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Using HCM Results

Work in progress

This page is a work in progress and should be considered a draft. Please check back later for the final version.

Overview​

After running a Hosting Capacity Module (HCM) work package, you'll have access to comprehensive results that reveal where and when network constraints will emerge under different DER adoption scenarios. This guide explains how to interpret, query, and apply these results to support network planning, connection assessments, and investment decisions.

The HCM performs time-series power flow calculations at 30-minute intervals across the entire distribution network—from zone substation busbars down to individual LV connections. For each interval, it identifies thermal overloads and voltage limit exceedances, storing these constraint events in a structured database that enables both detailed analysis and high-level strategic planning.

Understanding Result Types​

Network Performance Metrics​

The HCM aggregates constraint data into structured network performance metrics rather than overwhelming users with raw time-series data. These metrics are organized by measurement zone, quantifying:

  • When constraints first emerge (year and time of day)
  • How long they persist (total duration in hours)
  • What patterns they follow (daily, seasonal, annual)
  • How severe they are (magnitude and frequency)

This approach provides clear, actionable insights without requiring analysts to process terabytes of interval data.

Measurement Zones​

Results are organized by measurement zones—logical groupings of network assets that run from key control points (circuit breakers, switches, reclosers, transformers) downstream to the next zone boundary.

Key principles of measurement zones:

  • They never overlap - Each zone runs from its defining asset to the next zone or line end, preventing double-counting
  • Load and generation metrics are local - Metrics like gen_overvoltage_kwh or load_max_kw measure only assets within that specific zone
  • Network flow metrics aggregate through zones - Metrics like peak_import or peak_export capture net flows into and through the zone

This structure allows accurate aggregation of risk metrics such as Customer Energy Curtailment Value (CECV) and Values of Customer Reliability (VCR) without overlap.

Constraint Types​

The HCM identifies two primary constraint categories:

Thermal Constraints:

  • Equipment operating beyond rated capacity
  • Transformer overloading
  • Conductor thermal limits exceeded
  • Duration and magnitude of overload events

Voltage Constraints:

  • Overvoltage events (typically from high solar generation)
  • Undervoltage events (typically from high load or long feeders)
  • Voltage regulation equipment performance
  • Phase imbalance issues

Each constraint is linked to the specific measurement zone and equipment where it occurs, with temporal data showing when issues arise.

Duration Curves​

Duration curves provide a powerful visualization of equipment performance by sorting constraint severity over time. Rather than showing chronological sequences, duration curves rank all intervals from worst to best, revealing:

  • Peak stress conditions - The most severe operating points
  • Persistence of issues - How often equipment operates outside limits
  • Threshold crossing patterns - When violations begin and end

Duration curves help distinguish between occasional edge cases and systematic problems requiring intervention.

Interpreting Results for Planning​

Identifying Investment Priorities​

Use HCM constraint data to develop evidence-based capital expenditure plans:

1. Rank constraints by severity and frequency

  • Focus on measurement zones with the highest CECV or VCR values
  • Prioritize constraints that occur frequently versus occasional edge cases
  • Consider both current-year constraints and near-term emerging issues

2. Assess temporal patterns

  • Daily patterns: Peak load times versus solar generation peaks
  • Seasonal variations: Summer air conditioning versus winter heating
  • Annual progression: How quickly constraints worsen year-over-year

3. Evaluate constraint drivers

  • Determine whether issues stem from load growth, DER adoption, or both
  • Assess whether problems are localized or systemic across feeders
  • Identify root causes (conductor sizing, transformer capacity, tap settings)

4. Consider scenario-based planning

  • Compare constraint emergence across multiple scenarios
  • Identify robust investments that address constraints in most futures
  • Plan flexible solutions that can adapt as actual DER adoption unfolds

Supporting Connection Enquiries​

When evaluating DER connection applications, HCM results provide objective capacity assessments:

1. Check opportunity locations near the proposed connection

  • Query available capacity at the relevant measurement zone
  • Verify that capacity exists in the year the applicant plans to connect
  • Consider both the local zone and upstream zones

2. Review constraint patterns

  • Check for existing thermal or voltage issues
  • Assess whether the new connection would worsen existing problems
  • Evaluate timing—does the connection align with constraint periods?

