The Grid That Got More Complex Than Its Operators Could Model
The electricity transmission and distribution grid whose operational management determines the reliability of the power supply that every economic activity in a modern society depends on has become substantially more complex to operate over the past decade than the grids whose design principles and operational frameworks were established in the twentieth century for a fundamentally different power system. The twentieth-century grid whose power flow was determined by a small number of large controllable generation units whose output dispatchers could adjust in response to load changes operated in a regime where the physics of power flow were tractable to the computational tools available and where the number of control variables was small enough for experienced system operators to manage with conventional energy management system tools. The twenty-first-century grid whose power flow increasingly reflects the output of millions of distributed solar panels, wind turbines, battery systems, and flexible loads whose individual behaviour is difficult to predict and whose collective effect on grid voltage, frequency, and stability creates the operational complexity that conventional energy management system tools and the training of conventional grid operators were not designed for.
A digital twin of grid infrastructure creates a high-fidelity computational model of the physical grid whose state, updated in real time from the sensor data that smart meters, phasor measurement units, and substation automation systems collect, accurately represents the current operating condition of the physical grid at the level of detail that enables the simulation of what will happen to the grid under different operating scenarios before those scenarios are implemented. An operator who can simulate the effect of switching a specific transmission line out of service for maintenance in the digital twin before issuing the physical switching order knows the post-switching power flow, the voltage profile changes, and the contingency reserve requirements that the switching will create, enabling the maintenance scheduling decision that optimises grid reliability against the maintenance backlog that deferred maintenance creates. A grid planner who can model the effect of connecting a new large solar farm or offshore wind project on the power flows across the existing grid network, including the thermal loading of specific lines and transformers and the voltage regulation requirements at specific nodes, can identify the grid reinforcement investments required for reliable integration of new renewable capacity before the new generation is commissioned and the grid problems it creates become operational emergencies.
GE Vernova and the Transmission Digital Twin
GE Vernova's grid digital twin solutions, built on its PSCAD and PowerOn Advantage grid simulation and management platforms, create the transmission system operator's digital twin whose real-time synchronisation with the physical grid enables the operational and planning applications that the most advanced transmission system operators are deploying. Its work with National Grid ESO in the UK, Terna in Italy, and other European transmission system operators on digital twin implementations for grid stability analysis and renewable integration planning creates the commercial reference base for transmission digital twin deployment that other transmission operators globally evaluate. GE Vernova's grid digital twin combines the power systems simulation expertise accumulated over decades of grid modelling tool development with the real-time data integration and cloud computing infrastructure that modern digital twin implementation requires, creating the combination of domain knowledge and technology capability that grid digital twin projects require from their technology partner.
Siemens Energy's gridscale+ platform, whose digital twin capability models grid infrastructure at the level of individual substation equipment, creates the distribution network digital twin whose applications include predictive maintenance scheduling, fault location, and the distributed energy resource integration planning that distribution network operators whose networks are increasingly stressed by electric vehicle charging loads and rooftop solar generation require. The distribution network digital twin's commercial value is proportional to the cost of unplanned outages that predictive maintenance prevents and the capital cost of grid reinforcement that better-informed distributed energy resource integration planning avoids, creating the return on digital twin investment that distribution network operators can quantify against the operational and capital cost baselines whose improvement the digital twin enables.
The Renewable Integration Challenge
The most commercially urgent application of grid digital twins is the renewable energy integration planning that the energy transition's rapid expansion of wind and solar capacity requires grid operators to perform at a pace and scale that conventional grid planning tools cannot support. Each new large renewable energy project requires a grid connection study whose results determine the network reinforcements required for reliable integration, and the cumulative effect of thousands of simultaneous renewable connection applications across a grid network creates the planning workload that grid operators are struggling to process within the timelines that renewable energy developers require for their project financing decisions. A grid digital twin that can perform connection studies at computational speeds that reduce the weeks-long conventional study process to hours creates the planning throughput improvement that the renewable energy connection queue requires and whose acceleration directly influences the pace at which new renewable capacity can be connected to the grid.
Top 10 Companies in Power Grid Digital Twins Globally
- GE Vernova: US grid technology company with PowerOn Advantage and PSCAD grid simulation platforms for transmission system operator digital twins; its TSO partnerships in the UK, Italy, and other European markets and its real-time grid state estimation create the most commercially advanced transmission digital twin product whose deployment reference base defines the commercial benchmark.
- Siemens Energy: German energy technology company with gridscale+ digital twin platform for transmission and distribution grid infrastructure; its substation equipment modelling and its distribution network digital twin create the utility infrastructure digital twin whose predictive maintenance and DER integration planning applications serve both TSO and DNO customers.
- ABB: Swiss technology company with Ability Energy Manager and grid digital twin capabilities for utility operations; its protection and automation systems that generate the real-time sensor data that grid digital twins consume and its energy management system create the data infrastructure and operational tool integration that grid digital twin deployment requires.
- EPRI: US electric power research institute with grid digital twin research programmes for transmission and distribution utilities; its industry-funded research creates the technology development and testing environment that utility members use to evaluate digital twin approaches before committing to commercial deployment, making EPRI's grid digital twin programme the reference for utility-sector technology assessment.
- PowerSimTech: Norwegian power systems simulation company with real-time grid simulation and digital twin capabilities; its Arene platform and its Nordic transmission operator deployments create the real-time power system simulation commercial position in the European market where renewable energy integration challenges are most acute and where grid digital twin investment is advancing fastest.
- Hitachi Energy: Swiss-Japanese energy technology company with grid management and digital twin capabilities following its acquisition of ABB Power Grids; its Nostradamus AI-powered grid planning tool and its transmission system monitoring infrastructure create the grid digital twin capability in the Asian and European utility markets where Hitachi Energy's grid technology relationships are strongest.
- Oracle Utilities: US enterprise software company with utilities digital twin and network model management products; its network model management for distribution utilities and its integration with customer information systems create the IT-layer grid digital twin that complements the operational technology-layer digital twins that the power systems simulation companies develop.
- Esri: US geographic information system company with utility network digital twin capabilities based on its GIS platform; its ArcGIS Utility Network whose geographic model of the physical distribution network creates the spatial foundation for the distribution system digital twin that distribution network operators use for asset management, fault location, and new connection planning.
- Autodesk: US engineering software company with digital twin capabilities for infrastructure design and management; its Tandem digital twin platform and its infrastructure design tools create the engineering digital twin for grid infrastructure design and asset management whose integration with operational data creates the lifecycle digital twin from construction through operation.
- Bentley Systems: US infrastructure engineering software company with grid infrastructure digital twin capabilities; its OpenUtilities network management and its iTwin platform create the infrastructure digital twin for power transmission and distribution network asset management whose integration of engineering design data with operational sensor data creates the comprehensive grid infrastructure digital twin.