Table of Contents
Introduction
Digital twins and virtual power plants are two closely related ideas that show how digitalization can transform modern energy systems. Both use data, models, and communication technologies to monitor and control physical energy assets in smarter ways. They are especially important when more renewable and distributed energy resources are added to power systems.
What Is a Digital Twin in Energy?
A digital twin in the energy sector is a dynamic digital representation of a physical energy asset, system, or process. It stays connected to the real system through data streams. This connection allows the digital model to update as conditions change and to simulate how the real system might behave under different scenarios.
In energy, digital twins can represent a single wind turbine, a solar plant, a battery system, a building, or even an entire section of a grid. Sensors on real equipment measure variables such as temperature, vibration, voltage, and power output, then send these data to the digital twin. The twin uses these data, along with physical and statistical models, to estimate current conditions and predict future behavior.
A key difference between a simple computer model and a digital twin is the continuous link with the real asset. The twin is not static. It is constantly updated with real-time or near real-time data, so it reflects the current state of the system.
Core Elements of a Digital Twin
A digital twin typically includes three main elements. First, there is a detailed digital model of the physical asset or system. This model may include engineering design information, performance characteristics, and ideal operating ranges. Second, there is a data connection that feeds measurements from the real asset into the model, often through sensors and communication networks. Third, there are analytics and visualization tools that use the data and model to generate insights.
With these elements, a digital twin can calculate quantities that are not directly measured, such as internal stress in a turbine component or the remaining life of a battery. It can also test hypothetical situations. For example, it can answer questions such as what happens to power output if wind speed changes, or how a battery will respond to a new charging strategy.
Applications of Digital Twins in Renewable Energy
Digital twins are particularly useful for renewable energy technologies, which are influenced by variable natural resources. For wind turbines, a digital twin can help predict failures before they happen. By comparing expected behavior from the model with actual sensor data, operators can detect abnormal patterns, schedule maintenance when it is most effective, and avoid unplanned outages.
In solar photovoltaic plants, a digital twin can combine information about the design of the plant with real-time data on solar radiation, temperature, and inverter performance. It can estimate how much power the plant should produce under current conditions. If measured output is lower than expected, the twin helps identify whether the cause is shading, soiling of panels, inverter faults, or other issues.
Batteries and other storage systems also benefit from digital twins. Predicting the remaining useful life of a battery is complex, because performance depends on temperature, cycling patterns, and age. A digital twin can track the history of each battery, simulate internal processes, and provide better estimates of degradation. This is important when storage is used as part of larger systems such as virtual power plants.
Grid-Level Digital Twins
Beyond individual assets, digital twins can represent sections of the electricity grid. A grid digital twin can map lines, substations, switches, and loads, along with distributed energy resources such as rooftop solar and electric vehicles. With continuous data from smart meters and sensors, the twin can estimate power flows, voltages, and possible overloads at many points in the network.
This allows operators to test how the grid would respond to changes such as adding new distributed generation, connecting a new industrial load, or changing control settings. They can evaluate different configurations in the digital environment before applying them in the real grid. This reduces operational risk and supports planning when more renewable and distributed generation is connected.
Digital twins rely on accurate data and robust models. Incorrect data inputs or poorly calibrated models can lead to flawed predictions and suboptimal or even unsafe operational decisions.
What Is a Virtual Power Plant?
A virtual power plant, often abbreviated as VPP, is a coordinated group of distributed energy resources that are operated together as if they were a single power plant. These resources can include rooftop solar systems, small wind turbines, battery storage, electric vehicles, controllable loads, and sometimes backup generators.
The word “virtual” indicates that the power plant is not a single physical facility at one location. Instead, it is a digital concept built through software, communication networks, and control systems. The virtual power plant can receive signals from many small assets, decide how they should respond, and send commands back to them. From the view of the electricity market or grid operator, the VPP can provide services similar to a conventional power plant.
Components and Operation of a Virtual Power Plant
A virtual power plant typically involves three key groups of components. First are the distributed energy resources themselves. These are the physical systems that can generate or consume electricity or store it. Second is the communication and control infrastructure. This includes smart controllers, meters, and communication links that allow data to flow from the resources to a central platform and control signals to flow back. Third is the VPP control platform, which is usually a software system that aggregates data, optimizes operation, and interacts with external markets and grid operators.
