The Role of Concentrating Solar Power and Photovoltaics ... - CiteSeerX
The Role of Concentrating Solar Power and Photovoltaics for Climate Protection R. Pietzcker, S. Manger, N. Bauer, G. Luderer, T. Bruckner 10th IAEE European Conference Vienna, 9.9.2009
Contents • • • • •
Solar power technologies The integrated assessment model ReMIND-G Parameterization of solar technologies: Costs and Potentials Scenarios and Results Conclusion
2
Photovoltaics (PV) • • • • • •
Direct and diffuse sunlight is directly transformed into electricity Existing capacities (2005): 5GW Costs have strongly decreased „learning technology“ Relatively mature: Poly- and monocrystalline silicon cells New technologies: thin films, organic PV Freely scalable: 1W..100MW
3
Concentrating Solar Power (CSP) • • • •
Simple idea: Direct sunlight → heat → power Existing capacities (2005): trough 400MW, tower 30MW Large-scale power plant Advantage: heat is easily storable, in contrast to electricity from PV
4
Remind-G Hybrid energy-economy-climate model
5
ReMIND-G: Storage Model assumptions:
• Fluctuations parameterized for three time scales (daily, weekly, seasonal). Flow batteries* and H2-electrolysis* are used for daily and weekly fluctuations. • Storage requirements depend on the share of generation by a single fluctuating technology: • Exemplary average cost penalties in 2075 due to investments into storage capacities and storage losses for PV: without storage needs 760$/kWp
with storage needs 1130$/kWp
+50% * Parameterization based on Chen et al. 2009.
6
Remind-G: Technological Learning Learning curves for different energy technologies:
ReMIND uses learning curves with floorcosts Source: IEA (2000): Experience Curves for Energy Technology Policy; p. 21
7
Parameterization of PV & CSP I Learning curves for Solar technologies*:
* Based on the results of various other studies, either bottom-up cost projections or based on learning curves
8
Parameterization of PV & CSP II Techno-economic characteristics in ReMIND-G:
9
Solar Potential CSP potential from satellite data with GIS filters
Source: Trieb et al., 2009
10
Solar Potential Regional solar potential:
Global solar potential used in ReMIND:
Regional potentials calculated from values in Trieb et al., 2009
11
Scenarios & Results Basic (only PV, no CSP) BAU:
POL:
Solar (PV & CSP) BAU:
POL:
12
Option values: Solar Technologies Discounted* cumulated GDP differences between BAU and policy scenario as proxy for mitigation costs:
No Solar
Basic (no PV) CSP (no PV)
* A discount rate of 3% was used
Solar (PV&CSP)
13
Option values: other technologies Discounted* cumulated GDP differences between BAU and policy scenario as proxy for mitigation costs:
No Solar
Basic (no PV) CSP (no PV)
*A discount rate of 3% was used
Solar (PV&CSP)
14
Sensitivity Analysis I Share of generation* depending on the investment costs of CSP Share of power generation
PV
60
CSP Solar (CSP + PV)
% of power generation
50
40
30
20
10
0 7000
8000
9000
10000
11000
12000
13000
14000
Investment costs CSP [$2005/kW]
*Calculated from the cumulated electricity production from 2005 to 2100
15
Sensitivity Analysis II Share of generation* depending on the investment costs of CSP and the learning rate of PV
*Calculated from the cumulated electricity production from 2005 to 2100
16
Conclusion • Solar electricity can play a major role in the decarbonization of the energy system • PV and CSP compete for investments and partially replace each other • CSP is used until investment costs are increased by more than 45% • Further research with a more regionalized model is necessary to analyze the importance of local potential limitations and grid integration costs
17
Thank you for your attention!
