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Modeling long term interactions between energy vectors: a case study for gas and electricity systems in France

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Modeling long term interactions between energy vectors: a case study for gas and electricity systems in France

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Modeling long term interactions between energy vectors: a case study for gas and electricity systems in France

  1. 1. Modeling long term interactions between energy vectors: a case study for gas and electricity systems in France Rémy Doudard – Jérôme Gutierrez – Edi Assoumou Monday, July 10th, 2017 1
  2. 2. Topology of gas/electricity systems interactions Natural gas Gas for electricity production Gas demand Electricity production Electricity demand Competition between technologies Bio methane 1G Synthetic gas H2 Power-to-Gas 2 MIX 1st biomethane injection: 2015
  3. 3. Topology of gas/electricity systems interactions Natural gas Gas for electricity production Gas demand Electricity production Electricity demand Competition between technologies Bio methane 1G Synthetic gas H2 Power-to-Gas 3 MIX Pilot project - 1 MW / 2018 1st biomethane injection: 2015 ~ 450 TWh/ year Similar to final electricity demand ~ 9% of total installed capacity in the power sector
  4. 4. Prospective approach in order to assess…  Technological choices  Gas system (hydrogen, gas from methanation, biomethane)  Electricity system (gas-to-power…)  System operation  Hourly gas and electricity mix variation 4
  5. 5. Understanding systems topology and operation: focus on the Power-to-Gas chain Catalytic methaner Industry capture Gas for electricity production Industrial gas demand Natural gas Compression/ injection H2 Bio methane G1 MIX Other gas demand H2 CH4 CO2 Elect ricity AC/DC transformer + Electrolyser Compression/ injection CH4 Power plants 5 Biogas purification
  6. 6. Modeling challenges  Embed system complexity:  Various technological pathways, from primary resources to final demand  Determine the best configuration given various hypothesis:  Demand assumption,  Energy prices,  Emissions constraints,  … Optimal paradigm 6
  7. 7. Our choice : TIMES model generator Demand Constraints Minimization of the total discounted cost over horizon Technological choices 7
  8. 8. TIMES_FR_GazElec: french gas/electricity system joint optimisation model Joint work between two PhD students: - Jérôme Gutierrez (CMA) - Rémy Doudard (CMA/GRTgaz) TIMES_FR_GazElec 12 months, 2 representative days (Sem/We), 24h (576 timeslices) Electricity system modeling (electricity final demand) Detailed representation of power plants Gas system modeling (gas final demand) Power-to-gas chain modeling 8
  9. 9. Definition of long-term constraints Case study Final energy demand scenario Constraints Associated scenario « Basis » scenarios of French electricity & gasTSOs BASE F4 BASE CO2 constraint Factor 4 /2012 No CO2 increase from the electricity system Share of nuclear power limited to 50% from 2025 to 2050 9
  10. 10. A first decarbonization pathway 10
  11. 11. Results – « factor 4 » scenario 11
  12. 12. Use of methanation from 2025 to 2050…  Use of hydrogen and synthetic gas  « stress » on electricity and CO2 supply 12
  13. 13. …but the CO2 trajectory makes the system « overconstrained »  Hydrogen and synthetic gas are « imported » by the model with a very high cost  Marginal cost of CO2 : cost change in the objective function if we decrease CO2 emissions by one unit 13
  14. 14. What happens when we modify the decarbonization pathway ? 14 +50% emissions Initial pathway
  15. 15. Comparison of CO2 marginal costs 15 System not overconstrained anymore Comparison with basis scenario for following slides
  16. 16. Electricity & gas mix cross-analysis Higher electricity production in order to supply electrolysers 16 Hydrogen & synthetic gas injection
  17. 17. Electricity demand Gas consumption in 2050 : analysis for all timeslices 17 Gas mix Analysis for all timeslices (1 weekday and 1 weekend per month) The activity of the electrolyser is lower at annual peak demand (january) and also at low availability of intermittent renewable energy
  18. 18. Energy flow for gas system in 2050 η= 0.72 Global efficiency of the methanation chain : 60% 155 TWh 112 TWh 6 TWh 106 TWh 88 TWh 30 TWh269 TWh 18 Biogas purification
  19. 19. Conclusion  Prospective approach in order to investigate interactions between gas and electricity systems in France  A bottom-up approach is necessary in order to understand and quantify these long-term interactions  Two combined effects influence the level of interactions:  « Timing » of CO2 constraint  Availability of technologies 19
  20. 20. Further work : flexibility of multi-energy systems, prospective approach Residential / Tertiary IndustryImport /Export Central ised produc tion Import /Export Gas Hydrogen Electricity Heat 20 MobilityGrid Grid StorageStorage Other sources Petrol
  21. 21. Further work : flexibility of multi-energy systems, prospective approach Residential / Tertiary IndustryImport /Export Central ised produc tion Import /Export PV local Biomethane Gas Hydrogen Electricity Heat Mobility Local solar Grid Grid Storage StorageStorage Storage Other sources 21 Petrol
  22. 22. Further work : flexibility of multi-energy systems, prospective approach Residential / Tertiary IndustryImport /Export Central ised produc tion Import /Export PtH2 CCGT micro CHP CHP PtH PV local Biomethane Biogas Mobility Metha nation Local solar Grid Grid Storage StorageStorage Storage Power to gas 22 Heat recovery Petrol Other sources Gas Hydrogen Electricity Heat
  23. 23. Thank you for your attention ! 23 remy.doudard@mines-paristech.fr
  24. 24. Appendices 24
  25. 25. Cumulated emissions 25 Up to +23% of cumulated emissions in 2050
  26. 26. Electricity mix 26
  27. 27. Gas mix 27
  28. 28. Demand assumptions RTE BP 2014, scénario C Perspectives gaz naturel et renouvelables, scénario A One load profile and consumption annual mean growth rate for each sector 28
  29. 29. Energy prices assumptions Source petrol/coal/natural gas : WEO 2013 new policies Source : panorama du gaz renouvelable en 2016 29
  30. 30. CO2 capture - industry  Capture cost assumption: 75€/t Source : Reiter et al., Evaluating CO2 sources for power-to-gas applications – A case study for Austria, 2015 30
  31. 31. Methanation process 31  Assumption : stoechiometric conditions Methaner CO2 CH4 mol CH4 vers PJ CH4 PJ vers mol H2 H2 4 mol 1 mol kt CO2 vers mol CO2 1 mol 𝐶𝑂2 + 4𝐻2 → 𝐶𝐻4 + 2𝐻2 𝑂
  32. 32. Electricity Import/Export Source : Vincent Krakowski, 2016, « Renewable energy integration and power grid extension : reconciling spatial and temporal scales in long term planning exercises » 32

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