TC Energy drives major emissions reductions with AI-powered compression optimization
How TC Energy leveraged AVEVA PI System and CONNECT to optimize more than 1,000 compressors, reduce emissions by 54,000 metric tons of CO₂e, and advance its sustainability goals.
“We wanted a fit-for-purpose solution that operators could trust. That meant embedding domain expertise into the tool while also providing flexibility through what-if analysis and dashboards in CONNECT.”” - Steven Kluge, Sr. Real-time Monitoring & Analytics Consultant, TC Energy
Challenge
Reduce emissions across one of North America’s largest natural gas pipeline networks
Optimize compression fleet performance in real time across 365 stations and 1,000+ units
Align operations with corporate sustainability and emissions-reduction commitments
Solution
Deployed the Compression Optimization Tool (COT), integrating the AVEVA PI System portfolio and CONNECT with custom optimization engines and data from AVEVA Operations Control, AVEVA Measurement Advisor, and AVEVA Enterprise SCADA to deliver real-time, AI-driven compressor fleet recommendations
Results
180 actionable optimization opportunities implemented across operations
54,000 metric tons of CO₂e eliminated, equivalent to 11,800 passenger cars removed from the road
Fuel efficiency gains through optimized compressor configurations at scale
Culture shift toward data-driven sustainability, with continuous improvement
embedded into workflows
Enhanced visibility and collaboration using CONNECT dashboards accessible across team
TC Energy operates one of North America’s largest natural gas pipeline systems—spanning more than 58,000 miles and meeting 30 percent of continental demand. With over 1,000 compressor units delivering 11.3 million horsepower, the company faced a critical challenge: how to maintain reliable energy supply while dramatically reducing emissions across its vast footprint.
To drive a more sustainable energy future, leadership set ambitious sustainability targets, but achieving them required more than incremental efficiency gains. Compressor stations, among the most energy-intensive assets in the network, consume large amounts of fuel and are a major source of greenhouse gas emissions. This challenge demanded a fundamental transformation in how compression operations were monitored, managed, and optimized.
The business case was compelling: even small improvements in compressor performance could deliver significant emissions reductions when scaled across hundreds of sites. To create a greener future, TC Energy needed a purpose-built solution that could reduce fuel consumption and emissions without compromising reliability—while embedding sustainability into the core of its operations.
“Our goal was clear: reduce fuel consumption and emissions without compromising system reliability.” --Richard Stinson, Team Lead, Real-time Monitoring & Analytics, TC Energy
Inside the optimization process: Human + machine
To meet this challenge, TC Energy developed the Compression Optimization Tool (COT), a platform that combines machine learning, performance engineering, and combinatorics. COT’s purpose is straightforward: recommend the most fuel-efficient compressor configurations in near real time.
TC Energy uses several AVEVA products across its operations, including AVEVA Operations Control, AVEVA Measurement Advisor, and AVEVA Enterprise SCADA. TC Energy feeds much of the data from these solutions and other operational data sources into the AVEVA PI System portfolio. At the heart of TC Energy’s COT, AVEVA™ PI Server’s asset framework component contextualizes and organizes operational data from compressors. Digitized compressor performance models and fuel curves, delivered through the Compressor Book Performance API, feed into the COT optimization engine.
Every 15 minutes, the optimization engine evaluates configurations for every station across TC Energy’s U.S. footprint. If a more fuel-efficient setup is found, the system flags the opportunity for review. A human-in-the-loop process ensures that Gas Control supervisors validate recommendations before implementation.
The result is a system that balances automation with human judgment, enabling real-time decision support while building operator confidence.
The measurable impact: 54,000 metric tons of CO₂e eliminated
Since its rollout, the COT has delivered measurable results. Between June 2023 and January 2025, TC Energy implemented 180 COT-driven recommendations, directly reducing 54,000 metric tons of CO₂e. This figure does not account for secondary benefits such as cultural shifts and operational learnings, which have also contributed to emissions reductions.
The impact is tangible. According to the U.S. Environmental Protection Agency, one passenger vehicle emits about 4.6 metric tons of CO₂ annually. By this measure, TC Energy’s optimization program equates to removing nearly 12,000 cars from the road.
The Compression Optimization Tool combines PI System data, compressor models, and machine learning to deliver real-time recommendations. The Compression Optimization Tool combines PI System data, compressor models, and machine learning to deliver real-time recommendations.
Beyond emissions, the program has fostered a new culture of continuous improvement. To make insights more widely accessible, TC Energy used the PI to CONNECT Agent, which synchronizes real-time data and asset context from AVEVA PI Server to the CONNECT platform. Operators now routinely leverage CONNECT dashboards for situational awareness, while the COT Findings tool captures and documents lessons learned for future decision-making.
Future work is already planned. TC Energy intends to expand COT’s scope to include multi-mode station operation, broader system-wide optimization, and alternate objectives beyond emissions, such as throughput maximization.
TC Energy’s solution leverages the AVEVA PI System portfolio and the CONNECT industrial intelligence platform, which are both included with the AVEVA™ PI Data Infrastructure offering.
“We’re not just seeing numbers on a dashboard. These optimizations translate into real environmental benefits. That’s a point of pride for our teams and a validation of our sustainability strategy.” -- Brendan Bell, Team Lead, Asset Applications, TC Energy
