Clock Icon - Technology Webflow Template
min read

GRIDSIGHT'S INSIGHTS Q3 2023 | Dynamic Operating Envelopes

We discovered the most actionable data, network and customer insights around Dynamic Operating Envelopes in Q3 2023.

At Gridsight, our mission is to accelerate the grid's transition to renewable energy. Gridsight’s Insights is our quarterly highlights reel of what we’ve discovered while working with the most innovative and forward thinking electricity distributors.

This quarter, we will delve into our Dynamic Operating Envelope (DOE) algorithm and its application in managing distributed energy resource (DER) related grid constraints. In the past six months, we've retrospectively modelled DOEs on 2,835 solar inverters and have documented their effectiveness in mitigating voltage and thermal constraints.

We hope that you find this report useful. If you would like to learn more about any of the insights covered here, please reach out to us – we would love the opportunity to discuss them further with you.

Here is the downloadable PDF version.


Applying DOEs to Mitigate Thermal & Voltage Constraints

DOEs implement real-time import and export limits, typically on DERs such as solar PV and electric vehicles (EVs), as a solution to mitigate the increasing number of network voltage and thermal constraints.

Thermal Overloads: DOEs calculate loading at each point of the network and allocate maximum export and import limits to ensure thermal compliance for essential equipment. Figure 1 shows an example of a 100 kVA transformer (TX) thermally overloaded due to excess solar. The green line shows the DOE successfully limiting the thermal overload to -100 kVA.

DOEs optimise energy flow by considering both local load and generation. In this example with a 100 kVA TX, the downstream inverters were able to export 150 kVA due to 50 kVA of local demand. If local load decreased, the DOE would adjust to restrict export and mitigate TX overloading.

Across the low and medium voltage grid, DOEs have capacity to resolve a majority of the thermal constraints arising from large amounts of downstream solar generation.

Figure 1: 100 kVA transformer loading with and without DOE

Voltage Constraints: DOEs assess network-wide voltage levels and determine the maximum power each device (at the NMI/AMI level) can consume or export without breaching voltage limits. Figure 2 illustrates the effect of applying an individual-optimised DOE to maintain the voltage of the network within acceptable limits (216 V–253 V).

When simulated on 2,835 inverters, DOEs resolved 50% of voltage non-compliance events for smaller systems and up to 80% for larger systems – see Figure 3. With customers increasingly opting for larger PV systems, which can lead to greater voltage non-compliance, DOEs will fulfil a significant role in addressing these voltage challenges whilst maximising available export.

Figure 2: DOE application to reduce voltage constraints
Figure 3: DOE effectiveness in reducing voltage constraints for residential solar PV inverters


Model-Free DOEs

Application of DOEs requires accurate and fast determination of the network state (voltages and loading) to identify the available hosting capacity. Traditionally, this has been based on static load-flow calculations which are computationally intensive and require an accurate understanding of the network loads, conductors and configuration, which are often not readily available.

Gridsight presents a new machine learning based approach to hosting capacity estimation, making use of a model-free representation of the network to discern the available thermal and voltage headroom. Gridsight’s approach has been rigorously tested using historical data sourced from two utilities producing 4,516 model-free networks. This extensive training has resulted in an average accuracy of ± 1 V when predicting network voltages. Figure 4 shows the comparison between the measured voltage and the model-free network voltage estimation.

Figure 4: Comparison of measured voltage and model-free estimation

The key benefits of using a model-free DOE are:

  • Faster and more accurate hosting capacity calculations.
  • Improved scalability and implementation in real-time.
  • Effective with smart meter penetrations of only 20%.


Benefits & Savings for Customers & Utilities

To assess the effectiveness of using DOEs to mitigate both thermal and voltage constraints, we retrospectively simulated DOEs on 2,835 single phase solar inverters with installed capacities between 3 kW–10 kW. We enforced a DOE lower bound, or minimum export limit, of 3 kW.

Benefits for Customers: DOEs unlock additional solar energy, especially for larger PV systems curtailed by 5 kW static export limits. With customers continually embracing larger solar installations, DOEs will become vital in accessing additional solar energy potential. As observed in Figure 5, this can exceed $250 AUD annually for a 10 kW solar system (based on a 5.5 c/kWh feed-in tariff).

Energy Curtailment: Among the 2,835 inverters, only 15% encountered any curtailment due to DOE implementation. We found that DOEs delivered a strong net benefit for all customers by releasing significantly more energy than they curtailed – see Figure 6.

Figure 5: Benefits unlocked for customers with DOEs
Figure 6: Energy released and curtailed by DOE

Savings for the Utility: Critical infrastructure upgrades are capital-intensive and are being accelerated by increasing volumes of DERs. DOEs offer a cost-effective and flexible alternative, alleviating many thermal and voltage constraints in bottlenecked regions of the grid. This decreases pressure on immediate network upgrades, allowing utilities to focus on critically overloaded equipment and strategically allocate capital expenditure by facilitating gradual grid improvements over several years.


Gridsight helps electrical utilities transition to a decentralised grid by generating actionable, AI-powered network insights. These insights enable utilities to dramatically reduce the network augmentation required to safely and efficiently support more residential solar, batteries and electric vehicles. Based on CEO Brendan Banfields PhD research, Gridsight was founded in 2020 to accelerate the transition to renewables.

Brendan Banfield

Co-Founder, CEO

Passion for research and innovation related to integrating renewable energy technologies into distribution networks using data.