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Analysis of the role of energy storages in Germany with TIMES PanEU – methodology and results

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Analysis of the role of energy storages in Germany with TIMES PanEU – methodology and results

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Analysis of the role of energy storages in Germany with TIMES PanEU – methodology and results

  1. 1. IER Analysis of the role of energy storages in Germany with TIMES PanEU – methodology and results ETSAP Workshop Madrid Julia Welsch, Markus Blesl Julia Welsch
  2. 2. 17.11.2016 2 Outline Introduction Methodology 1 2 3 4 Scenario analysis Conclusions and outlook
  3. 3. 17.11.2016 3 Outline Introduction Methodology 1 2 3 4 Scenario analysis Conclusions and outlook
  4. 4. Motivation: • Political induced increase of share of renewables • Increasingly feed in of electricity from variable renewables (wind and pv)  Occurance of a strongly fluctuating negative residual load  Increased need for flexibility options to balance supply and demand of electricity Objective: • Methodological improvements of the energy system model TIMES PanEU regarding to the temporal resolution and the modelling of energy storages • Determination of the optimal configuration of energy storages and power-to-x under minimizing the overall system cost • Including ESTMAP database for storages • Analysis of the role of electricity storages within the ESTMAP project 17.11.2016 4 Introduction
  5. 5. Energy system model TIMES PanEU: • Linear optimization model • 30 regions (EU-28, NO, CH) • Horizon: 2010 to 2050 • Whole energy system, from energy supply to energy service demand 17.11.2016 5 TIMES PanEU Model improvement for modelling storages: • Creation of methodologial extensions for modelling and analysis of energy storages • Increase of the temporal resolution for Germany • The high temporal resolution is based on representative, coherent and successive time segments • Modelling of energy storages and power-to-x for Germany and Europe in TIMES PanEUResearch focus: Integrated consideration of options in the sectors of electricity, heat and mobility over the optimization period taking into account sector coupling through the use of Power-to-Heat, Power-to- Gas and electric mobility Supply of district heat and electricity Power-to-Heat Power-to-Gas Electricity Storage Heat Storage Gas Storage Supply of energy sources Industry Autoproducer Commercial Autoproducer Households Battery Night Storage Power-to-Heat Heat Storage Transport Electric mobility Energyservicedemand Energysourceprices, ressourceavailability Agriculture Demand Other energy conversion
  6. 6. 17.11.2016 6 Outline Introduction Methodology 1 2 3 4 Scenario analysis Conclusions and outlook
  7. 7. 17.11.2016 7 Increase of the temporal resolution for Germany Methodology: Temporal resolution in TIMES PanEU RD R RP RN Milestoneyear S F W SD SP SN FD FP FN WD WP WN Season Weekly Annual Daynite RS101 R … RS156 Milestoneyear S F W SS101 … SS156 FS101 … FS156 WS101 … WS156FP FPS101 … FPS156  Coupling of timeslice trees for modelling trade  Integral optimization over all regions and modelling periods Germany: • 280 time segments: • One typical week per season with three- hourly-resolution (224 time segments) • One additional week for representing high feed of variable renewable energies (56 time segments) • Representative, coherent times segments TIMES PanEU • 12 time segments per year (one typical day per season with three time steps) • No coherent temporal resolution
  8. 8. 17.11.2016 8 Demand structure exemplary for households Methodology: Temporal resolution in TIMES PanEU 0 10 20 30 Refrigeration Washing machine Cloth drying Dishwasher Cooking Lighting Other Gas Cooker Electric Hot Water Elektric Heating Electric Cooker … 0 5 Demand for Hot Water 0 5 10 Demand for Space Heat 0 10 20 Demand for Cooking Demand for electricity of households GW  Electricity demand and electricity load profile are results of the optimization and change depending on the used application technologies
  9. 9. Sector couplingElectricity production 17.11.2016 9 Methodology: Flexibility options in TIMES PanEU Power-to-Gas Electric vehicles H2, Biogas Electricity storage Pump storage Diabatic CAES Adiabatic CAES Stationary battery storage Grid Heat market Power-to-Heat Mobility Mobile battery storage Gas Power-to-Gas Flexibility options in the electricty sector DSM Curtailment Changed electricity demand Conventional power plants Coal Gas Oil Nuclear Renewable power plants Wind PV Geothermal Solarthermal Biomas Hydro CHP renewable/ conventional Grid Power-to-Heat Electricity trade between countries Changed electricity demand in Europe Heat storage Natural gas storage Hydrogen storage Gas grid as storage
  10. 10. 17.11.2016 10 Modelling of storages as a sequence of three processes Methodology: Modelling of energy storages in TIMES PanEU  Storage content and storage power are endogenous optimziation results Input Process Capacity: Power (GW) Storage process Capacity: Energy amount (PJ) Output process Capacity: Power (GW)
  11. 11. 17.11.2016 11 Interface ESTMAP storage database – TIMES PanEU/TIMES regional models EXCEL database analysis input deck Creation of Pivot Tables in EXCEL Integration of data in TIMES Database and GIS mapping Technical/economical characterization Potentials
  12. 12. 17.11.2016 12 Interface TIMES regional models – PowerFys TIMES Energy system modelling PowerFys Electrcity market modelling • Capacities of power and CHP plants • Electricity and heat production of power and CHP plants • Fuel input in power and CHP plants • Emissions • Electricity trade (capacities and amounts) • Capacities of electrcity storages • Electrcity demand by sector
  13. 13. 17.11.2016 13 Outline Introduction Methodology 1 2 3 4 Scenario analysis Conclusions and outlook
