Energy 4.0 for implementation of digital twin applications
Many electric utilities worldwide have started implementing the Industrial Internet of Things (IoT), machine learning, AI, and cloud computing in their operations for asset performance monitoring and management, smart metering, predictive and prescriptive maintenance, the operations and automation of distributed energy resources (DER), and planning and analysis of fluctuations in decentralized renewable generation systems. An electrical digital twin enables the utilities to forecast, predict, and analyze multiple power generation, transmission, and distribution models and renewable energy integration scenarios, allowing them to constantly update their operations to meet the growing need for electricity. These technologies enable better implementation of electrical digital twin solutions in utilities and are at the early stages of integration into the modeling of digital twin systems. Utilities experts and technology vendors have begun referring to this trend as Energy 4.0 to emphasize the enormity of the digital transformation that these technologies would bring to the electrical power industry.
AI enables electrical grids to have two-way communication between utilities and consumers by embedding smart grids with an information layer. This layer allows communication between the various components of a grid so they can better respond to the rapid changes in energy demand or urgent situations. This information layer is created by installing smart meters and sensors, which allow for data collection, storage, and analysis, and the creation of a digital twin. The implementation of AI, machine learning, IoT, virtual reality, and other technologies are expected to help power system and utility operators analyze the large volume and diverse structure of the datasets generated by digital twins and in efficient decision-making. This analysis can be used for a variety of purposes, including seamless fault detection in meters, predictive maintenance needs, quality monitoring of sustainable energy, as well as renewable energy forecasting. Furthermore, AI and machine learning can automatically suggest insights to power system operators and help in improving business outcomes.
The deployment of IoT in electrical digital twins optimizes the efficiency of a power plant by predicting failures that could result in an unplanned outage. Monitoring the field performance of power generation, transmission, and distribution activities via a digital twin and IoT allows real-time comparison of the performance of assets. This process provides valuable real-time feedback for improving the business and operational processes. Electrical digital twins also increase the efficiency of grid design and simulation, as testing in a physical environment consumes not only time but also requires high investments. Enhancements and upgrades can be done and simulated by adjusting parameters of the grid system in the digital twin itself, without any harm to the grid infrastructure and power generation systems. For example, in a wind farm, utilizing an electrical digital twin of turbines enables engineers to study how turbines interact with different landscapes and wind sources, and accordingly mix and match different turbines to optimize the configuration for each pad on a farm.
The power sector in developed countries has already started using AI, data analytics, IoT, and related technologies that allow for communication between smart grids, smart meters, and computer devices. These technologies help prevent power mismanagement, inefficiency, and lack of transparency while increasing the use of renewable energy sources. The major developers of digital twins such as General Electric (US), Siemens (Germany), and Bentley Systems (US) are developing accurate, transparent, interpretable, and fault-tolerant AI, IoT, and machine learning technologies, which would further provide an opportunity for seamless digital transformation and help utilities address the challenges of planning, designing, and operating an integrated network with DER in a highly efficient manner.