Lecture: Prospective Environmental Assessments

Transcription

1 Lecture: Prospective Environmental Assessments Upscaling and Learning Stefanie Hellweg

2 Statement of the problem Key question: environmental impact /kwh electricity? 20xx Technical and technology developments: Different sizes, changes of materials, design and production changes, supply chain changes, acceptancy, regulations, etc General Life Cycle Assessment aspects: Usually only few LCA studies per size, data not harmonized, no method to include technical & technology developments in LCA Prospective Environmental Assessment: Upscaling and Learning

3 What can change? Increase output capacity Increase in efficiency Change in utilities Supplier changes Maturization of used technology Modified legal requirements Process optimization Technical development By-product markets Innovation Change of background systems Prospective Environmental Assessment: Upscaling and Learning

4 Definitions Scaling: changes resulting from an increased output Learning: Process of acquiring modifications in existing knowledge, skills, habits, or tendencies (Britannica Concise Encyclopedia) Experience effects are defined as a combination of learning and scaling mechanisms Prospective Environmental Assessment: Upscaling and Learning

5 Scaling Prospective Environmental Assessment: Upscaling and Learning

6 Scaling: estimation Prospective Environmental Assessment: Upscaling and Learning

7 Cost scaling: examples Prospective Environmental Assessment: Upscaling and Learning

8 Cost scaling: example of coal combustion Prospective Environmental Assessment: Upscaling and Learning

9 Types of scaling Upsizing (up-scaling): increasing the size of an individual product, for instance upsizing a small engine to a large engine. Economies-of-scale: increasing plant capacity to produce large quantities. Economies-of-scope: synergies because of production of different products in the same company (joint use of production facilities, marketing, administration; by-products) Prospective Environmental Assessment: Upscaling and Learning

10 Learning curve concept Concept idea: the time required to perform a task decreases as a worker gains experience time decreases when cumulative output doubles Wright (1936): Labor costs in airframe manufacturing decline at a constant percentage with every doubling of cumulative production Prospective Environmental Assessment: Upscaling and Learning

11 Types of learning Learning-by-Searching: learning by invention, research and development (R&D) and demonstration on a laboratory or pilot plant scale. Learning-by-Doing: learning during volume production, based on the total cumulative production. Learning-by-Using: learning after the product is introduced to the market, based on for instance user feedback. Learning-by-Interacting: learning during the diffusion of the technology for instance through a network between academia, industry etc. Prospective Environmental Assessment: Upscaling and Learning

12 Prospective Environmental Assessment: Upscaling and Learning

13 Experience curve experience index BCG: Boston Consulting Group Prospective Environmental Assessment: Upscaling and Learning

14 Electricity generation ( ) Prospective Environmental Assessment: Upscaling and Learning

15 Experience curve for PV modules International Renewable Energy Agency, RENEWABLE ENERGY TECHNOLOGIES: COST ANALYSIS SERIES, downloaded Prospective Environmental Assessment: Upscaling and Learning

16 Technology structural change Prospective Environmental Assessment: Upscaling and Learning

17 Break-even PV Prospective Environmental Assessment: Upscaling and Learning

18 Experience not included Prospective Environmental Assessment: Upscaling and Learning

19 Experience included Prospective Environmental Assessment: Upscaling and Learning

20 Experience curve concept (costs) - achievements Integration of curves into energy models has made it easier to integrate technology change into energy-system analysis and scenario planning Illustrate the approximate rate of cost reduction for different types of energy technologies Curves illustrate the need for an initial market in order to cut costs Prospective Environmental Assessment: Upscaling and Learning

21 Experience curve concept (costs) - drawbacks Driving forces of the cost reductions are not known aggregated approach Empirical learning curves may masks underlying dynamics Limited usefulness for extrapolations Prospective Environmental Assessment: Upscaling and Learning

22 Environmental scaling and learning Efficiency changes Less material per product Higher performances Product life time Use of by-products Waste scenarios Changes in background systems Prospective Environmental Assessment: Upscaling and Learning

23 Methods for environmental scaling 1. Modelling based on empirical data 2. Engineering based quantifications 3. Environmental Impact Growth Laws (EIGL) Prospective Environmental Assessment: Upscaling and Learning

24 Methods for environmental scaling 1. Determining experience effects: Empirically fitting regression lines large dataset required no distinction between scaling and learning easy modelling: logy = loga + b logx; ordinary least-squares regression (OLS) 2. Engineering based models 3. Similarities: knowledge about physical relationships theoretical scaling; L α A 1/2 α V 1/3 α M 1/3 ; e.g. swept area of rotor blades A = ¼ π D 2 only size, upper boundary for experience effects between different products between different disciplines such as economics Harmonization of goal & scope definitions necessary Parameterization of life cycle inventory parameters Calculation of life cycle assessment impacts Interpretation Prospective Environmental Assessment: Upscaling and Learning

