Can Advanced Analytics Turn Carbon Capture into a Core Enabler of Clean Energy?

Carbon capture analytics is emerging as a defining force in the integration of renewable energy. What was once a peripheral technology is now central to scaling variable renewables while managing residual emissions from fossil-based and hard-to-abate sectors. The fusion of advanced analytics, next-generation capture systems, and real-time grid intelligence is changing the economics of decarbonisation. New approaches, such as membraneless electrochemical capture and integrated flow batteries that both store renewable energy and sequester carbon, are demonstrating how capture can enhance energy security, grid stability, and climate resilience. 

Deploying carbon capture at scale requires more than innovation. It requires system-level intelligence that links capture operations to the dynamics of renewable energy generation. Advanced analytics enables precise modelling of energy flows, capture performance, and cost trade-offs. By aligning carbon capture with renewable availability and grid conditions, analytics can maximise emission reductions, improve operational efficiency, and reinforce the value chain of a fully decarbonised energy system. 

The System Imperative 

Achieving a 1.5 °C-consistent pathway requires renewable energy and demand-side measures to deliver approximately 80% of emission reductions. The remaining cuts, roughly 20%, must be addressed through carbon capture and removal solutions, including post-combustion capture, pre-combustion capture, oxy-combustion, bioenergy with carbon capture and storage (BECCS), and direct air capture. According to IRENA, CO₂ capture rates must reach six gigatonnes per annum by 2040 and over eight gigatonnes per annum by 2050. 

Integration analytics is essential for three reasons: 

• Flexibility Optimisation: Carbon capture operations require energy and have operational constraints. Analytics enables alignment with variable renewable output to optimise efficiency and reduce energy penalties.  

• Cost Trade-offs: Capture increases both capital and operational costs. Scenario-based analytics quantifies the cost-benefit of hybrid systems and identifies economically optimal configurations. 
  

• System-level Emissions Profiling: Advanced modelling evaluates how capture reduces residual emissions while improving the effective utilisation of renewable generation, decreasing curtailment, and supporting grid stability. 

Technology Interfaces and Operational Considerations 

Integration of renewables with carbon capture involves three key operational interfaces: 

• Energy Supply for Capture Systems: Capture technologies require thermal and electrical energy, which can be partially sourced from renewable energy sources. Coupling solar thermal with solvent-based post-combustion capture reduces the extraction of steam from turbines and mitigates energy penalties. 

  
  

• Temporal Flexibility: Renewable generation is intermittent. Capture systems have minimum load requirements, ramp-rate limits, and regeneration cycles. Advanced modelling frameworks demonstrate that solar-assisted capture can reduce costs by nearly 9% and significantly reduce the need for storage, achieving emission reductions of up to 91% in U.S. coal plant simulations.  

  
  

• System Value Assessment: Beyond the volume of CO₂ captured, analytics must assess reductions in renewable curtailment, LCOE implications, flexibility value, and marginal emissions reductions. Capture operations effectively become a grid-integrated lever rather than a standalone solution. 

Proven Models from Large-Scale Implementations 

The global deployment of carbon capture and storage (CCS) projects offers critical evidence for understanding performance, cost, and integration potential. 

• The Petra Nova project in Texas remains one of the most recognised examples of a commercial-scale retrofit on a coal-fired power plant. Designed to capture around 1.6 million tonnes of CO₂ per year, the project demonstrated the operational feasibility of large-scale capture, with the captured CO₂ used for enhanced oil recovery. It has since served as a benchmark for evaluating the integration of post-combustion capture in hybrid renewable systems.  

  
  

• In Australia, the Gorgon Carbon Dioxide Injection Project on Barrow Island represents one of the world’s most extensive integrated CCS facilities. The project is designed to inject between 3.4 and 4 million tonnes of CO₂ annually, storing emissions separated from natural gas processing in deep geological formations. This project provides a valuable operational model for CO₂ transport, injection logistics, and monitoring analytics. 

  
  

• In Brazil, Petrobras’ Santos Basin pre-salt CCS initiative has established itself as the most extensive ongoing carbon storage operation globally, with an annual injection capacity exceeding 10 million tonnes of CO₂. The project’s continuous monitoring data, integrated with offshore production analytics, provides rich insights for designing renewable-integrated carbon management systems.  

  
  

• Earlier projects, such as the Weyburn-Midale Carbon Dioxide Project in Saskatchewan, Canada, provided foundational lessons for reservoir characterisation and monitoring methodologies, which now inform predictive modelling for carbon storage analytics.  

  
  

• The Northern Lights CCS Project in Norway, part of the country’s Longship initiative, marks a milestone in creating a scalable CCS value chain. With its first CO₂ injections completed in 2025, Northern Lights is designed to store several million tonnes annually, serving multiple industrial emitters through a shared transport and storage infrastructure. It represents the first fully integrated, cross-sector CCS hub aligned with a national renewable energy strategy.

  
  

Collectively, these projects demonstrate that CCS technologies are operational at an industrial scale, providing robust and verifiable data for system-level analytics that span energy requirements, capture efficiency, storage performance, and integration economics. 

Analytics Framework for Integration 

Expert-level carbon capture analytics must incorporate five pillars: 

1. Temporal Modelling: High-resolution, hourly simulations of renewable generation and capture operations enable precise assessment of energy penalties, ramp constraints, and solvent regeneration schedules. 

   
   

2. Scenario-Based Cost Sensitivity: Variations in renewable energy cost, capture system CAPEX/OPEX, carbon pricing, and plant utilisation must be modelled to identify economically optimal configurations. 

   
   

3. System-Level Metrics: Metrics should include avoided renewable curtailment, marginal emission reductions, flexibility contributions, and LCOE impact to quantify systemic value beyond CO₂ captured. 

   
   

4. Geospatial Assessment: Analysis should consider renewable resource availability, retrofit potential for capture, CO₂ transport and storage logistics, and local policy frameworks. 
   

5. Future Removal Technology Scenarios: BECCS and direct air capture should be incorporated into long-term scenario modelling, including interactions with variable renewable energy and storage solutions. 

 

Strategic Insights 

Several key insights emerge from real-world data and system-level analytics: 

• Capture Enhances Renewable Value: Acting as a flexible load, capture systems reduce curtailment and improve the effective utilisation of variable renewables. 
  

• Energy Penalty Mitigation is Critical: Integrating renewables as an energy source for capture reduces the energy penalty, improving net system efficiency. 
  

• Economics Depend on Policy, Asset Utilisation, and Integration: Carbon pricing, plant utilisation, and renewable system costs strongly influence the financial feasibility of hybrid configurations. 
  

• Location-Specific Resource Mapping is Essential: Optimising site selection and resource allocation ensures maximum emission reduction at the lowest cost. 
  

• Scale-Up Challenges Remain Substantial: The current global capture deployment is far below the levels required for meaningful decarbonization, highlighting the urgency of integrating these strategies. 

Conclusion 

Carbon capture is no longer a standalone mitigation measure. When integrated with renewable energy systems using advanced analytics, it becomes a system-level enabler that enhances flexibility, reduces emissions, and supports the penetration of renewables at scale. Verified real-world projects, from North America to Norway, demonstrate operational feasibility and provide concrete benchmarks for analytics. High-resolution modelling, scenario analysis, geospatial optimisation, and integration with emerging removal technologies are essential tools to ensure that capture complements renewables efficiently and effectively.  

Decarbonisation at scale demands a precise, data-driven approach where carbon capture and renewable energy act as synergistic partners in the transition to net-zero.