Powering a Smarter Grid: IoT and Digital Twins in Energy Management

The energy sector, particularly the power grid, is emerging as a prime candidate for digital twin adoption, due to the scale and complexity of the infrastructure involved.

With their intricate web of generators, transmission lines, substations, and end-user locations, power grids present challenges and opportunities perfectly suited to the capabilities of digital twins and perfectly enabled by IoT.

What are digital twins in energy management?

For power grids and energy management, digital twins offer a comprehensive view of the power network in a model that can be manipulated to simulate certain conditions. On a granular level they can digitally represent individual components like transformers and substations, and on a macro level can model the entire distribution network, to assist in more efficient operation and problem resolution.

In energy management, digital twins are used to improve real-time monitoring and enable predictive maintenance, grid stability, and disaster resilience.

These high-fidelity virtual replicas of grid assets and systems help ensure more reliable service in the face of evolving energy challenges.

Understanding the digital twin concept

The digital twins of today have greatly evolved from their early role as models and simulations.

The concept has roots in NASA’s Apollo program, where engineers used physical “twin” systems to test and diagnose problems remotely. The modern term “digital twin,” however, is widely credited to Dr. Michael Grieves around 2002. NASA later helped popularize it, applying the idea to digital spacecraft modeling.

By the early 2000s, the term ‘digital twin’ was commonplace inside NASA and beginning to take hold elsewhere. But it wasn’t until the advent of IoT that the concept moved beyond disconnected simulations to a model capable of ingesting live rich data from multiple sources and sensors to monitor, diagnose, and predict system behavior.

With IoT forming the critical link between physical production systems and their simulated counterparts, digital twins are now appearing in several industries, including:

• Energy and utilities - for power grid and infrastructure management
• Construction - for smart building construction and management
• Mining and drilling - for safety and surveying
• Agriculture - for crop management and weather simulation
• Smart cities and municipalities - for urban planning and traffic flow

Digital twins in the oil, gas, energy, and water sectors combine physics-based models of generators, inverters, and protection systems with real-time measurements from critical infrastructure components, continuously mirroring the operational state of physical assets.

The scale of these environments can range from a digital copy of an individual asset like a transformer to a comprehensive model of the entire distribution network. By modifying and manipulating these models, utilities can diagnose and predict grid behavior with unprecedented accuracy, facilitating informed decision-making across planning, operations, and response teams.

Benefits of a digital twin in energy management

Real-time visibility into grid health and operation

Digital twins help grid operators identify and address issues before they escalate into outages.​

Predictive analytics

Digital twins monitor equipment for issues and help utilities schedule proactive maintenance, reducing downtime and extending equipment lifespans.​

Scenario analysis

Digital twins enable utilities to simulate the impact of various events—such as demand spikes, severe weather, localized or mass outages—or how the integration of renewables will affect grid stability and resource allocation.​

Grid planning

Digital twins can simulate expansion plans through data modelling, identifying investment prioritization and efficient responses to growing demands for power and adoption of new technologies like electric vehicles and distributed energy resources, such as solar panels.​

Components of a digital twin

IoT-based digital twins replicate a physical system’s state, behavior, and architecture. They have only begun to appear in the oil, gas, and energy sectors in the last few years, and they are typically composed of five dimensions:

• Physical Entity: An object which, through IoT sensors, provides information on its behavior, states, and characteristics.

• Virtual Model: For each physical object, there exists a digital replica that can model the object and its behavior and is critical to provide deep insights into the physical entity via simulations.

• Data: These objects provide valuable information for modelling, simulation, optimization, and predictions, with historical and real-time data used to manipulate the environment and generate decisions for the digital twin.

• Connections: Connectivity for the IoT sensors is critical to enable all elements in the digital twin because both the real and virtual counterparts interact with each other and other entities through both physical and digital spaces. Without dependable IoT connectivity, a live digital twin cannot function effectively.

• Services and applications: These encapsulate the functionalities provided by the digital twin, as users or controllers only provide inputs, typically from IoT sensors. The results of the functions and simulations executed are generated by cloud applications, modelling and analysis.

The critical role of IoT in digital twins for energy management

Establishing the topology of the electrical grid is key in any power system digital twin, because the accurate representation of the electrical grid must be known in its current state to efficiently operate the grid and build a reliable twin.

Building a digital twin for a power grid requires two basic types of models:

Equipment models

These describe the functions of individual devices on the grid, each with their own specific behaviors.

Instances of a digital twin could include mapping for potential future equipment changes, which may alter generation sources or consumer loads.

Grid models or connectivity models

These describe how equipment is connected into a cohesive system, essential for full-scale grid simulations.

These models map key elements such as the terminals associated with each piece of equipment. A distribution line has a single terminal on each end, for example, while a busbar might introduce multiple terminals. And each terminal connection must be tracked.

If you consider that the topology of a power grid is also a meshed system, you can see that this makes the connections and distribution of energy very difficult to track.

IoT sensors

IoT devices are integrated into both equipment and connectivity in the physical system, and the data they generate is used to build the digital twin and generate the variables for simulation.

IoT devices in power grids include those on the consumer premises, such as smart meters, through to phasor measurement units (PMUs), remote terminal units (RTUs), and distributed sensors throughout substations and transmission networks.

IoT versus SCADA

SCADA (Supervisory Control and Data Acquisition) remains foundational for real-time control of industrial processes. IoT doesn’t replace SCADA, it extends it by offering higher-frequency data collection, new device classes, and powerful analytics.