25 Experience curve Y = a X b Y 2 = Y 1 (X 2 /X 1 ) b logy 2 = logy 1 + b log(x 2 /X 1 ) b: experience index X: parameter defining size Y: dependent parameter PR = 2 b LR = 1 PR progress rate learning rate - Dependent parameter: e.g. costs, LCI parameters, environmental impact - For energy production systems Y: cumulative power production - Commonly for a technology or sector and geographic location Prospective Environmental Assessment: Upscaling and Learning

26 Size scaling Y = a X b Y 2 = Y 1 (X 2 /X 1 ) b logy 2 = logy 1 + b log(x 2 /X 1 ) b: scaling factor X: parameter defining size Y: size-dependent parameter - Size-dependent parameter: e.g. costs, LCI parameters, environmental impact - For energy production systems Y: power output - Individual product level Prospective Environmental Assessment: Upscaling and Learning

27 Mass M (kg) Mass M (kg) Mass M (kg) Scaling: example of heat pumps mass versus power 1000 a) Brine/water heat pumps (M versus P) 100 R² = 0.77 n= b) Air/water heat pumps (M versus P) 1000 c) Water/water heat pumps (M versus P) R² = R² = Power P (kw) Power P (kw) Prospective Environmental Assessment: Upscaling and Learning

28 Refrigerant RF (kg) Scaling: example of heat pumps refrigerant use 10 1 Brine/water Air/water Water/water Pot.(Brine/water) Pot.(Air/water) Pot.(Water/water) 0, Power P (kw) Caduff M et al.., Scaling Relationships in Life Cycle Assessment: The Case of Heat Production from Biomass and Heat Pumps. Journal of Industrial Ecology 18 (3), , Prospective Environmental Assessment: Upscaling and Learning

29 COP (-) Scaling: example of heat pumps coefficient of performance (COP) 6 5,5 5 4,5 4 Brine/water Air/water Water/water 3,5 3 2, Power P (kw) Caduff M et al.., Scaling Relationships in Life Cycle Assessment: The Case of Heat Production from Biomass and Heat Pumps. Journal of Industrial Ecology 18 (3), , Prospective Environmental Assessment: Upscaling and Learning

30 Scaling: example of heat pumps - GWP Brine/water heat pump Air/water heat pump Water/water heat pump ( ) total impact (---) input materials ( ) manufacturing and disposal (- -) transport (- ) refrigerant Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

31 Scaling: example of heat pumps - GWP Brine/water heat pump Air/water heat pump Water/water heat pump ( ) total impact (---) energy input ( ) refrigerant ( ) heat pump production (- ) bore hole (- -) transport Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

32 Scaling: example of heat pumps - ODP Brine/water heat pump Air/water heat pump Water/water heat pump ( ) total impact (---) energy input ( ) refrigerant ( ) heat pump (- ) bore hole Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

33 Scaling: example of biomass furnaces n= ,000 d) Biomass log furnace (M versus P) 100,000 e) Biomass pellet furnace (M versus P) 10,000 10,000 1,000,100 R² = ,000,100 R² = 0.95, , Power P (kw) Power P (kw) Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

34 Efficiency (%) Efficiency (%) Scaling: example of biomass furnaces a) Biomass log furnaces: Efficiency versus power Power P (kw) b) Biomass pellet furnaces: Efficiency versus power Power P (kw) Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

35 Electricity Pel (kwh) Electricity Pel (kwh) Scaling: example of biomass furnaces a) Biomass log furnaces: electricity consumption versus power 1 0,1 0, Power P (kw) 1 b) Biomass pellte furnaces: electricity consumption versus power 0,1 0, Power P (kw) Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

36 Scaling: example of biomass furnaces - GWP Biomass log furnace Biomass pellet furnace ( ) total impact (---) input materials ( ) manufacturing and disposal (- -) transport (- ) refrigerant Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

37 Scaling: example of biomass furnaces - GWP Biomass log furnace Biomass pellet furnace total biomass input total biomass input Caduff M et al.., Prospective Environmental Assessment: Upscaling and Learning

38 Example of wind turbines Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Koehler, A.; Hellweg, S., Wind Power Electricity: the bigger the turbine, the greener the electricity? Environmental Science & Technology, 2012, 46(9), Prospective Environmental Assessment: Upscaling and Learning

39 Example of wind turbines engineering based scaling relationships Parameter proportional to Power, P D 2 h 3/7 M rotor D 3 M nacelle D 3 M tower D 2 h M foundation D 3 M electronics&cables EI production h M components EI use D 2 h 3/7 EI disposal M components Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

40 Example of wind turbines empirical data Rated power*, P [kw] Tower height, h [m] Rotor diameter, D [m] Construction year of turbine Calculated captured power at rotor, P captured, [kw] n/a n/a max Calculated energy generation, P cal [MWh/a] assuming a standard site with 5 m/s wind speed at 10 m height; wind shear gradient of 1/7 Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

41 Example of wind turbines empirical relationships Relationship * log a (95% CI) b (95% CI) R 2 n M total D 2 h 3/ ( ) 0.76 ( ) M rotor D 0.30 ( ) 2.22 ( ) M nacelle D 0.64 ( ) 2.19 ( ) M tower D 1.70 ( ) 1.82 ( ) M tower D 2 h 1.34 ( ) 0.68 ( ) M foundation D 1.44 ( ) 1.58 ( ) M electronics&cables h 2.88 ( ) 0.32 ( ) Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

