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Version: 0.5.1

Introduction

Energy Workbench Hosting Capacity Module (HCM)​

The Energy Workbench Hosting Capacity Module (HCM) is a scalable, cloud-based power flow simulation system that enables long-term, whole-of-system modelling of electricity distribution networks, spanning both the Medium Voltage (MV) and Low Voltage (LV) levels. It supports scenario-based forecasting from 1 to 10+ years into the future, allowing electricity distribution network owners and operators to assess the impacts of evolving customer behaviours, Distributed Energy Resources (DER), and network configurations on overall system performance.

Purpose and Role​

The HCM helps operators identify current and future constraints on the network—particularly thermal overloads and voltage limit exceedances—under a range of future energy transition scenarios. This supports informed, proactive long-range network planning and allows utilities to better manage grid curtailment, coordinate demand profiles, and maximise the utilisation of existing assets.

With the rise of CER / DER such as rooftop solar, electric vehicles (EVs), and battery energy storage systems (BESS), as well as enabling technologies that support real-time control or price-based load shifting, traditional assumptions are no longer sufficient, creating significant challenges for network infrastructure originally engineered for operational lifespans exceeding 40 years.

The HCM provides a structured and efficient way to assess how these changing dynamics will affect network performance and where constraints will occur.

Key Features and Concepts​

Scenario-Based Constraint Analysis​

At the core of the HCM is the ability to conduct rigorous, time-series based scenario modelling of network constraints. Operators can explore and compare scenarios involving various combinations of PV, EV, and battery adoption, and assess:

  • When constraints will first emerge
  • How long they will persist over time
  • What patterns (daily, seasonal) those constraints follow

These insights are derived not from overwhelming volumes of raw time-series data but from structured, aggregated metrics that highlight network risks at meaningful levels of resolution.

Network Performance Metrics​

To manage complexity and ensure actionable insights, the HCM quantifies how the network is forecast to perform over time, grouping results by event, time of day, week, season, and year. This provides clear, actionable insights into when constraints first emerge, their total duration, and their daily and seasonal characteristics, without duplicating terabytes of smart meter data.

Measurement Zones​

The HCM introduces Measurement Zones, which are logical groupings of linear network assets. Measurement zones can be established at key points like circuit breakers, switches, reclosers, and transformers. Each zone aggregates the downstream network risk between that point and the next zone, linking the risk directly to the asset ID that defines the zone's start. Because zones run from their defining asset until they meet the next zone or the end of the line, they never overlap. This allows for the accurate aggregation of risk metrics, such as the Customer Energy Curtailment Value (CECV) or Values of Customer Reliability (VCR), without double-counting.

The Base Year Model​

Scenario forecasting begins with a Base Year, which is built from existing historical time-series smart meter data. This allows every energy consumer on the network to be individually represented in the model, creating a high-fidelity starting point. This base year data is loaded once and then intelligently scaled for each forecast year, providing a highly efficient method for constructing future scenarios. The specific forecast EV, PV, and BESS devices are then connected to this scaled network model to complete the scenario.

Integration with the Energy Workbench Platform

The HCM operates within the Energy Workbench platform, which is underpinned by a central, shared Common Information Model (CIM). This ensures that all modules, whether for long-range planning, detailed design, or simulation, are aligned to a single, authoritative network dataset.

The platform's primary role is to provide common network data model services, establishing a single source of truth for network data. Consequently, whether performing high-level analysis in the HCM or generating models for detailed engineering in tools like SINCAL, OpenDSS, PandaPower, and PowerFactory, all modules work from the same aligned and centrally managed network dataset.

Interventions and Mitigation Planning​

Once constraints are identified, the HCM allows operators to explore strategies to resolve them through the Intervention Module. This module models changes to both system-wide and local conditions, enabling comparative analysis of mitigation options against the base case. The Intervention Module is a seperate module within the Energy Workbench platform, designed to work seamlessly with the HCM. Please talk to Zepben about this module if you are interested in using it.

Examples of systemic interventions include:​

  • Phase rebalancing
  • Controlled load power shifting
  • Tariff redesign
  • Dynamic voltage management

Examples of local interventions include:​

  • Installation of community batteries
  • LV STATCOMs (static var compensators)
  • Off-load tap changer optimisation

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