18
Thank you for your attention! References: Chen, H., Cong, T.N., Yang, W., Tan, C, Li, Y. and Ding, Y.: “Progress in Electrical Energy Storage Systems: A Critical Review”. Progress in Natural Science, 19, 291312, 2009. IEA (2000): Experience Curves for Energy Technology Policy; p. 21 Nuclear Energy Agency: “Uranium 2003 - Resources, Production and Demand”. 2003. Tzscheutschler, P.:”Globales technisches Potenzial solarthermischer Stromerzeugung”. Energie Management Verlagsgesellschaft mbH, IFE, 48 , 2005. Trieb, F., Schillings, C., O’Sullivan, M., Pregger, T. and Hoyer-Klick, C. (2009): “Global Potential of Concentrating Solar Power”. SolarPaces Conference Berlin, September 2009. Cost projections for CSP and PV: EU PV Technology Platform: “A Strategic Research Agenda for Photovoltaic Solar Energy Technology”. European Communities, 2007. Frankl, P., Menichetti, E. and Raugei, M.: “Final Report on Technical Data, Costs and Life Cycle Inventories of PV Applications”. Deliverable n° 11.2 - RS 1a of the NEEDS (New Energy Externalities Developments for Sustainability) project, 2005. Ginley, D., Green, M. A. and Collins, R: ”Solar Energy Conversion Towards 1 Terawatt”. MRS Bulletin. 33, 355-372, 2008. International Energy Agency: ”Energy Technology Perspectives 2008 – Scenarios & Strategies to 2050”. 2008. Johansson, T. B., McCormick, K., Neij, L. and Turkenburg, W.: “The Potential of Renewable Energy”. International Conference for Renewable Energies, Bonn, 2004. Junginger, M., Lako, P. Lensink, S., van Sark, W. and Weiss, M.: ”Technological Learning in The Energy Sector”. ECN, University Utrecht, 2008. Keshner, M. and Arya, R.: ”Study of Potential Cost Reductions Resulting from Super-Large-Scale Manufacturing of PV Modules”. National Renewable Energy Laboratory, 2004. Leimbach, M., Bauer, N., Baumstark L. and Edenhofer, O.: ”Mitigation Costs in a Globalized World: Climate Policy Analysis with ReMIND-R”. Accepted for Publication in Environmental Modeling and Assessment, 2009. Neij, L., Borup, M., Blesl, M. and Mayer-Spohn, O: ”Cost Development—an Analysis Based on Experience Curves”. Deliverable 3.3—RS1A of the NEEDS (New Energy Externalities Development for Sustainability) project, 2006. Neji, L.: ”Cost Development of Future Technologies for Power Generation – A Study Based on Experience Curves and Complementary Bottom-Up Assessments”. Energy Policy, 36(6), 2200-2211, 2008. Nemet, G.F.: ”Interim Monitoring of Cost Dynamics for Publicy Supported Energy Technologies”. Energy Policy, 37, 825-835, 2009. PV-Track: ”A Vision for Photovoltaic Technology”. European comission, 2005 Sargent&Lundy LLC Consulting Group Chicago: ”1. Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts”. National Renewable Energy Laboratory, 2003. Schaeffer, G.J., Seebregts, A.J., Beurskens, L.W.M., de Moor, H.H.C., Alsema, E.A., Sark, W., Durstewicz, M., Perrin, M., Boulanger, P., Laukamp, H. and Zuccaro, C.: “Learning from the Sun”. Final Report of the PHOTEX Project. Report ECN DEGO: ECN-C–04-035, ECN Renewable Energy in the Built Environment. 2004. Trieb, F.: ”ATHENE - Ausbau thermischer Solarkraftwerke für eine nachhaltige Energieversorgung“. Working Package 1.3 of the DLR Project „SOKRATES (Solarthermische Kraftwerkstechnologie für den Schutz des Erdklimas)“, 2004. Viebahn, P., Kronshage, S., Trieb, F. and Lechon, Y.: ”Final Report on Technical Data, Costs, and Life Cycle Inventories of Solar Thermal Power Plants”.
19
Backup
20
ReMIND-G: The energy technologies Overview of the conversion routes:
21
ReMIND-G: Renewable energies Techno-economic characteristics of technologies:
Hydro
Lifetime
Investment costs
Floor costs
Learning Rate
Cumulative capacity 2005
O&M costs
years
$US/kW
$US/kW
%
GW
$US/GJ
95
3000
4.23
3000
4.2
Geo HDR Wind onshore
40
1200
883
12
60
Wind offshore
40
2200
1372
8
1
SPV
40
4900
600
20
5
0.89
2.33
Source: Neij (2003), Nitsch et al. (2004), IEA (2008), Junginger et al. (2008), Lemming et al. (2008).
