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Version: 1.0.0

Using HCM Results

HCM results are structured around two ideas: that constraints should be described in terms of where they occur and how severe they are over time, and that uncertainty about the future is best handled by running multiple scenarios rather than a single forecast. This page explains how those ideas shape the result structure and what that means for how you use them.

How results are structured​

Measurement zones​

Results are organized by measurement zones - logical groupings of network assets running from key control points downstream to the next zone boundary. Zones never overlap, which means CECV and VCR risk metrics can be aggregated across the network without double-counting. For a full description, see Measurement Zones in the Introduction.

Constraint types​

The HCM tracks two categories of constraint: thermal (equipment operating beyond rated capacity - transformer overloads, conductor limits) and voltage (overvoltage from high solar generation, undervoltage from high load or long feeders, phase imbalance). Each constraint is tied to a specific measurement zone and carries temporal data showing when it occurs.

Duration curves​

Rather than storing raw time-series data for every interval, HCM aggregates constraints into duration curves - a ranking of all intervals from worst to best. This reveals how severe the worst conditions are, how often equipment operates outside limits, and when violations begin and end. The key insight is that duration curves distinguish between occasional edge cases and systematic problems: a constraint that appears for 10 hours a year at high magnitude is a different planning problem than one that appears for 200 hours at low magnitude, even if the peak severity is the same.

Why scenarios matter for interpreting results​

A single HCM result is a snapshot of one possible future. Its value comes from comparison - against other scenarios, against a baseline, against intervention alternatives.

Comparing scenarios reveals which measurement zones are sensitive to DER adoption assumptions. Zones with large differences between a high-DER and low-DER scenario are where planning uncertainty is greatest and where robust, flexible investment decisions matter most.

Diffing against a baseline quantifies what changes when you add interventions or update the network model. CECV and VCR differences at each zone show exactly how much constraint relief an intervention delivers, which makes cost-benefit comparisons objective rather than judgement-based.

Model update validation is an often-overlooked use of diffs: when the network model is updated (corrected impedances, new assets, revised tap positions), diffing before and after confirms that changes are localised to expected areas and haven't introduced unintended constraint shifts elsewhere.

How HCM fits into the broader workflow​

HCM results are an input to other tools and processes, not a standalone output.

The Intervention Module takes base work package results, identifies measurement zones exceeding constraint thresholds (candidate generation), deploys interventions to the highest-ranked candidates within yearly allocation limits, then re-runs work packages to measure effectiveness. Because the Intervention Module uses the same result framework as core HCM, before-and-after comparisons are direct.

The OpenDSS Exporter lets you take constrained feeders identified by HCM and export them as detailed power flow models for independent validation or deeper engineering studies. This is useful when HCM flags a constraint that warrants more scrutiny than aggregated metrics can provide.

The Feeder Load Analysis Tool works at a finer grain than HCM's measurement zones, revealing load distribution patterns and localized hot spots within a zone. Use HCM to identify which zones are constrained, then Feeder Load Analysis to understand what's driving it intra-zone.

Network model calibration closes the loop between results and model accuracy. As the future unfolds, comparing HCM constraint predictions against actual SCADA data reveals where the model diverges from reality, allowing iterative refinement of impedances, tap positions, and other parameters. Results from a well-calibrated model carry more weight in connection assessments and regulatory submissions.

Validating results​

HCM results are only as reliable as the model and inputs behind them. Before acting on results:

  • Compare base year results against SCADA measurements at zone substations and AMI voltage data where available
  • Check that constraint locations and timing align with field experience - if a result surprises you, investigate rather than accept it
  • Review power flow convergence rates; feeders with low convergence reliability produce less trustworthy constraint data and may need model attention

Next Steps​