Behind-the-Meter Power: Reducing AI Infrastructure Costs

The Power Problem in AI

AI inference is energy-intensive. A single large language model query can consume 10× the energy of a traditional web search. At scale, power becomes one of the largest operational costs—and a significant sustainability concern.

What is Behind-the-Meter Power?

Behind-the-meter (BTM) refers to electricity generation that connects directly to a facility, bypassing the public grid. For AI datacentres, this typically means:

• On-site solar or wind generation
• Direct power purchase agreements (PPAs) with adjacent renewable facilities
• Co-location with power generation assets

The Economics

Traditional grid power for datacentres includes:

• Generation costs
• Transmission and distribution fees
• Grid maintenance charges
• Regulatory levies

Behind-the-meter arrangements eliminate most of these intermediary costs. Our analysis shows:

60% Cost Reduction

Direct renewable integration reduces power costs from typical grid rates to near-generation costs. For a facility running 24/7 inference workloads, this compounds dramatically.

Price Stability

Long-term PPAs lock in energy costs, protecting against:

• Wholesale market volatility
• Carbon pricing increases
• Grid infrastructure charges

Carbon Neutrality

Direct renewable sourcing provides genuine carbon reduction, not just offset certificates.

Implementation Considerations

Location Selection

BTM viability depends heavily on geography:

• Solar: Optimal in high-irradiance regions
• Wind: Requires consistent wind resources
• Hydro: Limited to specific locations but highly reliable

Capacity Matching

AI workloads have variable demand. Successful BTM implementations require:

• Battery storage for load smoothing
• Grid interconnection for backup
• Workload scheduling aligned to generation patterns

Regulatory Navigation

Power arrangements vary by jurisdiction. Key considerations:

• Wheeling agreements for nearby generation
• Grid connection requirements
• Licensing for self-generation

Case Study: Renewable-Aligned Scheduling

Our infrastructure uses intelligent workload scheduling to maximise renewable utilisation:

1. Batch inference jobs shift to peak solar/wind periods
2. Real-time inference maintains priority with battery buffer
3. Model updates and maintenance schedule during surplus generation

This approach achieves 85%+ renewable utilisation without compromising latency SLAs.

The Broader Impact

Beyond cost savings, BTM power addresses:

• Scope 2 emissions for enterprise customers
• ESG reporting requirements
• Regulatory pressure on datacentre energy consumption

Conclusion

Behind-the-meter power isn't just about cost reduction—it's about building AI infrastructure that scales sustainably. As energy becomes an increasingly large share of inference costs, power strategy becomes competitive advantage.

Contact info@scx.ai to learn more about our energy-efficient infrastructure.