180 200 2010 estimated 2030 RIF 2030 Bio cost 2030 DEC kt/yearPM2.5 Agriculture Waste treatment and disposal Other mobile sources and machinery Road transport Extraction and distribution of fossil fuels and geothermal energy Production processes Combustion in manufacturing industry Non-industrial combustion plants Combustion in energy and transformation industries
  20. 20. 20 UC-Studi e Strategie kt PM2.5 2010 estimated RIF 2030 BIOcost 2030 DEC 2030 Coal 0.22 0.20 0.20 0.18 Natural gas 0.11 0.10 0.10 0.08 Other oil prod. 0.07 0.01 0.01 0.01 LPG 0.03 0.01 0.01 0.00 Wood Biomass 103.6 82.3 74.3 77.7 PM2.5 emission in residential and services sector, 2010-2030, kt More than 99% of PM 2.5 emissions in the residential and services sector originates in the combustion of wood biomass Emission scenario impacts
  21. 21. 21 UC-Studi e Strategie Concentration maps • To understand how the configuration of the future energy system influences the concentration levels of this pollutant, concentration maps with a resolution of 20kmx20km have been produced with the GAINS-Italy. • This was done by scaling down to regional level the input data on energy use. • This regionalization process has been updated using biomass consumption estimates by region from the 2014 ISTAT survey. • Concentration maps provide average annual emission values for the pollutant considered (without showing spikes or daily fluctuations) as a result of the interactions between pollutants from all energy and non-energy sources, as well as of the meteo- climatic conditions that influence locally pollutants dispersion in the atmosphere. • Concentration maps were computed for 2020 and 2030 assuming average meteorological year conditions.
  22. 22. 22 UC-Studi e Strategie 2030 RIF_CLE 2030 REF 2030 Biocost 2030 Biocost 2030 DEC • Scenarios examined show that total emissions of such pollutant as fine particulates fall over the horizon to 2030 due to the improvement of technologies deployed and to the introduction of pollution control measures mainly in the transport sector; however emission reductions are smaller whenever there is an increased use of wood biomass in residential heating. Concentration maps
  23. 23. Concentration maps and thresholds 2010 2020 2030 RIF Biocost Decarb RIF Biocost Decarb >5 μg/m3 100.0% 99.8% 99.6% 99.6% 99.5% 99.5% 99.5% >10 μg/m3 69.8% 62.9% 61.5% 61.4% 57.3% 54.5% 55.8% >15 μg/m3 31.2% 21.0% 19.1% 19.3% 15.5% 13.0% 14.9% >20 μg/m3 13.3% 8.0% 4.0% 5.8% 2.9% 1.4% 2.4% >25 μg/m3 6.0% 1.0% 0.6% 0.7% 0.3% 0.1% 0.2% Distribution of PM 2.5 concentrations in the map cells
  24. 24. 24 UC-Studi e Strategie Conclusions • Energy and climate policies can be made more robust by adopting a multidisciplinary and integrated approach in their design, so as to take into account also environmental and economic aspects. • Need an overall sustainability perspective, that pursues simultaneously climate change mitigation and air quality improvement, taking into account technological innovation aspects and a country’s overall competitiveness. • Importance of indirect incentives, like those for R&D and innovation on more efficient particulate abatement systems, or incentives aimed at a faster deployment of more efficient technologies both from the energy and the emission standpoint. • Attention to sensitive areas and local conditions, discouraging the use of fuels and technologies that can increase emission levels in areas already at risk for air quality.
  25. 25. Thank you! mariarosa.virdis@enea.it maria.gaeta@enea.it

×