Digital twins in energy system planning are revolutionising how the UK manages its increasingly complex electricity grid. As renewable energy sources (RES)—particularly solar—expand rapidly across Britain, the grid faces unprecedented variability and stability challenges. Traditional static planning models have long been a cornerstone of energy system planning, but they can no longer cope with the dynamism of modern energy systems, where solar output fluctuates dramatically with weather conditions, electric vehicles create intermittent demand spikes and data centres draw massive power loads. Digital twins offer a transformative solution: real-time virtual replicas of grid assets, transmission networks, and renewable generation systems that enable asset owners and grid operators to understand, predict, and optimise solar integration.
What is a digital twin in energy system planning?
A digital twin is a virtual model environment that is continuously updated as data is fed in to in real-time from sensors attached to real-world assets. Data is collected from assets in the energy grid, and the data is used to build an initial model, which is a virtual interactive environment, and the model is kept updated as new data is fed into it. Unlike a lot of other simulations, it’s a live environment that allows assets to be swapped around, scenarios to be run (e.g. load scenarios that predict how assets will behave in a local grid), energy forecasting to be predicted, and much more, without having to make any changes to the physical system. In short, instead of tweaking physical systems to find the most optimal way of running—which could run the risk of downtime or damage if not done right—grid assets can be configured and tested in the virtual environment before any changes are made to the physical network. Think of it as a way to do ‘digital prototyping’.
A digital twin in energy system planning can be run from the individual asset level (i.e. individual solar panels) through to modelling the local grid and even the national grid—as thousands of assets can be modelled, managed and changed inside a digital twin. This involves looking at how assets might impact the local grid, managing multiple assets and their loads in the grid under different load scenarios, optimising the asset itself, and monitoring and maintaining the assets over time (predictive maintenance) to ensure they are running optimally. Digital twins can also be used to make grid cybersecurity measures more robust—something that is becoming more important as more legacy grids transition towards smart grid architecture and the assets in the grid become digitally connected to each other.
In some cases, a single digital twin might not be enough to model certain large-scale scenarios, so it also possible to utilise multiple digital twins in energy system planning. This is particularly important for determining the impact of external factors on the grid, such as the weather, as those external factors can then be paired with the grid-specific digital twin. Additionally, as digital twins get more powerful and now need to manage more and more grid assets inside the virtual environment, artificial intelligence (AI) and/or machine learning (ML) is starting to be used a lot more.
Why renewable variability is the hardest grid planning problem
Renewable energy variability presents problems for grid planning, and both solar and wind variability can affect grid stability. Renewables are unpredictable and their outputs can change within a matter of minutes. Traditional monitoring tools are static and have not been designed to account for renewable energy variability and more dynamic monitoring tools are required.
Renewables are making up an increasingly larger share of the UK grid, however, solar panels (and wind turbines) have fluctuating outputs that can’t be controlled by energy operators and are instead controlled by the natural world, i.e., the weather—which is variable at the best of times but we can have four seasons in a day in the UK.
In the UK, this is an issue for solar panels, because even summer months can be dull, so there is huge solar variability and unpredictability that makes grid planning difficult when there’s a lot of solar assets that could, or could not, be providing energy to the grid compared to other countries. For example, there could be too much solar producing energy bottlenecks, followed by a dull period with no energy generation where the gap needs to be covered using other sources.
This intermittent nature of energy generation is more of a challenge because there are intermittent energy drawers (such as EVs) that spike local energy usage. This combination means that grid planning and grid stability is more fragile than ever, especially as more grids are moving to smart grid architecture with a higher number of renewable energy assets.
The dynamic nature of the modern grid requires a dynamic monitoring solution. This is where digital twins come in. Traditional solar monitoring methods use average values, but when the grid can be so dynamic, there is no true ‘average’ grid scenario, and these methods cannot adapt to the changing conditions that solar integration brings. Maintenance is also scheduled for after issues arise, rather than pre-emptively solving the problem predictively before it manifests.
Digital twins not only help with the predictive maintenance side, but the ability to simulate energy flows in real-time allows energy operators to monitor and react to dynamic changes in the grid—making the grid more adaptable and stable, even when there is a high concentration of intermittent loads and generators attached to the grid. These factors are making digital twins a critical part of monitoring strategies in modern-day grids and are going to be a key part of decarbonising the grid—and will help with grid planning regardless of renewable energy variability and will help to ensure gird stability, even when there’s a high solar and wind variability.
NESO’s virtual energy system and the UK’s national digital twin
The UK’s National Energy System Operator (NESO) is developing a digital twin ecosystem called Virtual Energy System. Launched in 2021, the Virtual Energy System is an interconnected digital twin of the UK’s energy landscape and is a shared industry asset that aims to support the long-term goals of decarbonising the grid in Great Britain. The NESO digital twin works in parallel to the physical grid and aims to improve the simulation and forecasting abilities of the grid.
