As renewable energy continues to advance, Battery Energy Storage Systems (BESS) are becoming essential for efficient energy management and grid stabilization. But as BESS technology grows more complex, so does the challenge of optimizing its performance, predicting failures, and minimizing downtime. This is where digital twins come into play: a virtual copy of a real-world system or object.

Digital Twins

Digital twins are useful because they let us study, test, and improve things without messing with the actual object. Experimenting with a digital version is safer, cheaper, and faster.

Let’s consider a wind turbine on a wind farm. Engineers can create a digital twin of this turbine on a computer. This virtual turbine looks and behaves just like the real one. Now, instead of climbing up the actual turbine to check its condition, engineers can look at the digital twin to see how fast it's spinning, how much energy it's producing, and if any part needs maintenance or replacement.

If they want to test how the turbine would handle powerful winds, they can do that safely with the digital twin. You don't have to wait for a storm or risk damaging the real turbine. This way, the digital twin helps keep the real wind turbine running smoothly and efficiently, without interrupting its operation.

Here is a representation of how they work

Why Digital Twins?

But why use digital twins instead of a regular simulation or just track the telemetry data of the devices? The key difference is that a digital twin is a living model, constantly synchronized with its real-world counterpart. Here’s how it stands out:

1. Unlike simulations, which are often run as hypothetical scenarios on a theoretical model, a digital twin is always connected to the physical object in real-time. It continuously ingests live data, so it reflects the object’s current state. Simulations are useful for one-time experiments, but digital twins provide continuous insight throughout an object’s lifecycle.

2. Telemetry tracking collects data from sensors, but it doesn’t give you a full, interactive model. It just tells you the current status and operating conditions of the device, but a digital twin helps you understand why it’s happening and lets you experiment with solutions or optimizations. It combines sensor data with predictive models to give a more comprehensive view of the system.

In this blog, we'll explore the power of digital twins, their applications in Battery Energy Storage Systems (BESS), and how to create one using Azure Digital Twins.

Digital Twins in BESS

Now that we have a good grasp on digital twins, let us look at how this can be applied to a BESS.

The Importance of BESS

Battery Energy Storage Systems are critical components in modern power grids as they help balance supply and demand, provide backup power, and enable greater grid stability.

Challenges in BESS Management

There are various obstacles involved in managing a BESS:

1. Optimizing charge/discharge cycles to meet energy demand while minimizing battery wear. And prevent deep discharges, which can considerably reduce battery life.

2. Monitoring battery parameters to detect anomalies and signs of degradation. Implementing preventive measures to mitigate degradation.

3. Challenges in multi-cell batteries, such as variation in cell voltages and energy inefficiency, lead to imbalance over time, which must be addressed.

4. Ensuring safe operation in varying temperatures and implementing mechanisms to detect and mitigate faults.

5. Minimize energy loss during charging, discharging and storage and develop effective maintenance schedules to improve overall efficiency.

How Digital Twins Address These Challenges

A digital twin of a BESS can:

1. Track parameters like voltage, current, temperature, and state of charge for each battery cell and the overall system.

2. Analyze patterns to forecast when components might fail or need maintenance.

3. Use AI and machine learning algorithms to determine the best charging/discharging strategies based on grid demand, energy prices, and battery health.

4. Simulate various operational strategies or configurations of the system without risking the physical assets.

5. Detect anomalies and predict potential safety failures before they occur.

Why Azure Digital Twins?

Now that we understand the importance of a digital twin in a BESS, let's look at how to create one. First, you create a digital model of the physical object. Then you can create a Digital twin of that model. Once you have the twins created, you can establish relationships between them. The final step is to ingest the twins with real-time data so that they accurately represent their physical counterparts.

Multiple platforms can be used to create and deploy digital twins, but we decided to go with Azure Digital Twins for our implementation. We’ll discuss its capabilities and the advantages that it offers over the other platforms.

