Outline. Intro Methodology Background energy system Foreground system Conclusion. DTU Management Engineering, Technical University of Denmark

Transcription

1 Optimal affaldsbehandling i fremtidens energisystem Marie Münster, Amalia Pizarro, Raffaele Salvucci, Hans Ravn Senior Researcher System Analysis, DTU Management Engineering Dansk Fjernvarme Stormøde i erfa-gruppen for affaldsvarme Odense, 26/1 2016

2 Outline Intro Methodology Background energy system Foreground system Conclusion 2

3 Strategic research project Markeder Økonomi Teknik Miljø Affaldsmængder Partners AAU(DK) DTU(DK) SDU(DK) IVL(SE) KTH(SE) Lund Uni(SE) Yale Uni(US) Regulering Organisation RAM-løse edb Holmboe Consult ARC Reno-Nord

4 Research questions Main purpose: to contribute to improved use of waste for energy or material recycling integrating economic and environmental considerations including resource scarcity. Important questions : How much waste will we have in the future? WP3 What is the future optimal treatment of waste fractions in terms of economy and the environment: recycling or energy production? WP2, WP 4 og WP5 Which regulatory and organizational measures may support the achievement of political goals under different market conditions? WP1 4

5 Incineration Landfill Composting and AD Recycling 5

6 GHG emissions 6

7 Useof waste Waste use 5% of electricity production 21% of DH production Fuel consumption for DH production Waste (organic) Waste (non organic) Electricity Renewables Waste (non organic) Coal Natural gas Oil 7

8 Waste for energy 2050 FRIDA model (economic development and trends) Govt. recycling target -50% of householdwaste - Separate targets e.g. 37% of OFMSW source segregated

9 No wastein 2050? Fuel Consumption (PJ/year) No Waste vs. Waste Scenarios (PJ/year) MSW Boiler MSW Backpressure Wood Pellets Extraction Wood Pellets Boiler Wood Extraction Wood Backpressure WIND Surplus Heat Natural Gas Extraction Natural Gas Condensing Natural Gas CCS Extraction Natural Gas Boiler Electricity to Heat Electricity Storage Coal CCS Extraction

10 TOPWASTE WPs Economic growth Waste statistics WP3 FRIDA WP2+5 TopWaste database and KISS tool Local complex energy marginals Tech s: Climate gas emissions, costs, efficiencies Fractions: Global prices, global emission marginals GHG emissions Costs Land use change Recycling rates Tech s: Climate gas emissions, costs, efficiencies Fractions: Global prices, global emission marginals WP4 OPTI WASTE Locally optimal waste treatment Local effects on energy system Investments and operation Total system costs and emissions Energy system: Energy demands Non WtE technologies Energy resource potentials Energy prices (and taxes)

11 Energy System Analysis(ESA) and waste LCA Collection Transport Treatment Recovery Disposal Technology 1 X X X X X Technology 2 X X X X X Technology 3 X Technology n X

12 Background Scenarios 12

13 Background Scenarios 2035 & 2050 Material marginals Marginal Biomass supply Combustible waste trade Energy scenarios Global electricity marginal Marginals depend on geography 13 Energy marginals also depend on flexibility

14 Foreground Scenarios Collection schemes Baseline (+ Paper kerbside + Bio(SF/MF)) 4-chamber bin (2x4/1x4 + Bio) (Sweden/Herlev) Dual-stream (+ Bio(SF/MF)) (Southern Jutland) Recyclables bin (+ Bio) (Germany) Central sorting schemes Central sorting with wet anaerobic digestion Central sorting with dry anaerobic digestion Central sorting with biodrying 14

15 Sorting scenarios (Ciprian Cimpan, SDU) 15

16 Separate collection levels, material recovery and treatment in the region of Funen 16

17 Avoided primary 17

18 CO2eq emissions 18

19 Sorting scenarios (Central and Bio) Carbon footprint results (relaxed energy targets and progressive biomass marginal): (a)reference system: residual WtEvs. central sorting; (b)(b) reference system: no vs. separate collection of biowaste; 19

20 Sorting scenarios (Dual stream) 20

21 Waste and energy system analysis FUEL Money Waste ELECTRICITY Management CONSUMPTION Energy System Greenhouse Gas emissions System DISTRICT HEATING CONSUMPTION Nutrients & Resources Nordic power system (Balmorel) - Long term electricity prices and DK heat capacities Danish heating system (OptiWaste) - WtE competes with other heating plants(chp, boiler, HP's) - Large scale WtE and biorefineries in large areas - Biogas plants and electrolysers in small/medium areas 21