PMUs typically capture 30–60 data samples per second, while SCADA and RTU systems refresh roughly every 2–6 seconds. This increase in temporal resolution gives operators much deeper situational awareness and faster response capabilities.

Data collection and real-time management

This dramatic increase in data granularity enables digital twins to detect subtle anomalies in power grid performance and predict equipment failures with significantly greater accuracy than legacy technologies.

Meanwhile, smart meters installed on consumer premises usually record data in 15–30-minute intervals (configurable by utility), a major improvement over daily or monthly reads. This enables utilities to better balance load and offer consumers more accurate, usage-based billing.

Data from IoT devices travels over cellular or LoRaWAN® networks using lightweight protocols such as MQTT or CoAP, secured through TLS or DTLS encryption, before reaching cloud or edge platforms.

Edge computing allows immediate, local analytics for real-time decision-making, reducing latency and bandwidth costs across complex network topologies.

Applications of digital twins in the energy industry

Equipment monitoring

Utilities use digital twins to monitor individual equipment, such as transformer temperatures and predict insulation failures or identify failing cooling systems.

Predictive maintenance

The ability to predict equipment failures and action preventative maintenance needs before problems arise is a game-changer for power grid management. Digital twins harness historical and real-time data to create predictive models and schedule maintenance to prevent outages and reduce operational costs.​

Grid optimization and resilience

Digital twins provide a panoramic view of the network, facilitating efficient operation and rapid problem resolution, with accurate modelling of power load.

During events like hurricanes, floods, or heatwaves, digital twins help utilities model the potential effects on grid infrastructure, ensuring rapid response and targeted resource deployment, improving resilience.​

Renewable energy management

As power grids increasingly incorporate variable renewable energy sources, digital twins play an essential role in managing the resulting complexity.

Distributed Energy Resources, or DERs, are small-scale power generation and storage often involving technologies like solar panels, wind turbines, and batteries. In many cases, DERs are consumer-owned—think roof mounted solar panels, or an EV in the driveway—and they significantly increase the complexity of demand-side control by introducing more generation assets.

Distribution grid operators leverage digital twins to optimize the integration of rooftop solar, heat pumps, and electric chargers, to model the behavior of grids under various solar and wind generation scenarios, determine optimal locations for renewable installations, and predict potential voltage fluctuations.

Smart grid optimization

Digital twins are rapidly evolving from simulation tools into operational platforms that provide actionable intelligence and holistic optimization for the grid.

As smart grid and smart city applications merge, increasing investments are coming in areas such as distribution automation, DERs, electric vehicle (EV) charging, and smart street lighting, and IoT is a key enabler.

SCADA systems, though older, remain foundational for many utilities. Yet, the integration of advanced IoT technologies is transforming these traditional architectures. Remote terminal units now serve as electronic remote-control devices enabling communication between grid elements and distributed control software, helping grid operators prevent critical issues through real-time monitoring.

The European TwinEU project

Launched in January 2024, TwinEU is creating the concept of the Pan-European digital twin based on the federation of local twins of the electricity system. ​​

With 15 countries and 75 partners currently involved, this initiative demonstrates the trajectory toward increasingly sophisticated digital twin implementations for power grids that integrate multiple data sources and stakeholders.​

The initiative champions the consideration that interoperability between digital twins and legacy systems must be ensured to unlock the potential of distributed flexible assets and enhance energy system resilience.​

Challenges and considerations

Successful digital twin implementation in energy management requires quality data integration from diverse equipment, because inconsistent or outdated data can diminish the accuracy and value of digital twin models.​

This creates challenges and opportunities in the abstraction layer, as data from different substations may be recorded in varying formats, leading to challenges in normalizing data for accurate predictions.

Data governance and standardization are critical concerns. The adoption of harmonized communication protocols and standardized data formats enhances data exchange and integration while ensuring data quality and security.

As is true of most IoT-driven initiatives, cybersecurity is paramount, as connected devices and communication infrastructure create expanded attack surfaces for malicious actors, and the potential for disruption of critical infrastructure should not be underestimated.

Grid decarbonization and sustainability goals

Under the 2015 Paris Agreement, nearly 200 countries committed to limit global temperature rise to well below 2°C and to pursue efforts to stay within 1.5°C. According to the IPCC, achieving this pathway requires cutting global CO₂ emissions by around 43% by 2030 (from 2019 levels) and reaching net zero by mid-century.

Digital twins, smart grids, and IoT are central to this ambition. While delivering emissions reductions is one challenge, accurately measuring and verifying progress is another.

By improving energy efficiency, enabling predictive maintenance, conserving electricity, reducing waste, and creating more flexible, data-driven operations, IoT-enabled smart grids make decarbonization both measurable and achievable.

The future of energy management with digital twins

The complexity and urgency of contemporary energy sector challenges necessitate an acceleration in grid operators’ adoption of digital twin technologies.

Digital twins represent a transformative technology, not just for power grid management, but for achieving the EU’s 2050 climate targets, with IoT serving as the essential data collection infrastructure that enables their function.

Digital twins, leveraging artificial intelligence and IoT applications, will play a key role in enhancing predictive analytics, real-time monitoring, real-time problem detection and system planning for energy management.

However, successful implementation requires addressing cybersecurity concerns, consistent data quality and standardization, and balancing the competing demands of security and availability that characterize critical power infrastructure.

Eseye’s IoT cellular connectivity solutions are essential for smart and sustainable deployments of digital twins, ensuring that all sensors and smart devices are reliably connected, which is crucial for real-time data collection and decision-making.