SRE Reliability Metrics: SAIDI/SAIFI Benchmarking for Electric Cooperatives in 2026

NRECA's 2025 Reliability Survey data reveals a pattern that has been consistent for the past decade: the gap between top-quartile and bottom-quartile cooperatives on SAIDI and SAIFI metrics is not narrowing — it is widening. Top-quartile cooperatives are achieving SAIDI values below 100 minutes per year. Bottom-quartile cooperatives are reporting SAIDI values above 400 minutes. The difference is not geography, not weather, and not system age — it is planning discipline and investment prioritization.

What the Top-Quartile Cooperatives Are Doing Differently

The cooperatives with the best reliability metrics share three characteristics. First, they have invested in systematic reliability studies — not just after major outage events, but as part of their annual planning cycle. Second, they use OMS data analytically, not just operationally — they mine outage records for patterns, identify chronic trouble spots, and prioritize capital investment based on reliability impact rather than asset age. Third, they have moved beyond reactive maintenance to predictive maintenance programs informed by data.

The AI Reliability Analysis Advantage

Traditional reliability studies require engineers to manually analyze OMS data, build reliability models in CYMDIST or similar platforms, and produce reports that are often outdated by the time they are delivered. AI-powered reliability analysis compresses this cycle dramatically — automated OMS data ingestion, machine learning-based outage pattern recognition, and AI-generated capital prioritization recommendations can produce a reliability study in days rather than months.

Regulatory Implications

State public utility commissions are paying increasing attention to cooperative reliability metrics. Several states have implemented reliability performance standards with financial penalties for cooperatives that consistently underperform benchmarks. NRECA's reliability benchmarking program provides cooperatives with comparative data, but it does not provide the engineering analysis needed to actually improve performance.

The Powerlytics.ai SRE Approach

Our System Reliability Evaluation studies combine traditional CYMDIST reliability modeling with AI-powered OMS data analysis to produce reliability improvement plans that are both technically rigorous and practically actionable. We identify the specific feeder segments, equipment types, and outage causes driving your worst reliability metrics — and we prioritize corrective actions by reliability impact per dollar invested.