Grid Management Software: Orchestration platforms
The proliferation of Distributed Energy Resources (DERs) makes grid stability a challenging issue. Grid operators need to know what is happening at the edge of the grid. Could a fleet of EVs overload a specific transformer or conductor? How might a set of DERs coming online affect outage restoration? ADMS1 and DERMS2 do not have the capabilities to answer these questions.
Grid orchestration fills this gap. By providing real-time and high-fidelity data of local grid conditions, grid orchestration enables operators to understand how specific DERs impact grid reliability.
Core to the idea of grid orchestration is the Distribution System Operator (DSO) concept. A DSO manages the grid at a local level through high quality telemetry of assets and forecasting loads at every node. It dispatches assets to maintain a reliable flow of electricity and can enable local resources to participate in grid-services markets such as peak load reduction.
Bridging the Grid Visibility Gap
Why can’t an ADMS and DERMS provide high-fidelity visibility into local grid conditions? An ADMS doesn’t have a view of all meters and has limited telemetry across the network. It typically only sees a static load profile for each transformer, based on the type of meters it serves (residential, commercial, industrial). This makes the power flow equation3 easier to solve but with the growth of DERs, an ADMS lacks insight into accurate electricity demand across parts of the grid.
Grid orchestration providers such as Camus4 gather all meter data to give a view into what’s happening at the edge. This is aggregated back to where the ADMS ends, providing utilities and grid operators with a more data-driven view of the grid.
DERMS focuses on monitoring and control of DERs. They typically don’t know about local grid conditions near DERs. This makes dispatching assets tricky as operators need to switch between multiple systems to understand what can be done safely5.
An orchestration platform integrates data from multiple systems to give operators a comprehensive view of grid conditions. It maximises the value of DERMS investments “by enabling precise, grid-aware dispatch across all resource types”6.
Case studies
Whilst each of the organisations below offer grid orchestration capabilities, they also provide DERMS and, in the case of GE Vernova, an ADMS. Kraken leans much more into the DERMS space than Camus. In fact some of the features each company offers can be complementary. For example, Kraken could use the grid context Camus provides to plan and dispatch the right assets to maintain grid stability.
Camus Energy
Camus’s vision is to enable US utilities to take on the DSO role:
Since we've gone to the trouble of describing a DSO and what it does, our goal is really to create a software platform that will enable a utility to take on that role. So a much more real time and local operations model that can include local resources as part of the supply and demand landscape and ultimately include them into capacity management and network management for the grid and let them get paid for it7.
Their grid orchestration platform integrates with multiple systems to provide a live view of what’s happening at the grid edge8. An advanced meter forecasting capability means the platform can predict loads at every point on the grid helping to inform “how the ADMS executes outage restoration, fault location, automated switching, and other distribution optimization activities”9. The platform can also dispatch all types of DERs including those managed by non-utility aggregators and helps utilities plan their systems by surfacing DER profiles.
Software platform
Camus deploys their software on Google Cloud Platform10. Services primarily written in Python ingest data from systems like ADMS and SCADA and are fed into BigQuery11. Camus uses NOAA weather forecasts12 for their meter forecasting that is also captured in BigQuery.
So, BigQuery sits at the centre of the software platform with many downstream consumers. Camus’s forecasting system reads and writes to BigQuery. It feeds time series data into BigTable13 for latency sensitive applications. A Postgres database is used for everything else - grid models, geodatabase deliveries and utility-specific data models.
A control server written in Go enables dispatching of DERs through vendor-specific code. A Vue web app overlays a utility’s grid on Google Maps. Users can see all substations, transformers, meters and DERs, and view real-time telemetry and forecasted load for each device14.
AI
Using data from utilities’ Geographic Information Systems (GIS), smart meters and NOAA weather forecasts, Camus’s supervised machine learning system with XGBoost can provide accurate forecasts for millions of meters15. XGBoost models also have explainable results and tools such as SHAP16 enable users to understand how much an input to the model contributed to the output.
ML can also inform power flow models. The models include solvers that attempt to solve the model’s equations by starting with an initial guess and refining until it achieves a desired accuracy. This is a very computationally intensive process. ML forecasts can make the process much faster:
initial guesses provided to the solver are much closer to reality which in turn give the power flow solver a head-start in solving its physics calculations. This framework significantly decreases the computational effort and at the same time, provides visibility at the midpoints of the distribution grid with high fidelity.17
Kraken
Kraken18 provides an operating system for energy. Their platform, deployed on AWS, combines advanced grid management with DER optimisation. It provides utilities and operators with comprehensive monitoring and control of distribution grids, offering real-time visibility into network performance, power quality, and fault detection.
Their system excels in integrating and managing a diverse range of behind-the-meter assets, including electric vehicles, heat pumps, and home batteries, while enabling the creation of smart tariffs and products that help balance grid demand. Through its data analytics capabilities, Kraken helps predict demand, prevent outages, and optimise distribution, ultimately improving grid resilience while delivering cost savings to consumers and enabling the smooth integration of renewable energy sources into existing infrastructure.
In collaboration with a Scottish Distribution Network Operator (DNO), Kraken developed a model of how electricity load is dispatched across the DNO’s network19. This helped inform the launch of a dynamic distribution grid tariff so that DERs could be orchestrated to operate at the “precise times and locations where distribution constraints arise”.
GE Vernova Grid OS
GE Vernova also aggregate data from multiple systems as part of their Grid Orchestration platform20. As well as utility GIS and asset topology data, they incorporate weather forecasts, satellite imagery, wildfire monitoring and vegetation data. This enables operators to understand risks to the grid and how DER management could overcome peaks in demand. Like Camus and Kraken, GE Vernova aim to provide real-time telemetry and forecasted load across the network so operators can more effectively manage the grid.
Grid Orchestration: The Missing Link in the Modern Grid
The emergence of grid orchestration platforms marks a crucial evolution in grid management, filling gaps that ADMS and DERMS cannot address alone. Companies like Camus and Kraken are leading this transformation by leveraging machine learning and cloud computing to create platforms that can predict and respond to grid conditions with unprecedented accuracy. By integrating multiple data streams with real-time telemetry, these solutions not only solve immediate grid management challenges but enable the transition to a decentralised energy future.
The VPP Insiders YouTube channel had a great talk from Camus about this
I’m indebted to Cody Smith, CTO of Camus, for taking the time to explain Camus’s software platform to me
Camus have a presentation demoing the web app in collaboration with Holy Cross Energy