22 Waste treatment options (OptiWaste) Fixed source seggregation of organic waste for co-digestion Biogas for Ngas gridor CHP No import or exportof wasteallowed Household waste Residual waste for MRF or incineration(chp (L/M/S) or boiler) MRF => materialrecycling, RDF (CHP or boiler), dry AD + incineration FreemovementwithinDK Industrial waste Residual for RDF or incineration(chps or boilers) 22

23 Models Linear programming(gams) Socio-economic cost optimisation(investments and operation) Open source Balmorel energy system model Nordic countries and Germany (Electricity regions and DH areas) Hourly time variations (demands and fluctuating production(e.g. wind)) Input: Demands, productiontech's, costs(fuel, tech's), Output: Investments and operation (production, storage, transmission) Output for OptiWaste: electricityprices, capacities(and mixed long term marginals) OptiWaste waste management (WM) model Denmark (66 areas) (heat and waste transport) Hourly time variations (electricity prices) and weekly(waste and heat) Input: Waste amounts, WM tech's, costs& prices, transport distances Output: Investments and operation (production, storage, transport) 23

24 OptiWaste Areas 33 urban 33 rural 24

25 Background data and results (Balmorel) 25

26 Electricity transmission 26

27 Import (positive) and export (negative) to the Nordic power system during two weeks with high wind penetration in autumn for Timesimuleringer med Balmorel 27

28 Future Scenarios DKWind Flex H2 DKWind Unflex H2 DKBio+ Bioref DK DKBio+ Bioref-DK SE, NO, FI (CNBS), DE (Ref) SE, NO, FI (CNES), DE (Ref) Flexible H2 production 2050 Wind UnflexibleH2 production Biorefineries placedin DK 2050 Bio Biofuels imported CNBS: Carbon Neutral Biomass Scenario CNES: Carbon Neutral Electricity Scenario DKWind: Danish Wind Scenario DKBio+: Danish Biomass + Scenario 28

29 Recreating the scenarios Copying the electricity (flex -no flex) and district heating demand (optimising electricity demand for heat pumps) Copied constant heat production from bio-refineries in large areas Limited expansion of wind (up to the level of the DEA scenarios) At least net self sufficient with electricity production Only existing and planned transmission capacity with availability of 95% 29

30 Fuel Consumption in the Danish electricity and DH system in

31 Net Electricity Demand for Denmark PJ Year Classic Flexible Bio+ Flexible Wind 31

32 Net Electricity Demand for Norway, Sweden and Finland CNBS and CNES scenarios Classic (PJ) Transport (PJ) Year Norway Sweden Finland Finland CNBS Finland CNES Norway CNBS Norway CNES Sweden CNBS Sweden CNES - 32

33 33

34 34

35 35

36 36

37 Electricity demand 37

38 Electricity price 38

39 Electricity from wind 39

40 Electricity production 40

41 Heat price 2012/MWh in winter time 41

42 Foreground data and results (OptiWaste) 42

43 WtE capacity Incineration capacity Capacity (ton/h) Boiler CHP Small CHP Medium CHP Large 50 0 Biomass Biorefineries in DK Biomass Biorefineries outside DK Wind Flexible H2 Wind Non-flexible H2 43

44 Costs EUR/t 44

45 Transport costs(eur/t) 45

46 Transport (kt*km/year) 46

47 Results- Feasibility of planned incineration over-capacity 10,000 Waste Import ( 1,000 tons/year) 8,000 6,000 4,000 2,000 Wind '35 Biomass '35 Low District Heating prices and fluctuating electricity prices High District Heating prices and less fluctuating electricity prices Import Gate Fee (excluding shipping cost)

48 Conclusion Waste heat from the transport sectormayplayan importantrolein the future The mainuseof wasteis alwaysin large scalechp plants It is more feasibleto have small and medium scaleincinerationin the biomass scenarios When transportation costs are included, more small scale incineration plants become feasible Decreased costs of MRF+RDF plants or increased prices for recyclables (as well as decreased source seggregation) may increase central sorting and useof RDF Import may be socio-economically feasible- but environmentally? 48

49 Thank you for your attention! Questions/ comments? 49