Digital Twins for Complex Power Generation Operations

Summary

With all the recent focus on electrification of human lifestyles across the globe, it can feel as though the energy industry has changed overnight. Today, the global public is hearing more about photovoltaic solar, wind generation, and battery storage than ever before. Businesses and residences are installing solar panels at an unheard-of pace, while expanding solar and wind farms become visible reminders that the grid is changing rapidly.

Though in the past they did not enjoy the market share they do today, solar, wind, and battery systems have existed for a long time. These technologies have been understood and improving for decades. It simply took a cultural shift—increased pressure and incentives from governments and the public—to bring renewable energy generation technologies into the spotlight.

With such a change comes increased complexity. Power generation companies must now run their operations differently. As these organizations incorporate renewables, they must navigate continual changes, both in weather and in energy markets, to be profitable and efficient. Simultaneously, many of these companies face severe workforce shortages. They can’t find people with the expertise necessary to run generation facilities—renewable or traditional dispatchable—at peak operational efficiency.

Fortunately, a critical technology the energy industry has relied upon for decades—digital twin simulation—has continued to evolve to support transitions in generation portfolios. Modern digital twins provide a way for users to navigate a changing industry. As a result, as power generation operations increase in complexity, the business case for digital twin simulation continues to improve.
 

What is a digital twin simulation?

Digital twins come in many different forms, but for the power industry, one of the most valuable is a virtual simulated replica of a control system that duplicates the monitoring and control of plant, process, and system operations in a secure, risk-free environment. Key components of a digital twin include:

• Simulation models that accurately reflect the operation and interaction of plant equipment and processes.
• Virtual controllers, which replicate plant controllers to execute simulated models.
• An instructor station that controls the simulation for operator training.
• Standard control system software for operations and engineering.
• Replica control system logic and graphics.

There are many different use cases for digital twin simulations, with the most common use case being operator training. With increasing turnover in industry and a shortage of experienced workers available to backfill a retiring workforce, companies need to train operators quickly, safely, and effectively. Best-in-class digital twins use the same automation platform as the plant control system for this training. An operator training on such a system gains real-world experience, interacting with controls, graphics, and tools that are identical to the ones they use when operating the physical plant. Moreover, systems using a single set of common tools help organizations realize cost savings through less maintenance, training, and service required to maintain a single platform for both the digital twin and plant controls, versus individualized platforms for each (Figure 1). 

Benefits for traditional dispatchable power generators

Power generation operations are changing. Plants built decades ago were typically designed to run continuously, supplying as much power to the grid as possible. However, with the rise of renewables, such plants are seeing the need for more dynamic operation, which increases the number of complex activities operators must perform. Improved operational efficiency is highly reliant on the organization’s ability to tap into the knowledge of industry veterans to reskill current staff, and to teach a new generation of digital natives how to operate plants safely and efficiently.

However, traditional dispatchable base load power plants typically have a lot more moving parts than most renewable facilities, with more severe consequences for failure. Consequently, it is difficult, if not impossible, to train new personnel on complex activities, such as startup and shutdown, on live equipment. New operators in these plants have likely had few or no opportunities to start up and shut down the plant. Moreover, they will have had even fewer opportunities to experience abnormal conditions, such as when a boiler feed pump fails.

These new personnel not only need to know what to do in such situations, they also need to know what not to do. For example, if the plant has a failure on a critical piece of equipment, the automation might initiate a runback, bringing the power output back to a level it can support. A system running back looks very different from a system in normal operating mode, and operators need to recognize such a status and understand the normal mitigation strategies common in abnormal operations. The only way to accomplish this is to let operators see such situations themselves, and the best way to let them do that safely is via a digital twin simulation.

In addition, as dispatchable base load generators experience more startups and shutdowns, it becomes more important to ensure that those operations occur in the same, optimal manner regardless of who is on shift at the facility. For example, as soon as a system starts burning fuel, the team needs to close a breaker as soon as possible, without violating equipment constraints, but doing so requires expertise. Digital twin simulation training allows teams to score operators based on ideal responses, then train and retrain them until they can optimize operation without creating undue mechanical stress on equipment.
 

Benefits for renewable power generators

Companies with renewables operations are seeing the most benefit from using digital twin simulations when they use them as test beds for engineering and improving operation. A digital twin can be used to test and validate new control strategies before starting commercial operation. This capability is particularly beneficial to organizations investigating microgrids—a collection of assets that presents itself as a single entity for energy distribution. Using a digital twin simulation, teams can more easily build and manage their portfolio of assets, modeling loads and determining the capability of electrical components.

Teams can also use a digital twin to more easily model the way distributed energy resources (DERs) interact with each other. As Federal Energy Regulatory Commission (FERC) order number 2222 gains more traction, allowing DERs to compete more easily in energy markets, new players in the industry will likely use digital twins to build virtual power plants, aggregating all their disparate DERs into larger, more easily controllable generation assets.  

Many companies are also extending their digital twin capabilities outside of the plant with smart grid extensions. These tools are used with grid-level simulation packages to provide simulation of an organization’s total power system. Such a solution can help organizations understand the grid’s varying conditions, while managing communications and data flows to optimize production across the total power system, from generation to distribution.
 

Predicting the future

One of the most significant trends in the power industry is the shift toward control room consolidation. The historical footprint for power generation is far less applicable to renewables sites, many of which maintain few or no personnel on premises. Even traditional plants have been forced to cut back on their on-site personnel.

As these changes occur, the way the fleets are being controlled and monitored is also changing. Consolidation of multiple plant operations into a single, remote operations center is a common strategy power generators are using to improve reliability, reduce costs, and increase operational flexibility.

As power companies centralize operations, digital twin simulations help them cross-train co-located experts to remotely monitor, operate, and maintain a wider variety of assets. Technicians, engineers, and operators in a centralized control facility can also use the digital twin as they collaborate to improve maintenance strategies and develop improved operations across the enterprise.

Another increasing trend in power generation is the use of artificial intelligence (AI) to improve efficiency and productivity. Under the right circumstances, a digital twin could be used in a predictive capacity, incorporating real-time plant data and running at faster than real time to identify potential flaws, bottlenecks, or other problems that will occur in the future.

Today, accomplishing this predictive capability on a digital twin is a difficult task. First principles models are hard to run faster than real time due to the complexity of their calculations and the computing overhead necessary to accomplish such a task. As tools improve, however, AI components could exercise digital twin models to learn the dynamics of a system, enabling them to build lightweight surrogate AI models that could be run faster than real time. Coupled with a generative AI-driven copilot, these tools could make a predictive digital twin more approachable, empowering personnel to simply ask the AI to predict the results of any changes to standard operations.
 

Building a flexible foundation

The rise of renewable power has brought increased complexity for generation and distribution organizations. Operations teams need to be much more flexible, which requires them to lock in best practices to ensure peak safety and operational efficiency. Digital twin simulation is a critical enabler of that flexibility, helping teams not only teach all their personnel to operate at their best, but also providing a test bed for the increasing number of operational changes necessary to compete in a more complex, hybrid environment.

The best digital twin tools will be based on the same platform as a control system designed specifically for the power industry. Such solutions eliminate the complexity and cost of maintaining separate modeling software, and they make it easy for in-house staff to update plant models and training scenarios using familiar—and often, automated—tools. Moreover, implementing a built-for-purpose system today will provide the foundation necessary for the smart grid extensions, centralized control, and AI technologies that will help organizations navigate the even more complex dynamic operations just over the horizon.

<sub>All figures courtesy of Emerson

This feature originally appeared in the June 2024 issue of InTech digital magazine.</sub>