Planning a Notable CCS Pilot-Scale Project: A Case Study in France, Paris Basin—Ile-de-France
Abstract
:1. Introduction
2. Materials and Methods
2.1. Mapping CCUS Aspects
2.2. Economic Key Performance Indicators (KPIs)
- Energy consumption.
- Net present costs for the capture, transport and storage.
- Amount of CO2 emissions avoided and negative emissions.
- Revenue created by the down-stream utilization industries.
3. Results
3.1. Mapping Results
3.1.1. Emissions Sources
3.1.2. Storage Options
With
[(A × 1 × 106) × h × Phi] = Reservoir Pore volume
3.1.3. Spatial Condition for Cluster and Network
3.1.4. National Low-Carbon Strategy and Emission Profile
3.1.5. Mapping Societal Aspects
- The majority of interviewees considered CCUS technologies as a potential option to fight against climate change.
- Interviewees often underline that CCUS is only one option among other solutions to reduce carbon dioxide emissions.
- CCU is particularly well-perceived by interviewees and appears to them to offer higher potential than CCS, regardless of the current limited volumes concerned by CO2 valorization.
3.2. Economic KPIs
Waste-to-Energy Challenge
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Emitter ID | Facility Name | Industry Sector | CO2 from Biomass Combustion (Ton) | CO2 Reported (Ton) | Year Reported | Emission Trend (2016–2019) |
---|---|---|---|---|---|---|
FR1.ES.002 | Borealis Grandpuits | Chemicals (other) | 645,723 | 2019 | Irregular | |
FR1.ES.003 | IVRY PARIS XIII | Energy from waste | 330,683 | 572,248 | 2019 | Stable |
FR1.ES.004 | CPCU chaufferies de ST-OUEN I et ST-OUEN II | Power | 150,949 | 522,182 | 2019 | Stable |
FR1.ES.005 | DALKIA WASTENERGY | Energy from waste | 231,791 | 416,366 | 2019 | |
FR1.ES.006 | TSI | Energy from waste | 217,779 | 383,763 | 2019 | Growing |
FR1.ES.007 | SNC Cogé VITRY | Power | 243,577 | 2019 | Stable | |
FR1.ES.008 | Ciments Calcia usine de Gargenville | Cement | 100,275 | 224,897 | 2019 | Falling |
FR1.ES.009 | VALO’MARNE | Energy from waste | 123,700 | 222,420 | 2019 | Stable |
FR1.ES.010 | SEMARIV-CITD | Energy from waste | 107,000 | 188,000 | 2018 | Falling |
FR1.ES.011 | CPCU ST-OUEN III | Power | 163,579 | 2019 | Stable | |
FR1.ES.012 | SIAAP Site Seine Aval | Energy from waste | 143,847 | 144,299 | 2019 | Falling |
FR1.ES.013 | SAREN | Energy from waste | 81,893 | 143,672 | 2019 | Growing |
FR1.ES.014 | Routière de l’Est Parisien (ISDND de Claye Souilly) | Energy from waste | 140,933 | 140,933 | 2019 | Falling |
FR1.ES.015 | AUROR’ENVIRONNEMENT | Energy from waste | 78,501 | 137,944 | 2019 | Falling |
FR1.ES.016 | CVD Thiverval-Grignon | Energy from waste | 76,000 | 133,000 | 2018 | Irregular |
FR1.ES.017 | AZALYS | Energy from waste | 67,860 | 119,053 | 2019 | Falling |
FR1.ES.018 | SOMOVAL | Energy from waste | 60,085 | 106,088 | 2019 | Stable |
FR1.ES.019 | GENERIS—Site de Rungis | Energy from waste | 60,033 | 105,599 | 2019 | Falling |
FR1.ES.020 | BOUQUEVAL ENERGIE | Energy from waste | 86,736 | 86,736 | 2019 | Falling |
FR1.ES.021 | SARP Industries | Energy from waste | 72,764 | 2019 | Irregular | |
FR1.ES.022 | SAM MONTEREAU | Iron & Steel | 68,948 | 2019 | Irregular | |