The UK’s Virtual Energy System has its roots in the National Infrastructure Commission’s 2017 National Digital Twin Proposal (NDTP)—which officially started in 2018. The NDTP is a centralised government effort to grow digital twin technologies in the UK, and includes developing the required standards, frameworks, guidelines, methodologies, and tools to create a robust digital twin infrastructure that spans both the energy industry and other critical technology industries.
The digital twins within the Virtual Energy System represent how electricity and gas assets and link up to other sectors and enables the secure sharing of energy data between organisations that operate within the grid. Sharing the data between relevant parties enables more complex scenario modelling within the grid to be performed, which improves decision-making at a system level. The hope is that better decision-making at a system level will balance the needs of users, electricity and gas systems, and adjacent sectors.
The Virtual Energy System contains an open framework, where parties have agreed access, operations and security protocols. Over time, new digital twins are added to the system and are used alongside the existing digital twins, where each digital twin gives access to real-time data on the status and operation of specific elements of the system. This has created a layered system where the virtual environment keeps getting added to as new data comes available and new grid challenges manifest.
The UK’s Virtual Energy System is not seen as an alternative digital twin network to those being developed internally by energy companies in the UK. It’s seen more as a way to improve interoperability within the UK energy industry and to provide better information sharing to optimise grid operations. The users of the Virtual Energy System have data models and digital twins that contribute to more efficient simulation and planning and/or have grid problems that can only be solved by having access to the data and models that are a part of NESO’s digital twin.
Digital twins, grid connection queues, and curtailment
The UK has issues with grid connections (as does a lot of mainland Europe) and there is often a queue to connect solar to the grid. There are also long grid connection queues for BESS.
Connecting solar and other renewables to the grid is a big issue in the clean energy transition and there are now hundreds of gigawatts of solar installations that are trying to be connected to the grid. As more data centres, especially AI data centres, come online, the amount of renewable + BESS penetration in the grid is likely to significantly increase—which will exacerbate the current problem further.
Traditional grid planning methods only look at historical data, reflecting how the grid looked a few months ago rather than its current state. With digital twins, real-time demand, generation, and connection requests can all be accounted for, giving grid operators a much better picture of what the current state of the grid is, where the energy is being generated from, and where the connection requests are. This gives grid operators the ability to look at the past, present and future state of the grid and make appropriate adjustments.
When new solar grid connections are applied for in the UK grid, the digital twins already have the required data to model their impact based on other assets in their local grid. Digital twins allow grid operators to model the solar output in different grid scenarios in the local grid network and determine the effects that connecting it to the grid might have—which also allows it to be better monitored going forward as all the assets and local grid are understood and can be monitored in real-time.
Digital twins can therefore help grid operators make more informed connection request decisions much quicker. But digital twins in grid operations is not about replacing humans entirely, it is more centred around the technology being ‘human-in-the-loop’ with the key engineering decisions still being made by humans.
Even when renewables are connected to the grid, curtailment is an issue— when renewable energy is lost or their output is supressed because the grid can’t accept the energy due to a high congestion or overvoltage. These bottlenecks can occur when there are high energy generation periods and no place to store excess energy, or the storage of excess energy is not optimised. Digital twins can reduce grid curtailment by running real-time models to optimise power flow, storage, and export limits.
Over time, the digital twins learn what normal operations looks like, so any abnormal grid congestion can be spotted against the historical data, and energy can be smartly dispatched or stored in BESS or other flexible grid assets (such as EVs when vehicle-to-grid operations become more common). Being able to smartly store and dispatch energy will lead to less of it being wasted and more being saved for other times when the grid needs it.
One of UK grid connection queue digital twins for solar assets and curtailment is the NESO Grid Connection Simulator Tool. The NESO Grid Simulator Tool is a cloud-based platform that allows asset owners to connect their models at designated connection points for electromagnetic transient (EMT) studies without having access to sensitive network data. The Grid Simulator Tool allows multiple people to participate (and keeps the data separate from other parties) and is used for streamlining grid connection studies to speed up renewable integration. The tool removes the need for NDAs with asset owners, so it speeds up the process for accepting new connections and identifies any issues quicker so that they can be rectified quicker and there’s therefore more chance of a connection being accepted.
Digital twins for solar asset forecasting and performance
Whilst digital twins have a lot of potential for managing the grid at a system level, digital twins are beneficial for solar PV forecasting and ensuring solar performance at the asset level. At the asset level, digital twins can simulate the performance of a solar panel under different irradiance, the impact of temperature on module efficiency, how shading, dust accumulation, and different weather conditions affect performance and panel/module array power output, if panels are misaligned and are causing performance degradation, and spot long-term degradation patterns—which provides more accurate solar PV forecasting for different panels and finds the most optimal configurations for an installation. For example, if the digital twin shows that a panel has a poor performance, the maximum power point tracking (MPPT) algorithm can be adjusted to optimise the panel’s output.
