MODELING THE INDUSTRY SECTOR FOR DECISION MAKING FOR MID TO LONG-TERM ENERGY EFFICIENCY PLANNING

Transcription

1 MODELING THE INDUSTRY SECTOR FOR DECISION MAKING FOR MID TO LONG-TERM ENERGY EFFICIENCY PLANNING Gilles GUERASSIMOFF 1, Gondia SECK 1, Ahcène DJEMAA 2, Alain HITA 2, Nadia MAÏZI 1 1 Centre for Applied Mathematics (CMA), Mines Paristech Chair Paristech Prospective Modeling for Sustainable Development 2 Electricité De France, EDF R&D Renardières IEW-ETSAP workshop June 18 th 23 nd 2012 Cape Town SOUTH AFRICA Centre for Applied Mathematics, Mines ParisTech EDF R&D Renardières

2 2 Overview of the presentation Context Model : MARKAL-TIMES for industry Approach for: Non-energy intensive industry (NEI) Energy intensive industry (EI) Specificities for each model Results Results for NEI Results for EI esu s o Conclusion

3 Context 3/21 The European Union sets a series of demanding climate and energy targets to be met by 2020 (20/20/20 EU scheme): Reduces its own emissions at least 20% below 1990 levels, Reduces energy consumption by 20% by improving energy efficiency, And increases the proportion of renewable resources at least 20% in the energy mix. Importance of Industrial sector in France Total final energy consumption Total CO 2 emissions

4 4/21 Modelling a detailed representation of the industry sector Assess the overall industry energy reduction potential Identify the path for new efficient technologies In mid term have a detailed model for France (EDF) Zoom on some key sectors (iron & steel, food and drink industry )

5 Model TIMES for industry 1/5 5/21 Different modeling approach in the case of industry due to the sectoral heterogeneity. Traditional Industry segmentation in homogeneous families for modeling Energy intensive industries (EI) (iron &steel, cement, sugar ) Non Energy intensive industries (NEI) (Food & Drink, electric &electronic instruments ) Determination of the characteristics of the frontier Limits considered in the industrial segmentation Share of energy costs in production value (2.5%) Energy intensity (7 GWh/M ) Energy consumed in production site (10 GWh/site)

6 Model TIMES for industry 2/5 6/21 Sectoral Approach for Non Energy Intensive industries (NEI) Modeling by Energy End-uses (Dry, Heat treatment ) (Generic model) Detailed TIMES model regarding subsectors Focusing only on Food & Drink industry (F&D) which encompasses 33 subsectors as a study case (Analysis of heat recovery by Heat pumps (HP) systems).

7 Model TIMES for industry 3/5 7/21 Sectoral Approach for Energy Intensive industries (EI) Modeling by processes Exchange of commodities between industries studied Grouping of country necessary Why detailed TIMES model for important energy intensive industries: To reduce energy consumption To reduce environmental impacts To improve exchange between Industries. Focusing on selected sectors Iron & Steel, Cement

8 Model TIMES for industry 4/5 8/21 Specificities of the model for NEI (Food & Drink industry) A time horizon from 2001 to 2020 due to smaller lifetime of technologies and higher structural effect Energy prices forecasting different from energy intensive industries A demand indicator which is suitable for NEIs is Value added (M ) Due to myriad of goods (no necessary physical links) and double counting Scenarios: Scenario BAU Business-as-usual Scenario HP: Considering the existence and a possible impact of HP in energy consumption from Results: Assess the possibilities of HP in terms of energy and CO 2 emissions. Tracking the important energy savings (in which subsector? In which energy end-use? In which h temperature range of heat consumption?) Reduced cost for heat pump penetration

9 Model TIMES for industry 5/5 9/21 Specificities of the model for EI (Iron & Steel industry) A time horizon from 2000 to 2050 Energy prices from other models (POLES) Demand forecast by sectoral experts Scenarios: Scenario BAU Business-as-usual Scenario Factor 4: Division of CO2 emissions by 4 Results: Global tendencies for the future energy mix. Assess the possibilities of future best technologies to impose reduction CO 2 emission by factor 4. Associated costs for these choices Global l vs idiid individual effort

10 Results for NEI analysis

11 Energy system evolution 11/21 Final Energy Consumption (in TWh) About 9,5 TWh of Energy Savings up to 2020 CO2 emissions (in Mt) 2.1 MtCO2 avoided up to Electricity Domestic Fuel Heavy Fuel Natural Gas LPG Coal Heat Others 2.5 CO2 reduction by HP introduction in F&D sector (MtCO2/yr) Percent reduction in total CO2 emissions in F&D sector (%) 25% % % 20% Reduction (MtCO2/y yr) % 16.6% 15% 10% Percent reduction (% %) % BAU Sc_HP %