FR1.ES.023 | SGD Usine de SUCY EN BRIE | Glass | 56,851 | 2019 | Stable | |
FR1.ES.024 | CYEL | Power | 32,042 | 54,489 | 2019 | Irregular |
FR1.ES.025 | ALPA | Iron & Steel | 50,398 | 2019 | Growing | |
FR1.ES.026 | KNAUF Plâtres | Other | 48,995 | 2019 | Stable | |
FR1.ES.027 | BIO SPRINGER | Food & drink | 45,223 | 2019 | Stable | |
FR1.ES.028 | GRAND PARIS SUD ENERGIE POSITIVE | Power | 44,095 | 2019 | Growing | |
FR1.ES.029 | ENERTHERM Noël Pons | Power | 40,437 | 2019 | Irregular | |
FR1.ES.030 | VELIDIS Chaufferie Vélizy V3 | Power | 39,226 | 2019 | Falling | |
FR1.ES.031 | VERSEO | Power | 37,512 | 2019 | Irregular | |
FR1.ES.032 | chaufferie zup de fontenay | Power | 35,777 | 2019 | Falling | |
FR1.ES.033 | SAFRAN AIRCRAFT ENGINES | Other | 35,666 | 2019 | Irregular | |
FR1.ES.034 | Chaufferie de Parly 2 | Power | 32,344 | 2019 | Stable | |
FR1.ES.035 | PEUGEOT CITROËN POISSY SNC | Other | 31,713 | 2019 | Falling | |
FR1.ES.036 | SEMECO (et IDEX ENERGIES) | Power | 30,916 | 2019 | Stable | |
FR1.ES.037 | chaufferie zup de sevran | Power | 16,938 | 30,738 | 2019 | Irregular |
FR1.ES.038 | LESAFFRE FRERES | Food and drink | 27,850 | 2019 | Irregular | |
FR1.ES.039 | ENGIE Chaufferie de Meudon | Power | 26,585 | 2019 | Growing | |
FR1.ES.040 | OUVRE FILS Sucrerie et Distillerie | Food and drink | 23,812 | 2019 | Irregular |
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Tiers | Classification | Suitability Criteria |
---|---|---|
Tier 1 | Regional assessment; equivalent to prospective (theoretical) | Generic SEFs (storage efficiency factor). Formation and storage unit estimate. First approximation. Low data burden and global storage efficiency values where boundary conditions are poorly constrained or uncertain. |
Tier 2 | Discovery assessment; equivalent to low contingent (effective) | Tailored SEFs. Daughter unit estimates. Second approximation. Moderate data burden and lithology-specific regional storage efficiency factors. Distinction between deep saline aquifers, depleted hydrocarbon fields and coal beds. Boundary conditions are established. |
Tier 3 | Prospect assessment; equivalent to pending/on hold (practical) | Detailed data prospective candidates. Third approximation with a more taxing data burden, including subattributes of the main factors used to estimate capacity and lithology-specific local SEFs. Each candidate prospect requires either existing or targeted data acquisition sufficient to build a simple geomodel for first-pass simulation and well location consideration. |
Tier 4 | Site assessment; equivalent to justified/approved/on injection (matched), project. | Targeted storage sites. The final approximation prior to operation. This has the highest data burden and requires a detailed geomodel for reservoir simulation studies. Outcomes from the simulations test the accuracy of the storage efficiency factors and provide scenarios for maximizing capacity based on well planning and scheduling. |
Benefits | Risks |
---|---|
Environmental benefits (climate change mitigation, carbon neutrality in the industries in the region and pollution reduction in the region) | Economic viability (increase in cost and decrease in competitiveness for industries) |
