The ongoing Industry 4.0 transformation is driving companies to adopt new data collection and analytics technologies. Implementing IIoT in energy enhances control over energy assets and operations and allows companies to manage their environmental impact.
Industrial Internet of Things (IIoT) devices collect real-time data from fossil sources to gain in-depth insight into their performance. More importantly, this data enables organizations to analyze their energy operations more accurately with digital twins—virtual replicas of the energy infrastructure.
Both of these technologies take a lot of effort to implement. However, our software development company has worked on multiple products in the energy and utility sector and has the experience to share. Let’s explore these technologies and discuss practical applications, relevant pilot projects, and ways to avoid common implementation challenges.
Introduction to Digital Twins and IIoT in the Energy Sector
IIoT is an umbrella concept for interconnected sensors, machine-to-machine communications, and AI-powered tools networked together for industrial applications. It could be called IoT for the industrial and manufacturing sectors.
In the energy industry, IIoT improves the monitoring of energy production and management systems like solar panels, turbines, and energy batteries. The data provided by the IIoT is used to analyze energy operations and, more importantly, to create digital twins.
A digital twin is a virtual model replicating the physical qualities of assets, utilities, and operations. In simple terms, digital twins allow companies to visualize and experiment with different scenarios and settings for their power plants and grids. Fed with real-time sensor data from IIoT, these virtual replicas simulate actual conditions more accurately for precise control and optimization.
What Is the Role of IIoT in Energy Optimization?
The short answer is that IIoT allows energy businesses to gain better control over energy sources. According to Precedence Research, the global IIoT was valued at $320.9 billion in 2022 and is projected to reach as much as $1.56 trillion by 2032. The energy and power sector is ranked as the second-largest market.
The growth of IIoT is driven by its ability to enhance productivity, performance, and efficiency in industrial companies. The key applications for the renewable energy sector include:
- Real-time monitoring of renewable sources. Smart sensors and cameras on grid equipment, such as solar panels and wind turbines, continuously collect data about the energy output and weather conditions. Operators can gauge the temperature, sunlight intensity, and performance anomalies.
- Energy distribution optimization. IIoT-enabled smart grids let companies adjust the energy flow dynamically based on real-time data. For example, they can redirect excess energy to areas with higher demand.
- Enhanced operational efficiency. Sensor data analysis allows operators to make operational adjustments to maximize energy output. For example, they can optimize the angle of solar panels throughout the day to capture maximum sunlight based on sunlight intensity.
- Faster anomaly detection. AI algorithms can analyze data from your equipment to detect anomalies that are not visible through traditional monitoring tools. Automation capabilities, combined with anomaly detection, enable the system to immediately alert technicians to any problems.
- Predictive maintenance of the equipment. The data collected from IIoT sensors is continuously analyzed to identify patterns that indicate wear. For example, certain sounds or unusual vibrations can signal the risk of breaking, helping to reduce repair costs and downtime.
- Battery storage improvements. Companies can optimize the charging cycles of storage batteries based on sensor data. This ensures they are charged and discharged at optimal times, extending their service life and stabilizing the energy flow.
IIoT systems can integrate with various external sources, such as satellite imagery and weather forecasts, to provide a comprehensive view of renewable energy systems. Sensors are also used to create digital twins of the grid.
Advancements in Energy Efficiency through Digital Twins
Digital twins help companies identify areas for improvement in their energy operations. These virtual twins use real-time data from your IIoT network to create highly accurate simulations, which helps energy companies understand their networks even better.
According to MarketsandMarkets, the global digital twin market is set to grow from $10.1 billion in 2023 to as much as $110.1 billion by 2028. For energy companies, key drivers for adoption include predictive maintenance, big data analytics capabilities, and advanced IIoT applications.
Compared to traditional energy management methods, digital twins offer quite a few advantages:
Digital twins make the grid more resilient, adaptive to changing demands, and suitable for renewable energy sources.
Implementing IIoT and Digital Twins for Renewable Energy Optimization
Just like any organization in energy sector, you want to minimize environmental harm without affecting the revenue. You can meet energy production and sustainability goals by leveraging digital twins and IIoT.
- IIoT in renewable energy enables continuous, real-time monitoring of environmental conditions. The constant flow of data, combined with analytical software, allows operators to optimize energy production processes and make adjustments that reduce emissions.
- Digital twins allow organizations to simulate operational conditions. You can simulate different demand response scenarios that are too costly to test in real life, finding ways to reduce environmental impact with minimal performance compromises.
