Que: What do you think about how digital tech, like AI, digital twins, and data analytics, can speed up the use of renewable energy?

Ans: Other than stringent policy, digital technology is the single greatest accelerator we have for the energy transition. It transforms the process of energy infrastructure delivery from a linear, often siloed, progression into a dynamic, integrated, and intelligent ecosystem.

Technologies like AI and digital twins are compressing timeframes and de-risking the massive capital investments required for these programmes, allowing us to move faster and with greater confidence.

Today, before a single panel is installed or a turbine erected, we can simulate asset performance and optimise operations in real-time. This is fundamentally changing the economics and viability of renewable projects.

Ultimately, digital tech allows us to move beyond simply building assets to orchestrating intelligent systems. AI provides the predictive brain, data analytics deliver the operational insights, and digital twins create the virtual world to test, validate, and manage every aspect of the renewable energy lifecycle.

This convergence is what is turning our ambitious sustainable policy goals into achievable engineering and operational reality.

Que: Can you share how digital twins are being used in projects like solar parks, wind farms, or energy storage?

Ans: In offshore wind, digital twins are revolutionising the entire lifecycle. During the design phase, they can simulate decades of weather patterns and turbine placements to optimise the farm's layout for maximum energy yield and minimal wake effect. During operations, a digital twin, fed by real-time data from sensors, can predict gearbox failures months in advance. This means we can plan maintenance and avoid costly downtime and emergency repairs in hazardous conditions.

For solar parks and battery storage, digital twins are critical for optimising grid integration and financial performance. A digital twin of a solar farm can model the precise angle of every panel throughout the day to maximise production and predict maintenance needs like cleaning or inverter servicing. For battery storage, the twin simulates charge and discharge cycles against live energy market pricing and grid demand forecasts, ensuring the asset is used to provide maximum value and stability to the grid.

The Pinnapuram Integrated Renewable Energy Project (IREP), in Andhra Pradesh, is a prime example of how digital technologies are driving efficiencies through modern energy programmes.

Using GeoStudio geotechnical analysis software from Seequent (a Bentley Systems company), AFRY, the engineering consultancy, has been able to simulate ground and subsurface conditions and create 3D models of the extensive site footprint to provide it with crucial insights ahead of design. It has gained a complete, holistic geological picture of the site, which will inform and accelerate the design process.

Que: How is AI being used in forecasting renewable energy generation and managing intermittency in grids?

Ans: The Achilles' heel of renewables has always been intermittency – the sun doesn't always shine, and the wind doesn't always blow. AI is our most powerful tool to transform this variability from a critical liability into a manageable, predictable variable.

AI forecasting models are a world away from simple weather reports. They are sophisticated systems that ingest and learn from dozens of inputs simultaneously: hyperlocal weather models, satellite imagery tracking cloud cover, historical performance data from the asset itself, and real-time SCADA system outputs. By identifying complex, non-linear patterns in this data, AI can generate hyper-accurate, probabilistic forecasts of energy generation – not just if the wind will blow, but precisely how much power a specific wind farm will generate in a 15-minute window three hours from now.

This predictive capability is the crucial first step. The next step is acting on it. These AI-driven forecasts are fed directly into the control systems of grid operators and the digital twins of the grid itself. When the AI predicts a significant drop in solar output due to approaching cloud cover, the system can proactively and automatically ramp up power from a battery storage facility or a hydropower dam to seamlessly fill the gap.

Ultimately, an infrastructure digital twin of the entire grid acts as the master orchestrator. It uses the AI forecast as a primary input to run thousands of simulations per second, determining the most stable and cost-effective way to balance supply and demand. This allows grid operators to maximise the intake of renewable energy with confidence, knowing they have an intelligent, automated system ready to manage any fluctuations.

Que: With massive volumes of data coming from sensors, SCADA systems, and weather models, how can renewable energy stakeholders best manage and leverage this data?

Ans: We live in a big data age. The challenge today is not about collecting or storing data; it's about context and accessibility. Often, data exists in siloed, incompatible formats – the engineering data is in one place, the operational data in another, and the geospatial data somewhere else entirely. The key to leveraging this data is to federate it.

This is where infrastructure digital twins provide immense value. A digital twin doesn't necessarily hold all the data, but it understands where it is and how it relates to the physical asset. It provides the crucial context, aligning time-series data from a sensor with its exact 3D location on a turbine, its maintenance history from the asset management system, and its original design specifications. This creates a ‘single pane of glass’ through which stakeholders can visualise, analyse, and act on information, turning raw data into actionable intelligence.

Que: What are the main challenges to scaling up digital innovation in renewable energy?

Ans: Today, the primary barrier to scaling up is not the technology, but rather people, processes and commercial models. These barriers include:

Risk Aversion: Organisational silos between IT (Information Technology) and OT (Operational Technology) often prevent the seamless flow of data required for true digital transformation.

Lack of Data Standards and Interoperability: Without open, common data environments, we risk creating thousands of ‘digital islands’ – highly optimised individual assets that cannot communicate with each other to form a truly intelligent system of systems. Overcoming this requires a commitment to open platforms and a shift in procurement from focusing on the lowest capital cost to the best whole-life value delivered through digital insight.

Que: How can the industry address the skills gap in the digital energy workforce?

Ans: Addressing the skills gap requires a three-pronged approach.

First, we must deepen the partnership between industry and academia to redefine the curriculum for the next generation of energy professionals. The future energy expert is not just an electrical engineer; they are a data scientist who understands physics, or a mechanical engineer who can write Python scripts to analyse performance data.

Second, we must invest heavily in upskilling and reskilling our existing workforce. This means creating continuous learning programs and fostering a culture that values digital literacy as a core competency for everyone, not just a few specialists.

Third, as technology providers, we have a responsibility to make our software more intuitive. We must democratise the use of advanced simulation and AI, empowering engineers to ask complex ‘what-if’ questions and get insights without needing a PhD in data science.

At Bentley Systems, we are working to address the problem through initiatives such as our programme with Enactus, a global non-profit advancing student innovation and entrepreneurship. We announced in June the start of the 2025 iTwin4Good Challenge, an international competition designed to build skills and grow a diverse pipeline of future infrastructure leaders and solution developers.

Que: How do you think the energy systems of the future will look like?

Ans: The energy system of the future will be defined by three ‘D's – Decentralised, Digitised, and Democratised. It will be a highly decentralised network of large-scale renewable projects, microgrids, community solar, and millions of homes and electric vehicles acting as both consumers and producers – or ‘prosumers’ – of energy, all connected and interacting in real-time.

This complex, bi-directional ecosystem simply cannot be managed manually. It will be orchestrated by a fully digitised, autonomous grid, underpinned by a federated system of infrastructure digital twins.

This ‘system of systems’ will be the operating system for our energy landscape, using AI to predict and balance supply and demand, ensuring resilience, and enabling new energy markets. This is not just a technological evolution; it's a fundamental democratisation of power, creating a more sustainable, resilient, and equitable energy future for our planet.