22
ReMIND-G: Storage I Storage • The fluctuating energy type (wind on/offshore, PV, CSP) is parameterized by 3 kinds of fluctuations with different amplitudes and frequencies •
Each kind of fluctuation is serviced by a storage technology day-night fluctuation week-long fluctuation seasonal variation
Redox-Flow-Batteries H2 with turbine build more capacities
23
ReMIND-G: Storage II Storage requirements depend on share of generation
24
Resulting average investment costs
25
Resulting average investment costs
26
Effect of storage Model assumptions: • Resulting average cost penalties due to investment into storage capacities and storage losses : PV: Wind: PV: Wind:
in 2050 830$/kWp 950-1150$/kWp 890$/kWp 950-1100$/kWp in 2075 760$/kWp 900-1100$/kWp 890$/kWp 930-1070$/kWp
Fossil fuel values based on literature data ranging from reasonably assured to speculative resources. Uranium based on NEA 2003
27
Remind-G: Technological Learning Learning curves for different energy technologies:
& Cumulated Capacity # !! Cost = Initial Cost * $$ Initial Capacity % "
learnindex
Source: IEA (2000): Experience Curves for Energy Technology Policy; p. 21
28
Remind-G: Technological Learning Common one-factor learning curve
& cumulated capacity # !! costs = initial costs * $$ % initial capacity "
learnindex
ReMIND-G uses learning curves with floor costs:
& cumulated capacity # !! costs = initial reducable costs * $$ % initial capacity "
learnindex
+ floorcosts
29
ReMIND-G: Extraction Costs Extraction cost curves for fossil fuels and uranium
Fossil fuel values based on literature data ranging from reasonably assured to speculative resources. Uranium based on NEA 2003
30
Scenarios & Results Basic Scenario (only PV): BAU:
Policy:
31
Scenarios & Results Solar Scenario (PV+CSP): BAU:
Policy:
32
Solar Potential Global solar potential* used in ReMIND:
Calculated from Trieb et al., 2009 and Tscheutschler, 2005
33
Contents • • • • •
Solar power technologies The integrated assessment model ReMIND-G Parameterization of solar technologies: Costs and Potentials Scenarios and Results Conclusion
2
Photovoltaics (PV) • • • • • •
Direct and diffuse sunlight is directly transformed into electricity Existing capacities (2005): 5GW Costs have strongly decreased „learning technology“ Relatively mature: Poly- and monocrystalline silicon cells New technologies: thin films, organic PV Freely scalable: 1W..100MW
3
Concentrating Solar Power (CSP) • • • •
Simple idea: Direct sunlight → heat → power Existing capacities (2005): trough 400MW, tower 30MW Large-scale power plant Advantage: heat is easily storable, in contrast to electricity from PV
4
Remind-G Hybrid energy-economy-climate model
5
ReMIND-G: Storage Model assumptions:
• Fluctuations parameterized for three time scales (daily, weekly, seasonal). Flow batteries* and H2-electrolysis* are used for daily and weekly fluctuations. • Storage requirements depend on the share of generation by a single fluctuating technology: • Exemplary average cost penalties in 2075 due to investments into storage capacities and storage losses for PV: without storage needs 760$/kWp
with storage needs 1130$/kWp
+50% * Parameterization based on Chen et al. 2009.