Scalability

Imagine a scenario where your physical infrastructure grows from a single BESS unit to hundreds of units across multiple sites. With Azure Cloud’s infrastructure, scaling your infrastructure becomes much easier. Azure also provides high availability and disaster recovery out of the box thanks to its global infrastructure.

Flexible Modelling

Microsoft’s Digital Twin Definition Language (DTDL) is open and flexible enough to model the complex system of BESS accurately. It allows you to define properties, telemetry, and components and also create relationships with other models.

IoT Hub Integration

Azure Digital Twins seamlessly integrates with Azure IoT Hub, which makes connecting the physical device to its digital twin and ingesting it with data pretty straightforward. This also means that the digital twin is constantly updated with data, giving you an accurate representation of its physical counterpart at any given time.

The architecture below shows how data can be ingested, processed and visualized using Azure services.

AI and Advanced Data Analytics Capabilities

Integrations with other Azure resources like Azure Synapse Analytics and Azure ML allow you to run complex predictive maintenance models, optimize charging/discharging cycles and even simulate grid interactions. As all these are Azure resources, integrating them is an effortless process.

Creating a BESS Digital Twin

Now that we know the capabilities of Azure Digital Twins, let us look at how to create a digital twin with Azure

1. Go to the Azure portal and search for Digital Twins in the Resources. Create an Azure Digital Twins instance and setup network, access and other settings

2. Once you have created your instance, create digital models of your physical entities using Microsoft’s Digital Twin Definition Language (DTDL). It uses a variant of JSON called JSON-LD to define the models and their relationships. Below is an example model of a battery cell.

{
  "@context": "dtmi:dtdl:context;2",
  "@id": "dtmi:xbattery:BatteryCell;1",
  "@type": "Interface",
  "displayName": "Battery Cell",
  "contents": [
    {
      "@type": "Property",
      "name": "voltage",
      "schema": "double"
    },
    {
      "@type": "Property",
      "name": "temperature",
      "schema": "double"
    },
    {
      "@type": "Property",
      "name": "stateOfCharge",
      "schema": "double"
    },
    {
      "@type": "Property",
      "name": "current",
      "schema": "double"
    }
  ]
}

3. Once the models are defined, you can use them to create a digital twin. Every instance you create will represent a physical BESS unit in your infrastructure.

4. Once the digital twins are set up, the next step is to ingest them with live data to represent their physical counterparts accurately. Azure IOT hub can be used to update the twins with real-time telemetry sent from sensors on the device.

5. Once the IOT hub receives the messages from Azure functions can be triggered to process the data before sending it to the digital twins. Algorithms to optimize performance, and predict and detect anomalies can be implemented at this stage.

6. You can perform built-in analytics by integrating Azure Synapse Analytics, Power BI and Azure ML.

7. Once we have multiple twin instances, filtering them based on a condition or a parameter can be easily done with the Azure Digital Twins Query Language. It can also be used to retrieve and analyse data from multiple twin instances.

Key considerations

Implementing digital twins for BESS comes with its own set of challenges and considerations that need to be addressed.

Model accuracy and complexity

For the digital twin to accurately represent the physical object, ensuring real-time data integration is crucial, and setting up this infrastructure is a challenge. There should also be a regular validation process set in place to ensure the accuracy of the model. Complex models replicating the physical system tend to be more accurate, but this would also require a large amount of data to be stored and maintained

Data security and integrity

The large amount of data collected is susceptible to cyberattacks and can lead to exposure of sensitive data if not handled properly. Unauthorized access to the digital twins can allow the attacker to manipulate and control the physical BESS which is a huge security risk. As data from the sensors flows through multiple channels before reaching the digital twin, ensuring data remains unaltered is crucial for operational integrity.

Although challenges like security and data integrity remain, the benefits digital twins offer far outweigh these concerns. Digital Twins in BESS will play an important role in grid stability and energy management, as we increasingly rely on renewable energy. The sustainable future we are moving towards is not just about making bigger batteries, but more about making smarter and interconnected storage systems that can interact and adapt to match our ever-changing energy needs.