Case Study Details - Future of Power Systems

Digital twin grids create a virtual representation of power-system assets and near real-time or periodically updated operational conditions to support improved grid planning, asset management and operational decision-making. By replicating the behavior of transmission and distribution networks under a wide range of scenarios, these models allow operators to visualize system performance, simulate future states and coordinate actions more proactively.

Modern digital-twin platforms integrate data from grid-management systems, sensors, geospatial models and asset registries into a unified digital environment. Combined with advanced simulation tools, AI-assisted analytics and automated data validation, they provide deeper situational awareness and help predict emerging equipment issues, identify constraints and assess energy-flow patterns. In addition to operational simulations, digital twins can represent physical system constraints such as thermal limits, voltage stability, protection settings and congestion, and can model planned and unplanned outages using N-1 and N-k contingency analysis under varying weather and demand conditions. When connected to planning or cost-assessment tools, they also support evaluation of reinforcement options, project sequencing and investment impacts. As systems become more complex – due to distributed energy resources, electrification and extreme-weather risks – digital twins help utilities transition from reactive maintenance to predictive, data-driven operations.

Main outcomes of the solution

Reliability & Flexibility: Improves operational reliability by enabling operators to test outages, switching actions, congestion events and extreme-weather conditions in a virtual environment before they occur; supports faster operational decisions, more accurate contingency planning and system stabilization during rapidly changing conditions.
System Resilience: Identifies weak points in the network – such as overloaded corridors, ageing assets, protection-setting issues or DER-driven congestion – through continuous simulation and model validation. This enables more targeted reinforcements, predictive maintenance and reduced unplanned downtime, improving readiness for climate-driven stresses.
Institutional Efficiency & Transparency: Provides a single, authoritative digital model that aligns planning, operations, protection engineering and regulatory reporting; eliminates duplicate modelling efforts, reduces human error, ensures consistent model updates across departments, and supports transparent decision-making and compliance processes.

Key deployment and scaling factors

Consolidate grid data into a single digital model by integrating SCADA/telemetry streams, GIS network topology, protection and switching schemes, asset registries and historical operational records into a standardized and validated network model.
Build high-resolution simulation and scenario-analysis capabilities to model power flows, contingency events, DER integration, switching actions, extreme-weather impacts and planned infrastructure upgrades under both real-time and hypothetical conditions.
Deploy advanced analytics and AI/ML tools to detect abnormal operating patterns, predict asset-health issues, prioritize maintenance and support risk-based planning across transmission and distribution networks.
Implement strong data governance, cybersecurity and interoperability frameworks so the digital twin connects seamlessly with existing utility systems (EMS, DMS, OMS, GIS, ADMS, asset-management systems) while ensuring secure handling of operational data.
Set up continuous model-maintenance processes that reconcile the digital model with field changes, asset updates and regulatory requirements.
Establish rigorous model-calibration and parameter-validation procedures to ensure the digital twin remains accurate as system conditions evolve.

Case Study

AcegasApsAmga and Siemens (2025), Italy

In 2025, the Italian distribution system operator (DSO) AcegasApsAmga began deploying Siemens’ Gridscale X platform to build a full digital twin of Trieste’s medium- and low-voltage electricity grid. The platform integrates network data, smart-meter information, outage insights and distributed-energy resource (DER) visibility to provide enhanced situational awareness of distribution-grid conditions.

Through its simulation and analytics capabilities, the digital twin supports the assessment of emerging congestion, load patterns and the energy required to maintain grid stability during periods of increased demand. These capabilities are particularly relevant for the electrification of Trieste’s port, which handles more than 70 million tonnes of cargo annually and requires reliable grid capacity for expanding shore-side power and other electrification initiatives.

The deployment forms part of the city’s broader strategy to prepare its urban and port infrastructure for increased electrification and distributed-energy integration, demonstrating how advanced modelling and operational analytics can support more proactive and coordinated distribution-grid management.[1,2]

eRoots Analytics (2025), Spain

eRoots Analytics is developing The Map, a planning-oriented digital grid twin that integrates network topology, market data and power-system analytics to support energy developers, power producers and market players in assessing hosting capacity, congestion risks and grid-connection options earlier in the project cycle. The platform simulates the Iberian market, identifies likely grid constraints, forecasts potential curtailment and evaluates the technical viability of connection points.

According to internal analysis provided by the company, the underlying simulation engine has been applied across Iberia in support of developers representing more than 3 GW of renewable capacity. Internal validation exercises report alignment between model outputs and TSO data for selected locations, and the tool has been used in studies commissioned by developers relating to national-scale HVDC and interconnection planning tasks.