6
Remind-G: Technological Learning Learning curves for different energy technologies:
ReMIND uses learning curves with floorcosts Source: IEA (2000): Experience Curves for Energy Technology Policy; p. 21
7
Parameterization of PV & CSP I Learning curves for Solar technologies*:
* Based on the results of various other studies, either bottom-up cost projections or based on learning curves
8
Parameterization of PV & CSP II Techno-economic characteristics in ReMIND-G:
9
Solar Potential CSP potential from satellite data with GIS filters
Source: Trieb et al., 2009
10
Solar Potential Regional solar potential:
Global solar potential used in ReMIND:
Regional potentials calculated from values in Trieb et al., 2009
11
Scenarios & Results Basic (only PV, no CSP) BAU:
POL:
Solar (PV & CSP) BAU:
POL:
12
Option values: Solar Technologies Discounted* cumulated GDP differences between BAU and policy scenario as proxy for mitigation costs:
No Solar
Basic (no PV) CSP (no PV)
* A discount rate of 3% was used
Solar (PV&CSP)
13
Option values: other technologies Discounted* cumulated GDP differences between BAU and policy scenario as proxy for mitigation costs:
No Solar
Basic (no PV) CSP (no PV)
*A discount rate of 3% was used
Solar (PV&CSP)
14
Sensitivity Analysis I Share of generation* depending on the investment costs of CSP Share of power generation
PV
60
CSP Solar (CSP + PV)
% of power generation
50
40
30
20
10
0 7000
8000
9000
10000
11000
12000
13000
14000
Investment costs CSP [$2005/kW]
*Calculated from the cumulated electricity production from 2005 to 2100
15
Sensitivity Analysis II Share of generation* depending on the investment costs of CSP and the learning rate of PV
*Calculated from the cumulated electricity production from 2005 to 2100
16
Conclusion • Solar electricity can play a major role in the decarbonization of the energy system • PV and CSP compete for investments and partially replace each other • CSP is used until investment costs are increased by more than 45% • Further research with a more regionalized model is necessary to analyze the importance of local potential limitations and grid integration costs
17
Thank you for your attention!
18
Thank you for your attention! References: Chen, H., Cong, T.N., Yang, W., Tan, C, Li, Y. and Ding, Y.: “Progress in Electrical Energy Storage Systems: A Critical Review”. Progress in Natural Science, 19, 291312, 2009. IEA (2000): Experience Curves for Energy Technology Policy; p. 21 Nuclear Energy Agency: “Uranium 2003 - Resources, Production and Demand”. 2003. Tzscheutschler, P.:”Globales technisches Potenzial solarthermischer Stromerzeugung”. Energie Management Verlagsgesellschaft mbH, IFE, 48 , 2005. Trieb, F., Schillings, C., O’Sullivan, M., Pregger, T. and Hoyer-Klick, C. (2009): “Global Potential of Concentrating Solar Power”. SolarPaces Conference Berlin, September 2009. Cost projections for CSP and PV: EU PV Technology Platform: “A Strategic Research Agenda for Photovoltaic Solar Energy Technology”. European Communities, 2007. Frankl, P., Menichetti, E. and Raugei, M.: “Final Report on Technical Data, Costs and Life Cycle Inventories of PV Applications”. Deliverable n° 11.2 - RS 1a of the NEEDS (New Energy Externalities Developments for Sustainability) project, 2005. Ginley, D., Green, M. A. and Collins, R: ”Solar Energy Conversion Towards 1 Terawatt”. MRS Bulletin. 33, 355-372, 2008. International Energy Agency: ”Energy Technology Perspectives 2008 – Scenarios & Strategies to 2050”. 