3. Assess required interventions

  • If capacity is insufficient, identify what upgrades would be needed
  • Estimate intervention costs using measurement zone metrics
  • Determine whether system-wide or local solutions are appropriate

4. Provide data-driven responses

  • Use specific capacity figures in connection offers
  • Present evidence of constraint risks with duration curve data
  • Justify connection conditions or upgrade requirements with objective metrics

Network Reinforcement Planning​

Duration curves and constraint metrics inform strategic multi-year planning:

Proactive Planning:

  • Address constraints before they cause operational issues or customer complaints
  • Stage upgrades based on scenario-projected constraint emergence timelines
  • Coordinate interventions with planned maintenance or other capital works

Scenario-Based Strategies:

  • Use multiple scenarios to test investment robustness
  • Identify no-regrets investments that provide value across scenarios
  • Plan adaptive strategies that can respond to actual DER uptake rates

Optimization Opportunities:

  • Evaluate system-wide interventions (tariff reform, controlled load shifting) versus local upgrades
  • Compare intervention costs against constraint impacts (CECV/VCR values)
  • Consider alternatives like community batteries or dynamic voltage management

Comparing Scenarios​

The HCM's scenario-based approach enables comprehensive "what-if" analysis by running multiple futures with different DER adoption assumptions, technology mixes, and network configurations.

Scenario Comparison Workflow​

1. Establish baseline

  • Run a base scenario representing current network state with historical load/generation
  • Use this to validate model accuracy through calibration
  • Identify existing constraints before DER growth

2. Explore growth scenarios

  • Model different DER adoption trajectories (conservative, moderate, accelerated)
  • Test varying technology mixes (high solar/low batteries versus balanced growth)
  • Assess different load forecast assumptions (low, medium, high POE)

3. Evaluate interventions

  • Compare base case against intervention scenarios
  • Model system-wide changes (tariff reform, phase rebalancing, DVMS)
  • Test local solutions (community batteries, LV STATCOMs, tap optimization)
  • Use the Intervention Module to systematically assess mitigation strategies

4. Assess sensitivity

  • Vary key assumptions (EV charging patterns, solar adoption rates)
  • Test different forecast network levels to understand data granularity impacts
  • Evaluate probability weightings in allocation strategies

Using Diff Functionality​

The EAS platform supports generating differences between work package results.

This enables:

  • Side-by-side constraint comparisons between scenarios
  • Quantifying intervention effectiveness
  • Identifying which measurement zones improve or worsen under different futures

Exporting and Reporting​

Data Export Workflows​

Results can be extracted via GraphQL for further processing:

CSV Export:

  • Query results and export to CSV for spreadsheet analysis
  • Share data with stakeholders using familiar formats
  • Integrate with existing planning tools and databases

JSON Export:

  • Extract structured data for programmatic processing
  • Feed results into other analytical software
  • Automate reporting workflows with scripting

Report Generation:

  • Combine query results with templates for standardized reports
  • Generate PDF summaries for regulatory submissions
  • Create executive briefings with key metrics and visualizations

Integration with Other Energy Workbench Tools​

OpenDSS Exporter​

The OpenDSS Exporter tool extracts detailed power flow models for specific feeders:

Use Cases:

  • Validate HCM results with independent OpenDSS analysis
  • Perform detailed engineering studies on constrained feeders
  • Test intervention designs in OpenDSS before implementation
  • Generate models for regulatory submissions or third-party review

Workflow:

  • Identify constrained feeders from HCM results
  • Export those feeders as OpenDSS models via the exporter tool
  • Conduct detailed analysis with OpenDSS-specific capabilities
  • Cross-validate findings against HCM constraint data

Feeder Load Analysis Tool​

Combine HCM constraint data with segment-level loading analysis:

Applications:

  • Understand load distribution patterns within constrained zones
  • Identify localized hot spots not apparent in aggregated metrics
  • Correlate customer types with loading patterns
  • Support targeted load management or tariff strategies

Integration:

  • Use HCM to identify constrained measurement zones
  • Apply Feeder Load Analysis to understand intra-zone loading
  • Combine insights for precise intervention targeting

Network Model Calibration​

Use HCM results in an iterative calibration workflow:

Calibration-Results Loop:

  1. Run snapshot calibration to validate model accuracy
  2. Execute HCM work packages with validated model
  3. Compare HCM constraint predictions against actual SCADA data as future unfolds
  4. Refine model parameters (impedances, tap positions) based on discrepancies
  5. Re-run scenarios with improved model accuracy

Benefits:

  • Continuously improve forecast reliability
  • Build confidence in long-term planning projections
  • Identify systematic modeling issues through result validation
  • Enhance trust in capacity assessments for connection applications

Intervention Module​

The Intervention Module works seamlessly with HCM for mitigation planning:

Local Interventions:

  • Community BESS: Battery storage at constrained measurement zones
  • LV STATCOMs: Voltage support at problematic locations
  • Distribution tap optimization: Improve voltage profiles through tap setting changes

System-Wide Interventions:

  • Phase rebalancing: Reduce imbalances across the network
  • Controlled load shifting: Move hot water loads to off-peak periods
  • Tariff reform: Incentivize behavior changes to avoid peak constraints
  • Dynamic voltage management: Active voltage control strategies

Two-Stage Process:

  1. Candidate Generation: Analyze base work package results to identify locations exceeding constraint thresholds
  2. Allocation and Execution: Deploy interventions to highest-ranked candidates within yearly allocation limits, then re-run work packages to measure effectiveness

The Intervention Module produces results using the same framework as core HCM, enabling direct comparison of constraint patterns before and after mitigation.