The Map is currently in a beta testing phase with industry partners, who are providing feedback to refine analytical depth and usability. Early internal testing suggests that automated grid-screening processes may reduce initial feasibility assessments from several weeks to under 24 hours, potentially accelerating project pipelines where sufficient organizational capacity exists. Its modular architecture allows adaptation to different regulatory and grid-data environments.[3]

American Electric Power (AEP), Siemens Advanta and Xtensible (2022), USA

American Electric Power (AEP) is implementing the T-Nexus program to modernize and centralize transmission system model management across its 11-state service territory. Working with Siemens Advanta and Xtensible, AEP is building a unified digital representation of its transmission network that consolidates planning, operations and asset information into a single high-quality system model.

T-Nexus establishes an authoritative “source of truth” for network models by integrating data from multiple legacy systems, standardizing model formats, and improving validation processes. This harmonized digital model supports more accurate planning studies, reduces manual inconsistencies and streamlines regulatory reporting. Siemens’ grid-software platform provides advanced model management and validation capabilities, while Xtensible delivers enterprise data-architecture and utility information-modeling expertise.

By improving data quality, automating updates, and enabling cross-functional collaboration, T-Nexus enhances outage analysis, compliance activities, and system-planning workflows. It demonstrates how large utilities can move from fragmented, manual model maintenance to integrated, digital environments that support more proactive and coordinated grid management.[4,,5,6,7]

Energy Market Authority (EMA) Singapore and SP Group (2021), Singapore

SP Group, Singapore’s national grid operator, is developing a comprehensive Digital Grid Twin under the oversight of the Energy Market Authority (EMA) and in collaboration with research partners – the Institute of High Performance Computing (IHPC), TUMCREATE and Nanyang Technological University (NTU). The initiative comprises two complementary components: an Asset Twin, which represents the condition and performance of grid assets such as substations, transformers and cables, and a Network Twin, which simulates electrical-network behavior under evolving demand, new grid connections and evolving grid conditions.

The prototype launched in 2021 integrates historical and near–real-time operational data from multiple systems. It is intended to support remote assessment of asset health, reduce reliance on physical inspections and provide network-impact analysis when integrating new loads or distributed energy resources (DER) such as EV chargers and rooftop solar. This supports more informed maintenance planning and infrastructure-upgrade decisions as electrification and DER adoption increase.

The system models the national grid serving 1.7 million electricity consumers in Singapore and is designed to support long-term grid resilience. By simulating new demand patterns, infrastructure stress points and DER hosting capacity, the Network Twin enables planners to identify reinforcement needs earlier and better assess operational risks. Public communications from EMA and SP Group indicate that ongoing development will focus on improving predictive analytics, situational awareness and coordination across planning and operations functions.[8,9,10]

National Energy System Operator (NESO) (2021-ongoing), United Kingdom

The UK’s National Energy System Operator (NESO) is developing the Virtual Energy System as part of its wider digitalization program. The Virtual Energy System is intended to support analysis of Great Britain’s energy system by enabling coordinated modelling of electricity, gas, storage, demand and flexibility assets across planning and operational time horizons.

Rather than operating as a single central model, the Virtual Energy System is conceived as a federated digital-twin approach, in which different organizations retain responsibility for their own models while aligning them through common data standards, interfaces and governance arrangements. This supports greater consistency across system-planning, operational and market analyses and helps reduce duplication of modelling effort across the energy sector.

The Virtual Energy System is supported by NESO’s Data Sharing Infrastructure (DSI), which provides the underlying mechanisms for secure data access and exchange between participants. The DSI enables standardized data interfaces, access controls and governance arrangements that allow energy-system data to be shared and reused across organizations, including in system-modelling and analytical tools developed by NESO and third parties.[11,12,13]

FlexOps (2023), Norway

Falkor (formerly Kongsberg Digital) worked with Norwegian distribution system operators Elvia, Linja and Tensio, alongside SINTEF Energy, the Norwegian Smart Grid Center and technology provider Pixii, to develop and test a digital twin for integrating flexibility into grid operations. The resulting Falkor Grid module combines grid models, switch settings, historical load and generation measurements, and sensor data in a virtual representation of the network. It provides an always-on grid simulation showing current load flows and forecasts for the next 48 hours, allowing operators to identify emerging bottlenecks and simulate whether flexible resources could resolve them.

In Lierne, Norway, the platform was also connected via APIs to a 1 MW/1 MWh battery supplied by Pixii and operated through Peak Shaper, enabling battery data to be viewed alongside grid capacity and load-flow information. Separate testing of the battery found a 22.7% reduction in active power losses during reactive-power support, while Norway's energy regulator estimates that the installation can increase local grid capacity by approximately 30%. The case illustrates how digital grid modelling can help operators identify constraints and assess the use of flexible assets as an alternative or complement to conventional grid reinforcement. [14,15,16]

References:

AcegasApsAmga

Siemens

eRoots Analytics

American Electric Power Company

Siemens Advanta Consulting

Xtensible

EMA Singapore

SP Group (Singapore Power)

National Energy System Operator (NESO)