12 Analysis by F&D subsector 12/ Energy savings (in TWh) Man. of tea & coffee Man. of alcoholic beverages Man. of wines and waters drink Man. of cocoa 3.5 Fish industry Man. of Bread & pastry goods Man. Of cider Man. of farinaceous prod. Man. of Grain mill products Man. of Oils & fats Man. of beer Man. of malt Man. of animals feed Man. of Fruits & vegetables 1 Man. of Sugar Man. of meat and poultrymeat Man. of meat products Man. of other food prod. Man. of Starches Man. of Dairy yproducts Rate of HP spread Man. of Dairy products 27.6% Man. of Starches 11.8% Man. of Fruits & vegetables 10% Man. of Sugar 9.2% Man. of other food prod. 9.0% Man. of meat products 7.7% Man. of meat and poultrymeat 5.0% Man. of animals feed 4.5% Man. of Oils & fats 2.3% Man. of malt 19% 1.9% Man. of cider 1.8% Man. of beer 1.7% Man. of cocoa 1.4% Man. of alcoholic beverages 1.3% Man. of farinaceous prod. 1.2% Man. of Grain mill products 1.0% Fish sector 0.8% Man. of Bread & pastry goods 0.7% Man. of tea & coffee 0.6% Man. of wines and waters drink 0.5%

13 Analysis by F&D subsector 13/21 Potential of energy efficiency in energy end-uses for and range temperature economically achieved for the most important subsectors Qua antity of heat reach hable by HP (TWh h) Drying Evaporation Concentration Heat of Liquids & Gas Thermal Treatment Mechanical Operations Cold production Building heating Chemical reactions Fusion Other Uses Quan ntity of heat (TWh) Heat dem mand C C C C C C C Température inconnue Penetration rate 57.0% Heat produce by HP Heat dem mand 30.3% 24.2% Heat produce by HP Heat dem mand Heat produce by HP Heat dem mand 44.4% Heat produce by HP Heat dem mand 38.1% Heat produce by HP Heat dem mand 50.0% Heat produce by HP Heat dem mand 36.9% Heat produce by HP 60% 50% 40% 30% 20% 10% 0% bsectoral heat dema and (%) HP prod duction relative to su Man. of Dairy products Man. of Starches Man. of Sugar Man. of Fruits Man. of Other Man. of meat & vegetables food prod. products Man. of animals feed

14 Results for EI

15 Energy intensive Industries 1/5 15/21 Context Model : MARKAL-TIMES for industry Results Conclusions Ressources Transformation Processes Consumptions Imp Biomasse Biomass Chaudière Boiler HTH BL Pulp Pâte & et Paper Papier FG Imp Coal Charbon Imp Oil Fioul Coal Oil Cogeneration Cogénération Turbine HTH ELC ELC Iron Sidérurgie & steel Ciment, Cement Chaux lime plaster Plâtre Demand Imp Nat. Gaz Gas Nat Natural Gas Tuiles, Tile, Brick, Briques, Céramique Ceramic Imp Electricity Électricité Electricity Glass Verre Imp RM MP Raw Materials Imp : Importation BL: Black Liquor FG: Furnace Gas

16 Energy intensive Industries 2/5 16/21 Scrap Coal Electricity Natural gas Heat Lime Coke oven gas Coke Iron ores Sintering Sinter Pellet Oxygen Nitrogen Blast furnace gas Pig iron Coke oven Blast Furnace Blast oxygen furnace gas Oxygen Liquid steel Pelletisation Oxygen Direct reduction iron Oxygen Converter Oxygen Nitrogen Electric Arc Furnace Secondary Metallurgy Secondary metallurgy liquid steel Continuos Casting Steel Hot Roll Hot rolling product Cold Rolling Cold rolling product Finishing operations Finished products

17 Energy intensive Industries 3/5 17/21 To avoid missing data: Grouping country to have better representation of residual capacity France as benchmark for boiler Environmental constraints are separated by country Valorization of scrap inside and between industries Hot Rolling. Steel Industry Cement Industry Continous Casting Blast Furnace Slag Cement import scrap Arc Furnace Scrap

18 Energy intensive Industries 4/5 18/21 Iron & steel and Cement structure changes under scenario factor 4 for CO 2 Cement (Clinker) production Mt Combustion System Improvement Dry process with CCS Preheater multi-stages Wet process Semi-dry process Dry process

19 Energy intensive Industries 5/5 19/21 Some results for France CO 2 abatement costs 2

20 Conclusions 20/21 Different way of modeling the structure (Process vs End-uses) A clear frontier is determined d between EI and NEI for modeling Different kind of demands, time horizon and energy prices Analysis of the results for different applications EI for long term technology and climate purpose while NEI for mid term and energy efficiency purpose Data very wide and difficult to collect for EU for both industries Lot of work to do to apply the NEI generic structure to all the other sectors

21 THANK YOU FOR YOUR ATTENTION IEW-ETSAP workshop June 18 th 23 nd 2012 Cape Town SOUTH AFRICA Centre for Applied Mathematics, Mines ParisTech