With the increasing complexity of the power systems, the urgent need for higher levels of digitalization and automation, and the ambitious goals to modernize and expand the grids in the fastest way possible, the concept of an electrical digital twin is receiving increasingly more attention from distribution grid operators. At the same time, while this concept has already gained a substantial foothold in such industries as automotive, aerospace, and construction, most related projects in the energy sector have more of a research character.
Therefore, let’s take a look at what it takes to create a grid digital twin, why it is considered to be the future of grids, and what the proverbial low-hanging fruits are in terms of the areas of application for grid digital twins.
A brief history of digital twins
The idea of digital twins first came up in 1991 in the book “Mirror Worlds” by Yale Professor David Gelernter. Far from being a science fiction work – despite of what one might think when reading the book’s title – “Mirror Worlds” accurately predicted the development of “mirrors” for objects, buildings, and even corporations: “This mirror world you are looking at is fed by a steady rush of new data pouring in through cables.”
Contrary to the widespread belief that NASA was the one who coined the term “digital twin” in 2010, its earliest appearance so far can be traced back to 1994 to a medical research article by Renaudin et al. However, this idea remained just a theory until 2002, when Michael Grieves introduced the concept of digital twins in the first executive product lifecycle management courses at the University of Michigan, where it was referred to as the mirrored spaces model.
A digital twin doesn’t always equal a digital twin
With the rise of the Internet of Things, digital transformation and big data, the concept of a digital twin became considerably more appealing and valuable. The digital twin of a system unfolds its full potential only when linked to real-world data and, ideally, also to the environment a system or an object is embedded in.
Pre-requisites for a successful digital twin:
- Real-life data
- Process data (measurement values, monitoring values, limits) and environment data (temperature, weather conditions, topology) combined
- Regular (or even better – near real-time) data sync with its actual counterpart
We can think of these pre-requisites as three dimensions that build pillars for a fully functional digital twin. Depending on the availability or lack of one of these dimensions – and also on their depth –, digital twins can have various applications, from the mere static display of a process to a highly dynamic virtual model that allows us to make accurate evaluations and reliable predictions. Due to constraints of the IT infrastructure and/or security concerns, a true real-time data sync is oftentimes not possible, though, but even then, with systematic data sync, a digital twin can be used perfectly for planning and design purposes.
Digital twins, digital siblings, digital cousins
Obviously, the more accurate the model of an actual object or process is, the more accurate conclusions and decisions its digital twin allows us to make. In this context, a distinction is sometimes made between true digital twins that use “potentially proprietary and confidential data” to represent the real system as closely and accurately as possible; digital siblings that use open data to create merely a statistically close representation of the real system, and digital cousins that have no real equivalent and are used only for speculative purposes.
This distinction is important in the context of power grids, because power grid data is often of very sensitive nature, and hence it’s not unusual for research and development to be based on digital siblings. This, obviously, has its drawbacks in a real-life application, because a statistically close representation of a power grid can hardly be used for making high-stakes decisions.
What are the advantages of an electrical digital twin for utilities?
As already mentioned in the first part of this short series, the power system is one of the most complex physical systems ever created by humanity, and the use of grid digital twins holds great promise for its transformation because it allows to encompass its complexity and translate it into actionable insights.
The concept of grid digital twins is seen as a crucial step towards making power grids smarter and, as a result, enabling dynamic monitoring, evaluation, and much improved decision-making. This will allow processes such as grid stability analysis, grid planning, model validation, and grid congestion analysis, which were previously conducted largely offline and manually – and as such were naturally prone to human error –, to now be mostly or even fully automated.
Grid digital twin vs. traditional grid simulations & grid modeling
Now, you might ask what the difference is between the common power system simulation / grid modeling and an electrical digital twin. Both refer to the concept of a digital representation of a real-world physical entity or system, which is not something new. Power system simulation and grid modeling have been used by operators for quite a long time already to identify possible bottlenecks and obstacles in planning and development.