42 Example of wind turbines empirical data (mass versus rotor diameter) Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

43 LCA of wind turbines System boundaries: Resource extraction, material manufacturing and processing, production of the elements (nacelle, rotor, turbine, foundation, cables inside the turbine, cables to the grid, and the electronic control box), transport, turbine maintenance and disposal; Assembly of the turbine and the energy for decommissioning of the turbine were not included. Electricity produced was calculated for a standard site Material masses were linked to material inventories from ecoinvent Standard transport distances assumed for materials, foundation, operating materials (lucricating oil) Cables length was parametrized according to hub height plus a size independent distance to the grid of 1000 m for all cases. LCIA: midpoint indicators from ReCiPe Prospective Environmental Assessment: Upscaling and Learning

44 Example of wind turbines LCIA impact category unit log a (95% CI) b (95% CI) R 2 climate change kg CO 2 eq/kwh ( ) ( ) freshwater kg 1,4-DB ecotoxicity eq/kwh ( ) ( ) urban land m 2 a/kwh occupation ( ) ( ) metal depletion kg Fe eq/kwh ( ) ( ) 0.83 Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

45 Example of wind turbines GWP/kWh Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

46 Example of wind turbines (scaling and learning) GWP/rotor Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

47 Example of wind turbines Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

48 Example of wind turbines Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

49 Results overview engines, heat pumps, furnaces, turbines: M = apb Equipment b (95% CI) Gasoline engine 0.77 ( ) Diesel engine 0.64 ( ) Marine engine 1.23 ( ) Generator 0.68 ( ) Steam boiler 0.87 ( ) Brine-water heat pump 0.60 ( ) Air-water heat pump 0.67 ( ) Water-water heat pump 0.55 ( ) Log furnace 0.66 ( ) Pellet furnace 0.78 ( ) Wind turbine 0.76 ( ) Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

50 Synthesis Results: GWP = apb Equipment b (95% CI) cradle-to-gate kg CO 2 /unit b (95% CI) cradle-to-grave kg CO 2 /kwh Brine-water heat pump 0.61 ( ) ( ) Air-water heat pump 0.82 ( ) ( ) Water-water heat pump 0.73 ( ) ( ) Log furnace 0.66 ( ) ( ) Pellet furnace 0.78 ( ) ( ) Wind turbine 0.78 ( ) ( ) 0.73 ( ) ( ) Caduff M et al Prospective Environmental Assessment: Upscaling and Learning

51 Environmental impact / output What about the very early stage? Laboratory & pilot plant scale Commercial scale 5,0 5,0 4,0 3,0 4,0 3,0 Y = 3.67X R² = ,0 2,0 1,0 0,0 Layout 1 Layout 2 Layout 3 Layout 4 1,0 0,0 0,0 10,0 20,0 30,0 Cumulative production Prospective Environmental Assessment: Upscaling and Learning

52 Conclusions Environmental impacts per FU do not remain constant; they often display a non-linear scaling pattern which can be modeled as a power law, y = a x b Learning: concerned with cumulative production over time not the manufacture of a single product/batch at a particular moment in time To enable a fair comparison of technologies at different development stages, effects of learning and scaling should be considered. Prospective Environmental Assessment: Upscaling and Learning

53 Limitations of empirical experience curves Harmonization of published datasets can be difficult and time-intensive Large datasets not always available Black box approach Modelling of entire production chain Extrapolation to other technologies, size ranges debatable Linking effects during laboratory and pilot plant scale to effect during volume production represents a challenge Prospective Environmental Assessment: Upscaling and Learning

54 Recommendations for future study Modelling of further products, sectors and ranges to allow modelling of entire supply chain More research on environmental experience effects of laboratory and/or pilot plant scale size to volume production Division of environmental experience effects into scaling and learning Prospective Environmental Assessment: Upscaling and Learning

55 Thank you to Marloes Caduff for providing an initial set of slides, on which the current lecture is based on (adapted and updated version). Further reading Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Koehler, A.; Hellweg, S., Wind Power Electricity: the bigger the turbine, the greener the electricity? Environmental Science & Technology, 2012, 46(9), Caduff, M.; Huijbregts, M. A. J.; Althaus, H.-J.; Hendriks, A. J., Power-Law Relationships for Estimating Mass, Fuel Consumption and Costs of Energy Conversion Equipments. Environmental Science & Technology, 2011, 45(2), Hendriks, A. J.; Schipper, A.; Caduff, M.; Huijbregts, M. A. J., Size relationships of water inflow into lakes: Empirical regressions suggest geometric scaling. Journal of Hydrology, 2012, , Caduff, M.; Koehler, A.; Huijbregts, M. A. J.; Althaus, H.-J.; Hellweg, S., Scaling Relationships in Life Cycle Assessment: The Case of Heat Production from Biomass and Heat Pumps. Journal of Industrial Ecology 18 (3), , Prospective Environmental Assessment: Upscaling and Learning