Best Practices​

Result Validation​

Always validate HCM results against known network conditions:

Historical Validation:

  • Compare base year results against SCADA measurements at zone substations
  • Verify voltage profiles against AMI data where available
  • Cross-check thermal loading against protection relay trip records
  • Use the calibration process to establish model uncertainty baselines

Engineering Judgment:

  • Sanity-check capacity estimates against equipment ratings
  • Verify that constraint locations align with field experience
  • Assess whether constraint timing matches operational observations
  • Question anomalous results and investigate root causes

Convergence Quality:

  • Review power flow convergence rates for each feeder
  • Investigate feeders with low convergence reliability
  • Consider model improvements for problematic network sections
  • Document convergence issues for scenario interpretation

Version Control and Documentation​

Maintain comprehensive records for reproducibility and compliance:

Work Package Tracking:

  • Document scenario configurations for each work package
  • Record which network model date was used
  • Track all input assumptions (profiles, allocations, forecasts)
  • Store work package UUIDs and execution timestamps

Scenario Documentation:

  • Maintain clear descriptions of each scenario's purpose
  • Document probability weightings and allocation strategies
  • Record forecast data sources and assumptions
  • Version control scenario configurations as they evolve

Result Archiving:

  • Retain work package results for regulatory requirements
  • Archive duration curves and constraint summaries
  • Preserve intervention comparison analyses
  • Maintain audit trails for connection assessment decisions

Stakeholder Communication​

Present HCM results effectively to different audiences:

For Technical Audiences:

  • Show detailed duration curves and measurement zone metrics
  • Present CECV/VCR calculations and methodology
  • Discuss convergence quality and model uncertainty
  • Provide equipment-level constraint details

For Management and Executives:

  • Focus on investment priorities and cost implications
  • Use map visualizations to show spatial patterns
  • Summarize scenario comparisons in decision-focused formats
  • Quantify constraint risks with dollar values (CECV/VCR)

For Regulators:

  • Demonstrate methodology rigor and validation
  • Show multiple scenarios to bracket uncertainty
  • Document model calibration and accuracy
  • Provide evidence trails for investment proposals

For Connection Applicants:

  • Present clear capacity availability figures
  • Explain constraint drivers objectively
  • Show intervention costs transparently
  • Provide scenario context when relevant

Troubleshooting​

Empty or Missing Results​

Symptom: Queries return no results or incomplete data

Possible Causes:

  • Work package still processing or failed during execution
  • Querying incorrect scenario name (check spelling and case sensitivity)
  • Equipment MRID doesn't exist in the network model for that date
  • Results database connection issue

Resolution Steps:

  1. Check work package status with getWorkPackageById
  2. Verify scenario name matches scenario configuration table
  3. Confirm equipment MRID exists using Network Explorer
  4. Test database connectivity through EAS
  5. Check for error messages in work package execution logs

Unexpected Constraint Patterns​

Symptom: Results show anomalous constraints or patterns that don't match expectations

Possible Causes:

  • Scenario configuration errors (incorrect allocations or forecasts)
  • Invalid profile data (incorrect normalization or technical parameters)
  • Network model issues (wrong conductor types, missing equipment)
  • Forecast data problems (unrealistic capacity projections)

Resolution Steps:

  1. Review scenario configuration in scenario_configuration table
  2. Validate profile data for proper normalization and value ranges
  3. Check allocation probability weightings sum to 1.0
  4. Compare results against calibration baseline
  5. Inspect network model at constrained locations using Network Explorer
  6. Review forecast input data for reasonableness
  7. Test with simplified scenario to isolate issue

Poor Power Flow Convergence​

Symptom: High failure rates or incomplete results for certain feeders

Possible Causes:

  • Extreme voltage conditions preventing convergence
  • Network model connectivity issues
  • Unrealistic load or generation levels
  • SWER network modeling problems

Resolution Steps:

  1. Review convergence rates in work package metadata
  2. Check for network model errors at problem feeders
  3. Validate forecast magnitudes aren't unreasonably high
  4. Review voltage regulator and tap changer settings
  5. Consider running calibration to improve model accuracy
  6. Inspect OpenDSS-exported model for problem feeders
  7. Consult with Zepben support for persistent issues

Query Performance Issues​

Symptom: Slow response times for GraphQL queries

Optimization Strategies:

  • Add specific filters (year, equipment MRID, scenario) rather than broad queries
  • Request only the fields you need rather than full object graphs
  • Use pagination for large result sets
  • Consider caching frequently accessed results in your application
  • Query during off-peak hours if running large extracts
  • Batch related queries rather than making many individual requests

Next Steps​

Now that you understand how to use HCM results effectively, explore related documentation:

For additional support interpreting results or assistance with complex analyses, contact Zepben technical support.