  14. 14. 17.11.2016 14 Scenario definition Scenario analysis from the ESTMAP project Scenario assumptions 2030 2050 Base MorePV Battery Cost Base MorePV Battery Cost GHG reduction in the EU (vs. 1990) 40 % 90 % Renewables at electricity consumption in the EU 30 % 80 % Renewables at gross final energy consumption in the EU 27 % 75 % PV Capacity in Europe 120 GW 180 GW 120 GW 364 GW 546 GW 364 GW PV Capacity in Germany 58 GW 87 GW 58 GW 70 GW 105 GW 70 GW
  15. 15. 17.11.2016 15 Electricity production by energy carrier in Germany Scenario analysis from the ESTMAP project -100 0 100 200 300 400 500 600 700 Statsitics Base Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost 2010 2015 2020 2025 2030 2035 2040 2045 2050 Electricity storage (excl. pump storage) Net Imports Others / Waste non- ren. Other Renewables Biomass / Waste ren. Solar Wind offshore Wind onshore Hydro incl. pump storage Nuclear Gas CCS Gas w/o CCS Oil Lignite CCS Lignite w/o CCS Coal CCS Coal w/o CCS Net electricity TWh • Electrification of the energy system until the year 2050 (achieve GHG emission targets) • Electrification enables integration of variable renewable energies • Other energy carriers can be substituted • With nuclear phase-out in the year 2025 wind turbines are increasingly used to achieve the share of renewables • From the year 2035 additional lignite CCS power plants enter the market (reduction of GHG emissions)
  16. 16. 17.11.2016 16 Capacities in Germany Scenario analysis from the ESTMAP project 0 50 100 150 200 250 300 350 Statsitics Base Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost 2010 2015 2020 2025 2030 2035 2040 2045 2050 Electricity storage (excl. pump storage) Others / Waste non-ren. Other Renewables Biomass / Waste ren. Solar Wind Hydro incl. pump storage Nuclear Natural gas Oil Lignite Coal Capacity GW • Increase of the capacities is higher than the increase of the overall electricity production • Lower full load hours of pv and wind plants
  17. 17. 17.11.2016 17 Electricity storages in Germany Scenario analysis from the ESTMAP project 0 5 10 15 20 25 Statistics Base Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost 2010 2015 2020 2025 2030 2035 2040 2045 2050 Battery stationary Redox Flow Battery stationary Lead Acid Battery stationary Lithium Ion CAES adiabatic cavern CAES adiabatic tank CAES diabatic cavern Pump Storage Storage input capacity GW 0 5 10 15 20 25 30 Statistics Base Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost 20102015 2020 2025 2030 2035 2040 2045 2050 Battery stationary Redox Flow Battery stationary Lead Acid Battery stationary Lithium Ion CAES adiabatic cavern CAES adiabatic tank CAES diabatic cavern Pump Storage Storage output capacity GW • The increased fluctuating supply of electricity from wind and pv systems leads to a higher flexibility need in the whole energy system • Therefore new electricity storages (especially stationary lithium ion batteries in combination with pv) are built from the year 2045 onwards in Germany
  18. 18. 17.11.2016 18 Electricity storages in Germany Scenario analysis from the ESTMAP project 0 30 60 90 120 150 180 Statistics Base Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost Base MorePV BatteryCost 20102015 2020 2025 2030 2035 2040 2045 2050 Battery stationary Redox Flow Battery stationary Lead Acid Battery stationary Lithium Ion CAES adiabatic cavern CAES adiabatic tank CAES diabatic cavern Pump Storage Storage content GWh It can be seen, that the ratio of storage content and power is lower for the battery storage than for the pump storage. This is due to the lower content-specific investment costs with higher power-specific investments of pumped storage.
  19. 19. 0 20 40 60 80 100 120 140 160 180 200 2010 2015 2020 2025 2030 2035 2040 2045 2050 Stored electricity Electricity electric cars Increased electricity demand Decreased electricity demand Load shifting in time segment with negative residual load Curtailment in time segments with negative residual load Net export in time segments with negative residual load Electricity Power-to-Gas in time segments with negative residual load Electricity Power-to-Heat in time segments with negative residual load 17.11.2016 19 Flexibilisation of electricity in Germany for the MorePV scenario Scenario analysis from the ESTMAP project TWh Flexibilisation • In order to achieve a high share of renewables in electricity consumption in Germany, less curtailment is used in 2050 • The amount of electricity which is not curtailed is instead stored in electricity storage or used by Power- to-Heat or Power-to-Gas installations (sector coupling)
  20. 20. 17.11.2016 20 Outline Introduction Methodology: Temporal resolution and modelling of storages in TIMES PanEU 1 2 3 4 Scenario analysis Conclusions and outlook
  21. 21. 17.11.2016 21 Conclusions and outlook Conclusions: • Comparing the results for Germany to the ones from the TIMES PanEU model it can be seen, that a big part of new energy storages in Europe are built in Germany. • This results are not only attributable to the amount of potential storage sites from the ESTMAP database, but also due to the model design with a higher temporal resolution only of the German region. • With a lower temporal resolution the storage need in the energy system is generally underestimated. The high flexibility need, which results from peaks and valleys of PV and wind production, cannot be mapped adequately in a model with a temporal resolution of only a few typical days. • New storages after 2040, other flexibility options are important model components Outlook: • Outcomes of energy system analysis depend on storage technology parameters, such as cost projections, so that a sensitivity analysis could be useful • Higher temporal resolution for other countries – model handling
  22. 22. Thank you! E-Mail Phone +49 (0) 711 685- Fax +49 (0) 711 685- University of Stuttgart Heßbrühlstraße 49a 70565 Stuttgart Germany Julia Welsch 87848 87873 Institute for Energy Economics and the Rational Use of Energy (IER) julia.welsch@ier.uni-stuttgart.de

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