To operate, the VPP collects real-time information from each asset, such as available solar power, battery state of charge, or current household consumption. It also receives external information, such as electricity prices, grid congestion warnings, or renewable generation forecasts. Using optimization algorithms, the VPP decides how each asset should act in the next period, for example how much battery capacity to charge or discharge, or whether to increase or reduce flexible loads. These decisions aim to reach objectives such as maximizing revenue, supporting grid stability, or reducing emissions.
Services Provided by Virtual Power Plants
Virtual power plants can participate in several parts of the electricity system. In some markets, they can sell electricity into wholesale markets by aggregating the power output of many small generators. They can also provide balancing and ancillary services by increasing or decreasing their net power injection into the grid when requested. For example, a VPP can quickly discharge its collective batteries to help stabilize frequency after a disturbance.
Virtual power plants can also support local network operations. In areas with high rooftop solar, coordinated control of batteries and flexible loads through a VPP can reduce voltage problems and line congestion. Instead of building more physical grid infrastructure, network operators may contract services from a VPP that can adjust local injections or consumption.
For customers, participation in a VPP can create new revenue streams or cost savings. A household with a rooftop solar system and a battery can earn income by allowing the VPP to use part of the battery capacity to provide grid services. Industrial and commercial customers can also offer flexible loads, such as refrigeration or process heating, that can be adjusted within limits to support the VPP.
Virtual power plants must respect technical and contractual limits of each asset. Overusing customer batteries or disrupting industrial processes beyond agreed boundaries can damage equipment, reduce customer trust, and undermine the VPP business model.
Interaction Between Digital Twins and Virtual Power Plants
Digital twins and virtual power plants complement each other. A VPP depends on accurate knowledge of the state and capabilities of its many assets. Digital twins can provide this information in a structured way. For example, a digital twin of a battery can estimate its degradation and safe operating limits more precisely than simple rules. A VPP that uses this twin can schedule battery use so that it provides valuable services without shortening the battery’s life too much.
For a group of assets, a digital twin can represent the expected behavior of a neighborhood energy system. The VPP can then simulate future scenarios within the twin, such as high solar output combined with low demand, and plan control actions ahead of time. This improves reliability and allows more aggressive use of flexibility while still protecting system stability.
Similarly, grid digital twins can interact with VPPs. Before the VPP takes an action that changes injections in certain parts of the grid, operators can use the grid twin to check whether this action will cause voltage or congestion problems. This creates a feedback loop where physical actions, digital models, and control decisions inform each other.
Benefits for Renewable Integration
Digital twins and virtual power plants together support higher shares of renewable energy in several ways. They improve visibility of distributed and variable resources. Grid operators and VPP operators can see not only how much power is being produced, but also how assets are likely to behave in the near future. This makes it easier to plan and coordinate renewable output with demand and storage.
They also increase flexibility. By aggregating many small resources into a VPP, the system can respond faster and with finer granularity than large central plants alone. Digital twins help to harness this flexibility safely by modeling constraints and predicting impacts. This reduces the need for fossil fuel plants used only for balancing and reserve, and allows more renewable capacity to connect to the grid.
Challenges and Considerations
Although the potential of digital twins and virtual power plants is significant, there are important challenges. High quality data and reliable communication infrastructure are essential. Missing or inaccurate measurements reduce the value of digital twins and can impair VPP operation. Interoperability among devices from different manufacturers is also needed so that they can communicate and be controlled by common platforms.
Cybersecurity is another concern because both digital twins and VPPs depend on networked systems. Unauthorized access or malicious control could affect many assets at once. Governance and clear rules for data ownership and use are also important. Customers need to understand how their data will be used and what benefits and risks are involved.
Finally, regulatory frameworks in many regions were designed for centralized, controllable power plants, not for aggregated virtual plants made of many small units. Market rules, grid codes, and standards often need to evolve so that VPPs can participate fairly and safely. Without appropriate regulation and business models, the technical potential of digital twins and VPPs may not be fully realized.
Successful use of digital twins and virtual power plants requires reliable data, secure communication, clear rules for participation, and regulatory frameworks that recognize aggregated distributed resources as legitimate actors in energy markets and system operations.
Outlook
As digitalization in energy progresses, digital twins and virtual power plants are likely to become more common and more sophisticated. Advances in sensors, communication, and analytics will allow more accurate models and faster decisions. This will support the integration of variable renewables, enhance system flexibility, and open new opportunities for consumers to participate actively in energy systems.