- Digital twins and IIoT improve grid stability by balancing energy supply and demand in real time. Stable grid operations reduce energy waste and lower the overall environmental impact.
- With a digital twin, renewable energy sources can be integrated more smoothly. The technology makes integrating solar, wind, and hydroelectric power into your existing energy grid easier, reducing reliance on emission-heavy fossil fuels.
- Improved load balancing reduces excessive power generation. As mentioned, sensors and simulation software help adjust energy distribution based on demand patterns, balancing the grids and lowering the need for energy generation.
- Virtual twins enable cities to test sustainability strategies cost-effectively. Traditional methods involve physical pilot projects that can be time-consuming and expensive. Testing sustainability and energy-saving scenarios in a simulated environment is much faster and more affordable.
- Adopting digital twin technology for smart cities improves sustainability. The virtual model integrates data from all sources to help you manage and redirect energy.
- Microgrids and distributed energy resources reduce transmission losses. These systems produce energy closer to where it is used, decreasing transmission losses. Digital twins help model the performance of these systems for better efficiency.
- IIoT and digital twins can aid wildlife tracking. Organizations use tools that track different environmental parameters, such as vegetation health and water quality. For example, a hydropower plant can employ a digital twin to optimize water flow, ensuring minimal disruption to aquatic habitats.
Integrating these technologies ups your management capabilities and accelerates the transition to a green energy future. Yet the integration itself is pretty challenging.
Challenges in Digital Twin Technology
Data incompatibility
Data used by digital twins comes from diverse sources that often follow different standards and protocols. A lack of common standards makes it even harder to create comprehensive virtual models. Low-quality data only adds to the issue, as it can corrupt the accuracy of AI predictions.
Addressing the compatibility issue requires enforcing standardized data exchange formats and reference frameworks. Another solution is integrating disparate data sources into a unified analytical model using data extraction and transformation tools. It’s also critical to invest in quality management processes to ensure that data is accurate, complete, and reliable.
Scalability issues
Your system must be able to handle the growing data volumes as the number of interconnected IIoT devices increases. Poor scalability can hinder your digital twin’s ability to process large-scale applications like smart cities or industrial IoT networks.
Maintaining efficiency at scale requires careful management of system resources. You should optimize data storage, computing power, and network bandwidth. Companies should consider cloud infrastructure that can adjust computing power and data storage based on demand. Cloud-based digital twins also reduce the need for costly on-premise infrastructure, maintenance, and security updates.
Security and privacy considerations
A potential cyberattack may expose energy consumer data and intellectual property or disrupt your systems. In addition to that, you must comply with strict privacy laws like GDPR and CCPA.
To improve security, set up robust firewalls, intrusion prevention systems, role-based access, and zero-trust network policies to prevent unauthorized access. Encrypt communication between systems. Finally, use de-identification tools to anonymize your data, eliminating the risk of leaks in case of breaches. Of course, regular security audits are also necessary to maintain security and privacy protection.
High costs of implementation
Digital twin technology requires substantial investment in smart sensors, 3D computer-aided design (CAD) software, business analytics software, and, possibly, augmented and extended reality. The cost can be prohibitive not just for small and midsize businesses, but also for large enterprises that might need to refactor their legacy infrastructure.
I recommend a phased approach. Start with smaller pilot projects to validate the digital twin’s value before scaling up. Ready-made cloud-based solutions will also cut some of the hardware investments. Another option is seeking funding or grants from industry agencies supporting renewable energy initiatives.
Lack of skilled workforce
The talent gap can slow the adoption of digital twins as companies struggle to find people capable of effectively using these tools. Moreover, your employees may not fully understand the benefits of technology, which creates resistance to digital transformation in the energy industry.
Alongside hiring new employees, companies often partner with educational institutions to offer training programs. Collaborating with an experienced renewable energy software development company can help bridge the skill and technology gaps.
Technology providers can also share the financial burden and help reduce the risks associated with high initial investments. The first case study in the next section is all about our cooperation with an energy company.
Case Studies: Digital Twins and IIoT Implementation
We’d like to highlight some successful implementations and pilots to show how widely these technologies are being adopted in the energy industry.
Digital twins for energy storage system optimization
Techstack developed a custom solar energy storage system for a company in Los Angeles. During the initial research, we identified problems in existing systems. Following up on that, our team created a custom solution that integrated with the company’s solar panels, batteries, and PV inverters.