Economic development in the region (new industries, employment, investments and allowing power plants to keep working) | Environmental risks (risk of underground storage) |
Other (financial benefits for companies, beneficial for company image and promotion of a circular economy) | Social impacts (public opposition) |
Industries | Sector | Location | Capture Start Year | Annual CO2 Emissions Considered—MtCO2/y | CO2 Capture Rate (%) | Annual CO2 Captured (Mt/y) | Total CO2 Captured (Mt/y) | Part of CO2 Captured from Biomass (Mt/y) |
---|---|---|---|---|---|---|---|---|
E#01 (FR1.ES.002) | Chemistry | Grandpuits | 2027 | 0.65 | n/a | 0.36 | 9.7 | 0.0 |
E#02 (FR1.ES.003) | Energy from waste | Ivry-sur-Seine | 2030 | 0.30 | 0.90 | 0.27 | 7.4 | 3.7 |
E#03 (FR1.ES.004) | Energy from waste | Issy-les-Moulineaux | 2032 | 0.38 | 0.85 | 0.33 | 8.1 | 4.0 |
Industries with Capture Medium Term | CAPEX (M EUR) | Fixed OPEX (M EUR) | Variable OPEX (M EUR) | Total Costs (M EUR) | Excess of Energy Consumption for Capture (TJ) |
---|---|---|---|---|---|
E#01 (FR1.ES.002) | 4.1 | 2.9 | 1.3 | 8.3 | n/a |
E#02 (FR1.ES.003) | 76.4 | 360.1 | 0.3 | 436.8 | 24,413.0 |
E#03 (FR1.ES.004) | 84.9 | 362.2 | 0.4 | 447.5 | 28,255.0 |
EU ETS Parameters (EUR/tCO2) | Price of Allowances in 2025 | 70.1 |
---|---|---|
Price of Allowances in 2045 | 212.4 | |
Whole regional expense without CCUS (M EUR) | ETS costs without CCUS | 2270.0 |
Whole region expense with CCUS (M EUR) | ETS costs with CCUS and remaining emissions | 89.8 |
Costs of CCUS | 1041.2 | |
TOTAL costs with CCUS | 1131.0 |
CCS Value Chain (EUR/tCO2 Avoided) | −42 | |
CAPEX (EUR/tCO2 avoided) | Total per block | −8.3 |
Cost of Capture | −2.9 | |
Cost of Transport | −1.1 | |
Cost of Storage | −4.3 | |
OPEX (EUR/tCO2 avoided) | OPEX per block | −33.4 |
Cost of Capture | −24.7 | |
Cost of Transport | −0.6 | |
Cost of Storage | −8.1 | |
Transport cost (EUR/tCO2 transported) | −1.1 | |
Utilization (income from CO2 sales) (M EUR) | 0 | |
EU ETS credit savings in the region (M EUR) | 2180 |
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Share and Cite
Veloso, F.M.L.; Gravaud, I.; Mathurin, F.A.; Ben Rhouma, S. Planning a Notable CCS Pilot-Scale Project: A Case Study in France, Paris Basin—Ile-de-France. Clean Technol. 2022, 4, 458-476. https://doi.org/10.3390/cleantechnol4020028
Veloso FML, Gravaud I, Mathurin FA, Ben Rhouma S. Planning a Notable CCS Pilot-Scale Project: A Case Study in France, Paris Basin—Ile-de-France. Clean Technologies. 2022; 4(2):458-476. https://doi.org/10.3390/cleantechnol4020028
Chicago/Turabian StyleVeloso, Fernanda M. L., Isaline Gravaud, Frédéric A. Mathurin, and Sabrine Ben Rhouma. 2022. "Planning a Notable CCS Pilot-Scale Project: A Case Study in France, Paris Basin—Ile-de-France" Clean Technologies 4, no. 2: 458-476. https://doi.org/10.3390/cleantechnol4020028
APA StyleVeloso, F. M. L., Gravaud, I., Mathurin, F. A., & Ben Rhouma, S. (2022). Planning a Notable CCS Pilot-Scale Project: A Case Study in France, Paris Basin—Ile-de-France. Clean Technologies, 4(2), 458-476. https://doi.org/10.3390/cleantechnol4020028