The main difference is, however, in the latest iterations of digital twins. Gartner sums up their key characteristics the following way:
- virtual models based on a digital twin are more robust;
- digital twins have a direct link to the real world, potentially in real time;
- process data combined with environmental data (=context data) make the application of advanced big data analytics and AI more effective;
- data comprehensiveness allows for better communication with virtual models and even more accurate evaluation of “what if” scenarios
Conventional grid simulation tools are static in nature; they might have the most recent data and therefore help make accurate predictions and run different scenarios with various variables introduced into the digital environment, but they still lack the element of real-timeness or at least near real-timeness. In this sense, a traditional electric grid simulation is much like a digital sibling – just a statistically close representation.
Digital twins, on the other hand, respond to changes in the physical system in a dynamic way, thus enabling event-driven automation. Provided that the virtual model is “subscribed” to specific grid change events, it can update itself in real time and initiate a series of necessary computational steps to respond to a disturbance in the grid either automatically or by providing the human operators with the required decision support.
Moreover, traditional energy modeling is mostly performed with the help of the so-called simplifications, when only a few representative networks are selected to build a generic model, which is used for e.g. grid integration studies or grid planning. The results are then extrapolated system-wide. Such an approach is by definition error-prone as it cannot adequately reflect the complexity of today’s power system.
On the contrary, the digital twin technology allows to map the entire grid, including the distributed power generators and loads and its other components, which provides a considerably more reliable and true-to-life virtual power grid model. This obviously increases the accuracy of any kind of analysis and simulations many-fold.
What are the areas of application for an electrical digital twin in distribution power grids?
#1: Increasing the levels of transparency to ensure the overall grid stability
The increasing presence of rooftop solar, heat pumps and e-chargers adds to the complexity of a network topology in the way grid operators have never experienced before; the energy system becomes increasingly interconnected and integrated. In this context, a electrical digital twin can play a crucial role in managing and ensuring a smooth operation of such a complex system by allowing grid operators to monitor the stability / resilience of their network.
For instance, provided that there is enough monitoring data in real time – for example from residential smart meters or substations –, controllers can use the digital twin of their distribution power grid to get a comprehensive overview of the grid status in a certain area. This way they are able to step in timely and regulate voltages through inverters or other remotely operated systems in order to alleviate or even prevent grid congestion.
#2 Prioritizing needs and investment for grid reinforcement and grid expansion
Furthermore, an electrical digital twin can help the grid operator plan the grid reinforcement and grid expansion measures as well as grid assets maintenance. By combining current grid data with historical information and detailed virtual models, the operator can analyze, or in some cases even predict, the health of the grid and its assets and take proactive measures.
Particularly in grid planning, both operational and strategic, it is imperative to have accurate and complete models of the grid. With this in mind, the digital twin combines the physical models of a given power network with the relevant process and environment data, thus offering a nearly perfect virtual copy of the real grid. This allows grid operators to simulate certain experiments such as potential grid reinforcement measures – for example, virtually replacing current underground cables with more powerful ones –, evaluate various scenarios and their potentials and after that, closely analyze the implementation pathways in a safe, staged environment.
In addition to that, by gaining a more holistic overview of the current and planned locations of various alternative power generators and consumers as well as their input power and output power data, distribution grid operators can more effectively prioritize the grid expansion needs and measures. In turn, this allows for more efficient planning and investment.
#3 Providing more automation to counteract the impact of the skills shortage
Oftentimes, ensuring reliable power supply requires quick decision-making – for instance, to apply the right voltage stability control action in case of emergency and thus prevent voltage collapse. In the past, experienced grid operators were fully trusted with making high-quality decisions quickly; experienced grid planners justifiably relied on their gut feeling to guide the decisions about what to build, when to build and where to build.