Digital twins can also be used to manage individual panels through more intelligent controls to improve their efficiency and reduce downtime through optimised maintenance schedules. If any panels in an installation are underperforming, or if a fault/anomaly against historical data is detected, they can be investigated. Prior to a maintenance schedule, virtual inspections can take place on individual panels which minimises the physical interaction with each panel, so this negates any pre-maintenance inspection downtime as well. This can be done individually for each panel in an installation, as well as for ancillary equipment attached to solar installations, such as inverters.
Digital twins also shift the maintenance focus from reactive maintenance to predictive and preventative maintenance, lowering downtime and ensuring the longevity of assets because any issues are sorted out before they cause further degradation and/or damage to the solar installation (and attached equipment). Once solar panels are nearing the end of their useful life, digital twins can also help to plan strategies for replacing them by identifying the trade-offs and operating yield of continuing to use old panels vs the cost and efficiency of new panels to find the best time to replace them and maximise ROI.
Alongside solar PV forecasting and solar performance simulations on individual solar assets, digital twins can also be used with hybrid systems where solar panels are co-located with BESS. There’s a lot of dynamic interactions between storing and releasing the energy from the BESS Vs using the directly captured energy, and these interactions can be better managed with digital twins. Digital twins can help to better coordinate energy flows between the grid, inverter system, BESS and individual solar assets to maximise energy harvesting to maximise the ROI on the energy captured for asset owners.
AI-driven scenario planning for high renewable penetration
Digital twins are increasingly using AI to increase the adaptability and intelligence of the virtual environment. The development of AI digital twins helps with advanced scenario planning within the grid and helps with facilitating renewable penetration. This is because the AI can handle and process much larger swathes of data that not only helps to produce more accurate outcomes but also makes the digital twin better at detecting anomalies and predicting maintenance needs before they occur—as the current data can be compared against ‘normal’ historical data to find anomalies that traditional models can’t find which represent deterioration. As renewable penetration continues to increase within the UK grid; AI digital twins are going to become more important for managing their integration and ongoing operations.
Digital twins integrated with AI also have the capability over time to become self-learning systems that improves its own models using the data that is fed into it over time. This historical data analysis is why AI digital twins are good at energy forecasting for solar panel systems because they can quickly and accurately analyse the historical data against the real-time data. Solar PV plants need to change their operational requirements when they degrade, so AI-enabled digital twins can minimise their degradation over time by optimising their energy yield and operational parameters. This analytical capability also helps AI digital twins to effectively test grid stability under different renewable penetration levels before infrastructure investment decisions are made.
Whilst all digital twins can provide simulation environments to test solar assets, the integration of AI provides a greater degree of foresight and control for operators. This not only includes renewable penetration in different areas of the UK, but also ongoing support and renewable management for grid service operations such as voltage regulation, frequency regulation, and congestion management. These AI digital twins can also support grids with high levels of renewable penetration, as these grids are more susceptible to bottlenecking and causing thermal constraints in the transmission network.
Within the realm of AI digital twins, Monte Carlo simulations are also highly beneficial as they can be used inside the digital twin to optimise and stress-test the grid under extreme operational and environmental conditions—including extreme weather events and rapidly fluctuating renewable outputs. Monte Carlo simulations specifically add randomness to the virtual scenarios being tested, so instead of just testing our potential outcome, the simulations can run multiple (up to thousands) of different and randomised scenarios to see how the solar assets and the grid react/cope. AI digital twins running Monte Carlo simulations provide a virtual environment to ensure grid stability and reliability with increasing renewable penetration as thousands of simulations can be run quickly whereas traditional models can take hours to compute a single complex scenario.
What this means for UK solar developers and asset owners
For solar developers in the UK, digital twins can improve the performance, longevity, and monitoring capabilities of solar PV plants to reduce operational and maintenance costs—as assets can be pre-emotively fixed or replace before they fail and cause unexpected downtime.
Digital twins are also a more efficient system planning tool for grid operators and for asset owners attached (or looking to attach) to the grid because they provide better grid connection modelling capabilities that speeds up connection timelines. Once attached, measuring the real-time performance, and testing them under different environmental conditions, maximises their efficiency yield to provide a greater ROI for solar developers.
For asset owners, digital twins can monitor the environmental factors and the renewable energy output to prevent curtailment through more accurate and advanced forecasting. For assets that are susceptible to curtailment, digital twins can help UK solar developers and asset owners with BESS siting and pairing decisions to maximise the generation, storage and sale of solar energy to the grid and maximise ROI.
For asset owners and solar developers in the UK, digital twins ultimately lead to quicker decision making, cost savings because fewer physical assets need to be tested (further reducing downtime) and provide a better chance of having their assets connected to the grid in a timely manner. For those who focus on sustainability, digital twins provide solar developers in the UK with the ability to create cleaner sustainability reports for compliance and investor reporting because the digital twin can track both performance and emissions in real-time.