2008. Johansson, T. B., McCormick, K., Neij, L. and Turkenburg, W.: “The Potential of Renewable Energy”. International Conference for Renewable Energies, Bonn, 2004. Junginger, M., Lako, P. Lensink, S., van Sark, W. and Weiss, M.: ”Technological Learning in The Energy Sector”. ECN, University Utrecht, 2008. Keshner, M. and Arya, R.: ”Study of Potential Cost Reductions Resulting from Super-Large-Scale Manufacturing of PV Modules”. National Renewable Energy Laboratory, 2004. Leimbach, M., Bauer, N., Baumstark L. and Edenhofer, O.: ”Mitigation Costs in a Globalized World: Climate Policy Analysis with ReMIND-R”. Accepted for Publication in Environmental Modeling and Assessment, 2009. Neij, L., Borup, M., Blesl, M. and Mayer-Spohn, O: ”Cost Development—an Analysis Based on Experience Curves”. Deliverable 3.3—RS1A of the NEEDS (New Energy Externalities Development for Sustainability) project, 2006. Neji, L.: ”Cost Development of Future Technologies for Power Generation – A Study Based on Experience Curves and Complementary Bottom-Up Assessments”. Energy Policy, 36(6), 2200-2211, 2008. Nemet, G.F.: ”Interim Monitoring of Cost Dynamics for Publicy Supported Energy Technologies”. Energy Policy, 37, 825-835, 2009. PV-Track: ”A Vision for Photovoltaic Technology”. European comission, 2005 Sargent&Lundy LLC Consulting Group Chicago: ”1. Assessment of Parabolic Trough and Power Tower Solar Technology Cost and Performance Forecasts”. National Renewable Energy Laboratory, 2003. Schaeffer, G.J., Seebregts, A.J., Beurskens, L.W.M., de Moor, H.H.C., Alsema, E.A., Sark, W., Durstewicz, M., Perrin, M., Boulanger, P., Laukamp, H. and Zuccaro, C.: “Learning from the Sun”. Final Report of the PHOTEX Project. Report ECN DEGO: ECN-C–04-035, ECN Renewable Energy in the Built Environment. 2004. Trieb, F.: ”ATHENE - Ausbau thermischer Solarkraftwerke für eine nachhaltige Energieversorgung“. Working Package 1.3 of the DLR Project „SOKRATES (Solarthermische Kraftwerkstechnologie für den Schutz des Erdklimas)“, 2004. Viebahn, P., Kronshage, S., Trieb, F. and Lechon, Y.: ”Final Report on Technical Data, Costs, and Life Cycle Inventories of Solar Thermal Power Plants”.
19
Backup
20
ReMIND-G: The energy technologies Overview of the conversion routes:
21
ReMIND-G: Renewable energies Techno-economic characteristics of technologies:
Hydro
Lifetime
Investment costs
Floor costs
Learning Rate
Cumulative capacity 2005
O&M costs
years
$US/kW
$US/kW
%
GW
$US/GJ
95
3000
4.23
3000
4.2
Geo HDR Wind onshore
40
1200
883
12
60
Wind offshore
40
2200
1372
8
1
SPV
40
4900
600
20
5
0.89
2.33
Source: Neij (2003), Nitsch et al. (2004), IEA (2008), Junginger et al. (2008), Lemming et al. (2008).
22
ReMIND-G: Storage I Storage • The fluctuating energy type (wind on/offshore, PV, CSP) is parameterized by 3 kinds of fluctuations with different amplitudes and frequencies •
Each kind of fluctuation is serviced by a storage technology day-night fluctuation week-long fluctuation seasonal variation
Redox-Flow-Batteries H2 with turbine build more capacities
23
ReMIND-G: Storage II Storage requirements depend on share of generation
24
Resulting average investment costs
25
Resulting average investment costs
26
Effect of storage Model assumptions: • Resulting average cost penalties due to investment into storage capacities and storage losses : PV: Wind: PV: Wind:
in 2050 830$/kWp 950-1150$/kWp 890$/kWp 950-1100$/kWp in 2075 760$/kWp 900-1100$/kWp 890$/kWp 930-1070$/kWp
Fossil fuel values based on literature data ranging from reasonably assured to speculative resources. Uranium based on NEA 2003
27
Remind-G: Technological Learning Learning curves for different energy technologies:
& Cumulated Capacity # !! Cost = Initial Cost * $$ Initial Capacity % "
learnindex
Source: IEA (2000): Experience Curves for Energy Technology Policy; p. 21
28
Remind-G: Technological Learning Common one-factor learning curve
& cumulated capacity # !! costs = initial costs * $$ % initial capacity "
learnindex
ReMIND-G uses learning curves with floor costs:
& cumulated capacity # !! costs = initial reducable costs * $$ % initial capacity "
learnindex
+ floorcosts
29
ReMIND-G: Extraction Costs Extraction cost curves for fossil fuels and uranium
Fossil fuel values based on literature data ranging from reasonably assured to speculative resources. Uranium based on NEA 2003
30
Scenarios & Results Basic Scenario (only PV): BAU:
Policy:
31
Scenarios & Results Solar Scenario (PV+CSP): BAU:
Policy:
32
Solar Potential Global solar potential* used in ReMIND:
Calculated from Trieb et al., 2009 and Tscheutschler, 2005
33
