India’s clean energy transition has entered a decisive operational phase. As renewable capacity scales rapidly across regions, technologies and market structures, the sector’s priorities are shifting from installation speed to sustained performance. Grid volatility, tightening deviation penalties, increasingly complex hybrid project configurations and ageing assets are exposing operational blind spots that conventional practices are no longer able to absorb.
In this evolving landscape, digitalisation is not being adopted merely as an efficiency enhancer, but as a system-wide enabler that links forecasting, execution, operations and strategic decision-making. The central challenge is no longer the absence of data. Instead, it is the ability to convert dispersed, real-time information into coherent, actionable intelligence that supports grid stability, asset reliability and cost discipline. The long-term success of India’s clean energy transition will increasingly depend on how deeply digital tools are embedded across the renewable asset lifecycle, from planning to operations.
Renewable Watch recently hosted a webinar on “Transition to Clean Energy”. This article highlights the key insights from the panel discussion featuring Samir Ahmad, Head – Data Analytics, Juniper Green Energy; Rajesh Grandhe, Head – India Pre-Sales, AVEVA; Devanand Pallikuth, Chief – O&M, Tata Power Renewable Energy; Vinod Sharma, Head – IT and Cybersecurity, Hero Future Energies; and Atul Srivastava, DGM – Asset Management, BluPine Energy.
Grid pressures expose the limits of current forecasting frameworks
Grid integration has emerged as the most immediate pressure point for renewable generators. As the share of variable renewable energy rises, generation volatility is increasing, making deviation management a critical operational and financial concern. With deviation settlement mechanisms becoming more stringent, even small forecasting errors are translating into penalties, curtailment risks and heightened regulatory exposure.
Although machine learning-based forecasting tools have improved accuracy over time, their effectiveness remains constrained by the quality of weather inputs. Existing weather datasets available for India lack the spatial granularity and temporal frequency required for high-precision solar and wind forecasting. Large-area models often fail to capture hyperlocal variations in cloud cover, wind speeds and radiation intensity, limiting their usefulness for scheduling and despatch decisions.
Consequently, developers increasingly rely on external forecasting services calibrated for geographies outside India. This dependence introduces uncertainty and weakens confidence in generation schedules. The implications extend beyond individual projects. Grid operators, faced with large volumes of intermittent power, struggle to maintain balance and system stability, often resorting to curtailment as a fallback mechanism. The discussion highlights that without reliable, hyperlocal weather intelligence tailored to Indian conditions, forecasting accuracy will remain a structural constraint in a renewable-dominated grid.
Execution bottlenecks bring supply chain digitisation into focus
While operational challenges dominate sector discourse, inefficiencies during project execution continue to erode value across the renewable pipeline. Renewable project development involves complex supply chains spanning equipment manufacturing, logistics, site readiness and workforce mobilisation.
Material delays, misaligned deliveries and idle manpower remain frequent challenges, driven by limited visibility into logistics movement and site-level preparedness. In many cases, construction teams reach project sites before critical equipment arrives, inflating costs and extending commissioning timelines. The absence of real-time coordination between supply chain activity and site execution prevents developers from optimising labour deployment and sequencing tasks efficiently.
Digital tools that enable advanced shipment notifications, real-time tracking of equipment and tighter integration between logistics and site teams can significantly reduce these inefficiencies. Applying manufacturing-style execution frameworks to renewable construction allows developers to compress timelines, improve cost predictability and reduce execution risk. As project sizes increase and bidding margins narrow, digitised execution is becoming essential to sustaining project economics rather than a discretionary improvement.
Recasting renewable O&M through digital twins and predictive analytics
As renewable assets mature, operations and maintenance (O&M) has become the primary determinant of long-term performance. Many plants continue to operate based on assumptions embedded during the design stage, even as real-world conditions evolve. Variability in weather patterns, declining radiation levels and progressive equipment ageing are widening the gap between expected and actual generation.
Digital twins are increasingly viewed as a critical tool to bridge this gap. By creating dynamic virtual replicas of physical assets, operators can continuously compare design intent with operational performance. This enables a more accurate diagnosis of whether generation losses arise from external resource constraints or from equipment degradation and operational inefficiencies.
When paired with predictive analytics, digital twins enable a shift from reactive maintenance to proactive optimisation. Historical and real-time data can identify early warning signals such as abnormal temperature trends, efficiency degradation or component stress. This allows operators to intervene before failures occur, reducing downtime and avoiding costly emergency repairs. The value of predictive maintenance is particularly evident for older plants, where component failures are becoming more frequent and warranty coverage has expired, making unplanned outages significantly more expensive.
Moving beyond dashboards to data-driven decision engines
There is a growing gap between data availability and decision usability. Renewable operators today have access to vast volumes of information through dashboards, monitoring platforms and reporting tools. However, much of this data remains underutilised, requiring manual consolidation, interpretation and validation before it can inform action.
Operational teams often spend significant time collecting data from multiple systems, contextualising it and analysing trends. This slows response times and increases dependence on individual expertise. The next stage of digital maturity involves transforming dashboards into decision engines that automatically integrate data, apply contextual logic and generate actionable insights.
Agent-based digital architectures are gaining traction in this context. These systems allow different digital tools to communicate with each other, escalate only relevant alerts and support faster, more consistent decision-making. By embedding intelligence directly into workflows, they reduce reliance on individual judgement and mitigate the impact of workforce churn, a persistent challenge in geographically dispersed renewable portfolios.
Human systems emerge as the weak link in digital resilience
As digital infrastructure expands, exposure to cyber risk increases in parallel. Renewable energy systems are becoming more decentralised and data-intensive, shifting from secure perimeters to distributed edge environments. While advanced cybersecurity tools enhance visibility and monitoring, vulnerabilities continue to stem from human behaviour.
Most cyber incidents originate from basic errors such as phishing attacks, compromised credentials or inadequate access controls. Technology alone cannot address these risks. Continuous training, awareness programmes and simulated attack exercises are essential to build a human defence layer. Equally important is resilience planning. Organisations must be prepared to detect breaches quickly, isolate affected systems and restore operations without cascading disruptions across interconnected assets.
Human systems also play a crucial role in operational resilience beyond cybersecurity. Digital tools must support field teams by providing focused alerts and prioritised actions rather than overwhelming them with data. Acceptance of digital systems improves when they simplify decision-making, enhance safety and reduce manual workload. Successful digitalisation, therefore, depends as much on organisational readiness and workforce alignment as on technological sophistication.
Going forward, digitalisation will play a key role in integrating storage into India’s renewable ecosystem. As firm and despatchable renewable power becomes more prominent, batteries and other storage technologies must operate seamlessly alongside solar and wind assets. This requires digital platforms capable of optimising charging and discharging decisions while simultaneously supporting grid services such as frequency regulation and balancing.
Autonomy in this context does not imply fully unmanned operations but a higher degree of automated decision support. Systems that can evaluate multiple scenarios, trigger predefined actions and continuously learn from outcomes will be essential to managing complexity at scale. At a broader system level, shared data platforms could enable optimisation across regions rather than individual projects, reducing curtailment and improving utilisation of clean energy.
The transition from data-rich but insight-poor operations to intelligent, integrated energy systems will define the next chapter of India’s clean energy journey. Digital intelligence, embedded across forecasting, execution, operations and governance, will ultimately determine whether renewable energy can deliver not just rapid capacity growth, but reliability, resilience and economic value at scale.