However, the increasing grid complexity, an aging and retiring workforce, and the efforts to transition the electrical grid to the new digital era, leading to an ever-growing stream of data, create completely new challenging situations. In fact, the recent Power Grids Research Report by DNV placed the issue of skills shortages and aging workforce in the top five of barriers to a faster energy transition.
In situations, when a critical decision must be made fast, a grid digital twin can be used as an advisor and a support tool for less experienced workers. Of course, accurate and clean data that a digital twin operates with is of utmost importance in such cases.
Alternatively, an electrical digital twin can equally well serve new or less experienced grid operators equally well as a training and educational tool to simulate emergencies, cascading failures, cyber-attacks, and other critical issues, or even analyze past breakdowns and critical events to learn from real data. The more the new workforce has been coached with such simulations, the quicker they are going to be in the case of an actual event in the future.
This is why an electrical digital twin is key to ensuring grid stability and smooth grid operations
The electricity ecosystem is undoubtedly becoming increasingly crowded, where each element provides an enormous amount of data. Decentralized energy generation and consumption systems, operating assets equipped with sensors, and smart meters – all adding up to the complexity of distributed grid management and operations.
With this in mind, providing high levels of transparency into the current state of the grid on an area-by-area basis is becoming vital for avoiding grid congestion and bottlenecks, ensuring smooth operations and making well-informed decisions about the areas where grid reinforcement and expansion are most urgently needed.
Smart meter data in particular provide an invaluable basis for ensuring grid resilience through even better understanding of the high demand times of use and the flow of energy along the grid. However, all this data must be translated into actionable insights.
This is where the digital twin technology is proving to be of high value in improving the levels of transparency and visibility into the grid ecosystem in a more holistic way than it has ever been possible before. It makes it possible to combine various data sources such as smart meters, GIS, ERP, SCADA, ADMS and so on, to provide a single source of truth for grid operators that is dynamic and can react to different event changes such as weather conditions or loads.
It also makes it easier for grid operators to plan grid reinforcement and expansion measures more effectively, since it provides an accurate and always up-to-date map of the current grid situation.
The energy transition is happening already. However, the utility industry is facing many challenges to support it to the required extent. The digital twin technology allows grid operators to integrate together the entire electricity ecosystem from renewable energy generation to prosumer, thus helping them cope with and react to new challenges most effectively.
This project is supported by the German Federal Ministry for Economic Affairs and Climate Action as part of the Renewable Energy Solutions Programme of the German Energy Solutions Initiative.
German Energy Agency (dena)
The German Energy Agency (dena) is a centre of excellence for the applied energy transition and climate protection. dena studies the challenges of building a climate-neutral society and supports the German government in achieving its energy and climate policy objectives. Since its foundation in 2000, dena has worked to develop and implement solutions and bring together national and international partners from politics, industry, the scientific community and all parts of society. dena is a project enterprise and a public company owned by the German federal government. dena’s shareholders are the Federal Republic of Germany and the KfW Group.
German Energy Solutions Initiative
With the aim of positioning German technologies and know-how worldwide, the German Energy Solutions Initiative of the Federal Ministry of Economics and Climate Action (BMWK) supports suppliers of climate-friendly energy solutions in opening up foreign markets. The focus lies on renewable energies, energy efficiency, smart grids and storage, as well as technologies such as power-to-gas and fuel cells. Aimed in particular at small and medium-sized enterprises, the German Energy Solutions Initiative supports participants through measures to prepare market entry as well as to prospect, develop and secure new markets.
Renewable Energy Solutions Programme (RES Programme)
With the RES programme, the Energy Export Initiative of the Federal Ministry of Economics and Climate Action (BMWK) helps German companies in the renewable energy and energy efficiency sectors enter new markets. Within the framework of the programme, reference plants are installed and marketed with the support of the German Energy Agency (dena). Information and training activities help ensure a sustainable market entry and demonstrate the quality of climate-friendly technologies made in Germany.