As renewable energy portfolios evolve towards hybrid, firm and despatchable renewable energy (FDRE) and round-the-clock (RTC) projects, digitalisation, artificial intelligence (AI) and data analytics are becoming increasingly important for improving asset performance and decision-making. In an interview with Renewable Watch, Narendra Somoshi, Chief Asset Creation and Performance Management Officer, Aditya Birla Renewables Limited, discussed the company’s approach to digitalisation, AI/machine learning (ML) applications and project performance optimisation; evolving portfolio; and the key challenges facing renewable energy development. Edited excerpts…
What are the key priority areas for the company across different functions of digitalisation and AI, and where do you see the greatest potential for these technologies to improve operations and decision-making?
AI and ML are expected to play a key role across the renewable energy project life cycle, starting with engineering and design. AI-based tools can support layout planning and help optimise project design from an early stage. Another important application is modelling the technology mix for FDRE projects. For a 100 MW RTC project, for instance, determining the right combination of wind, solar and storage is critical. AI and ML can support this modelling by assessing different combinations and their cost and performance implications.
At the organisational level, FY 2027 is being seen as a year of consolidation of digitalisation, with the focus primarily on project execution and operations. One key area is project monitoring and planning. A project monitoring office has been established, with project planning and engineering being managed through digital systems. The aim is to digitise the entire process, from the initial drawings through the lifetime of the asset. Another area is the digitisation of daily generation reports, moving from Excel-based reporting to a centralised portal. The third area is centralised monitoring and analytics. Data from project sites is collected at 10-minute intervals and integrated into a centralised monitoring system called “Drishti”, which enables real-time monitoring and supports predictive maintenance.
AI and ML are being applied across three key areas: predictive maintenance to improve asset performance and reliability; optimisation of engineering and project design through simulations and analytical models; and improved decision-making and enterprise productivity through data-driven management. Our focus is on digitalisation, AI/ML applications, digital twins and data analytics. The key focus is to move beyond data collection and convert data into business outcomes by improving asset productivity, accelerating decision-making and enhancing operational efficiency.
With increasing focus on maximising output from renewable energy projects, what measures, beyond digitalisation, are you adopting to improve cost efficiency and optimise project performance?
There is a greater focus on maximising the output from the capacity that has already been added. The emphasis is shifting from simply measuring MWs of capacity added to MWh generated and ensuring optimum plant performance.
One of the key changes is the shift from time-based availability to production-based availability. Rather than looking only at whether a plant is available for a certain percentage of time, the focus is on ensuring that the plant is available when the renewable resource is available. For solar projects, this means ensuring availability during periods of adequate solar radiation, while for wind projects, the plant needs to be available when wind resources are favourable.
Performance is also being optimised against the available resource. For solar projects, the performance ratio is being monitored to assess how much power is generated against the available solar radiation. For wind projects, power curve measurements are used to assess whether the plant is delivering the expected output at the prevailing wind speeds.
Other measures include the use of robots for solar panel cleaning and soiling reduction, which is also relevant given expected water constraints in the future. String monitoring and digital tools are being used to identify data and performance outliers, which can then be analysed by the technical and engineering teams.
How would you assess the quality and effectiveness of the digital tools and software currently available? Are they adequately addressing the day-to-day challenges faced by asset operators?
The quality and effectiveness of digital tools have improved, although the selection of the right technology remains important. Over the past two years, we have evaluated around seven to eight technology solutions through proof-of-concept exercises with three technology partners, following which one solution was shortlisted. Cost remains an important consideration. A lower-cost solution may ultimately prove more expensive if it does not meet the required requirements in the long term. Therefore, careful evaluation and selection of technology products is important. The current focus has been on moving away from manual processes, Excel-based work and supervisory control and data acquisition (SCADA) systems towards digitalisation. The next step is to build on this digital foundation through AI and ML applications. The company expects to reach a certain level of maturity in these applications over the next year, with further opportunities for AI and ML development going forward.
With the tightening of DSM norms, do you believe the current framework is fair and reasonable for renewable energy developers?
The tightening of deviation settlement mechanism (DSM) norms is fair from the grid operator’s perspective, but it remains challenging for developers because of the variability of renewable generation. Wind and solar generation are highly dependent on weather conditions, and sudden changes in these conditions can make accurate forecasting difficult, even on an intra-day basis. There are solutions to address this variability, particularly through energy storage and pumped storage. However, these solutions also add to project costs. A suitable combination of wind, solar and storage will therefore be important to provide FDRE and manage DSM requirements.
Although there has been significant progress in forecasting, scheduling and analytics over the past decade, challenges remain. One is the lack of sufficient and reliable measurements related to climate and weather conditions. The second is the cost involved in meeting increasingly stringent DSM requirements. There is therefore room for improvement in the measurement and forecasting of such conditions. Better data and measurement, combined with storage solutions, can help manage these variations. While these parameters are improving, further tightening of DSM bands could remain challenging for renewable energy projects, particularly from an investment and cost perspective.
How is the company navigating some of the long-standing challenges in the sector, such as land availability, grid connectivity and delays in PPA signing?
With a large expansion planned, Aditya Birla Renewable is looking to secure the required infrastructure well in advance. Given the scale of its portfolio target, the company is assessing land and grid connectivity and aims to finalise these requirements two to three years ahead of project development. There is also a focus on optimising the use of the available land and infrastructure. At the engineering and design stage, the objective is to determine how much capacity and generation can be achieved from the available resources and infrastructure.
Could you give us an overview of Aditya Birla Renewables’ current portfolio?
Aditya Birla Renewables currently has a portfolio of over 4 GW operational and under construction projects and serves a mix of offtakers. The company has PPAs with Gujarat Urja Vikas Nigam Limited, the Odisha government and the Karnataka government. The client portfolio also includes internal commercial and industrial (C&I) customers, including Aditya Birla Group companies such as UltraTech Cement, Grasim and Hindalco. In addition, the company has entered the external C&I segment, having recently signed a contract with Gujarat Alkalies and Chemicals Limited. Expanding the external C&I market is an important area of future growth for the company.
The company has so far developed a 2.5 GW state transmission utility portfolio and is now moving towards a Central Transmission Utility portfolio. The company will also commission close to 1 GW of capacity over the next two to three months, across projects in Fatehgarh, Rajasthan and Gujarat. Looking ahead, Aditya Birla Renewables aims to scale up its portfolio, with its earlier target of 10 GW by 2030 now revised to 20 GW by 2030.
How is Aditya Birla Renewables adapting its strategy to the evolving dynamics in the renewables sector?
Today’s renewable energy portfolios are evolving from conventional wind and solar projects to hybrid, RTC, peak power and FDRE projects. The focus is now on delivering more predictable and reliable renewable power. For FDRE projects, there is a need to target a planned load factor of around 85 per cent, with variations limited to around 2-3 per cent. This requires greater accuracy in power forecasting, particularly in view of the applicable DSM bands. Our C&I customers, including Hindalco and UltraTech, are also seeking FDRE power. Meeting these requirements requires a combination of wind and solar generation with storage solutions. Pumped storage is also being pursued, alongside battery energy storage system (BESS) solutions. The combination of wind, solar, pumped storage and BESS is expected to play an important role in supplying FDRE power, including to data centres.