In particular, we created an IIoT network that connected all devices within the company’s energy infrastructure. Digital twins were instrumental. Simulations pointed us toward the most efficient energy usage practices for operations. The tech also helped us implement predictive maintenance capabilities that improved the system's reliability and reduced downtime.
Adaptable federated digital twin ecosystem
TwinEU has initiated a project to develop a federated digital twin ecosystem. The system is being implemented in several European countries, including Greece, Germany, Bulgaria, Spain, and Italy. The digital twin ecosystem will span three layers: an adaptive twins federation layer, a dataspace-enabled data and model sharing infrastructure, and a service workbench for third-party access.
These pilots aim to validate the use of digital twins for grid real-time monitoring, balancing, and optimization. A separate focus is their ability to improve the resilience of physical power system components in scenarios with high renewable energy integration.
Interconnected energy distribution infrastructure
SGN, one of the UK’s largest gas distribution networks, launched the Gas System of the Future digital twin project in collaboration with Amazon Web Services and IBM. The goal is to connect multiple digital twins from various energy ecosystem partners using an open-data approach across multiple energy vectors, including hydrogen, biomethane, natural gas, and electricity.
The digital twin provides operators with an up-to-date view of the network’s status, improving safety, reducing supply interruptions, and protecting vulnerable customers.
Digital twins for multi-visual collaboration
Azule Energy adopted digital twin technology to address the challenges of offshore operations. This integration enabled a contextual, multi-visual view of the sites, which helped break down silos between onshore and offshore teams.
Using data-led insights, Azule Energy improved maintenance planning, reduced the need for offshore trips and visits, and facilitated remote operations. Plus, real-time monitoring and forecasting capabilities allowed the company to save resources and make operations safer.
Sustainability and decarbonization project
Siemens and Ecolab partnered in 2022 to develop a digital twin program, Climate Intelligence. The companies want to help energy organizations achieve sustainability goals while staying profitable.
The digital twin technology provides actionable insights that industries can use even as they formulate long-term decarbonization strategies. Preliminary results from pilot programs have been promising. For example, tests in a mid-size refinery showed a potential reduction of about 40,000 tons of carbon emissions per year and estimated savings of $1.8 million.
While the current use cases and pilots are quite impressive, the technology is still evolving. There are quite a few things to look forward to.
Future Trends in Digital Twin and IIoT for the Energy Sector
In the coming years, you can expect some exciting developments in digital twins and IIoT. These are the major trends we identified:
- The movement towards energy interoperability. International bodies like the EU are enforcing measures to improve interoperability in the energy sector. We expect organizations to exchange data between silos and communicate across the IIoT networks more freely in the future.
- Continued expansion of decentralized energy systems. Digital twins and IIoT will be critical in expanding decentralized energy systems. These technologies support the integration of renewable energy sources, reduce transmission losses, and provide greater energy security for communities.
- Real-time energy trading via the blockchain. The blockchain can make energy operations more secure and transparent. Combining digital twins, IIoT, and blockchain will also facilitate peer-to-peer trading, allowing consumers and producers to buy and sell energy directly.
- Continuous integration with smart cities. Companies will rely on digital twins to implement energy-saving measures for different aspects of smart cities. They can use virtual process simulations to calculate the most efficient strategies for distributing power between neighborhoods or lighting buildings and streetlamps.
- Increased use of edge computing. Edge computing brings data processing to the analytical systems. This allows digital twins to operate more efficiently by processing data locally rather than relying on centralized cloud servers.
- Adoption of explainable AI models. The AI and machine learning models that provide predictions must be fair, unbiased, and understandable.
- Leveraging generative AI tools. Organizations like IBM report that their clients in the energy sector use generative AI and large language models in their digital twins. Some use cases include visualizing anomalies in utility assets, managing key performance indicators, and introducing automated service assistance tools like question-answer chatbots.
Looking at current use cases and predictions, one thing is clear: Digital twins are slated to become a core part of any energy grid system and a great way to transition from fossil fuels to renewable energy.
Embracing Industry 4.0 with IIoT and Digital Twins
The digital revolution in the industrial sector has accelerated the use of digital twins and IIoT in the energy field. These technologies can transform the way you control and maintain your entire network, from monitoring operations in real time to simulating advanced scenarios in a virtual model.
The key obstacle to adoption is not technological complexity or high investments, but the fear of change. Techstack's experience in the energy industry can help overcome those fears.
Our software development and IIoT product engineering services can change how you manage your microgrid. We can integrate IIoT and digital twin systems for your organization and help your employees embrace them. Contact us to discover the full range of our solutions for the energy sector.
