Why you can be "right on average" and still pay imbalance costs

Understanding the hidden covariance effect in power markets

Forecasting teams often optimize for a simple goal: be "right on average". Near‑zero mean error? Check. Near‑zero imbalance–day‑ahead spread? Check.

So why does the imbalance bill still come in positive?

Here's the part many market participants overlook: Your bill isn't driven by the average of two numbers — it's driven by the average of their product.

In other words:

Imbalance cost ≈ forecast error × (imbalance price – day‑ahead price)

Over time, that becomes:

mean(imbalance cost) = mean(error × spread) = mean(error) × mean(spread) + covariance(error, spread)

This last term "covariance" is the silent force behind persistent imbalance costs. Even if your average error is close to zero, and your average spread is close to zero, a positive covariance will still generate a positive bill.

Why covariance matters in real power markets

Power markets aren't linear. They're full of stress moments: scarcity hours, renewable volatility, plant outages, grid events, and unexpected ramps.

These are also the exact periods when:

• Forecast errors tend to be largest
• Imbalance spreads widen dramatically
• BRPs, traders, and asset owners feel the most financial pain

So the system doesn't penalize you because you're "wrong on average". You're penalized because you're wrong at the wrong time.

That alignment — large errors occurring in high‑spread hours — is exactly what pushes the covariance term positive… and keeps the imbalance bill stubbornly above zero.

The real takeaway: optimize for the moments that matter

Reducing bias is not enough. Improving your average error is not enough.

What matters is reducing error when spreads explode.

That's where true financial impact lives.

In practice, this means focusing forecasting improvements on stress regimes:

• Highly volatile solar and wind periods
• Transition moments between renewable ramping
• Scarcity events and system imbalances
• Grid congested hours
• Weather‑driven uncertainty windows

When you improve accuracy specifically during these high‑spread hours, you directly reduce covariance — and with it, your structural imbalance cost.

In one sentence

You don't lose because you're wrong on average — you lose because you're wrong at the wrong time.

The Amplifino perspective

At Amplifino, we see this pattern across the portfolios we help forecast and optimize. The winning approach combines:

• High‑performance forecasting models tuned for volatility
• Explainable AI that reveals where and when errors spike
• Stress‑regime modeling to identify high‑spread conditions early
• Continuous recalibration to ensure models stay stable in extreme situations
• Deep operational knowledge of BRP processes and market behavior

Our goal is simple: ensure energy companies are not just "balanced on average", but balanced when it matters most.

Want to reduce your imbalance costs where it actually counts? Talk to our forecasting engineers. We help BRPs, traders, and asset owners build forecasting systems that perform exactly during the most financially impactful moments.

Explore AmpliCast™ or request a technical session with our team.