This creates a level of operational complexity for which traditional approaches are insufficient. Software precision — through forecasting, simulation, and optimisation — becomes essential to maintaining reliability.As India accelerates towards a renewable-first energy system, AI-driven forecasting, digital twins, and cybersecurity are becoming essential for grid reliability and operational efficiency. The article explores how software-defined energy infrastructure is reshaping power system management in the clean energy era.
India’s energy transition is no longer just about adding renewable capacity — it is about reimagining how power systems are planned, operated, and secured in a software-defined world.
India’s energy transition is entering a defining phase. With an ambitious target of 500 GW of non-fossil capacity by 2030, accelerating electrification, and a rapidly expanding digital infrastructure, the country is not merely adding renewable capacity — it is fundamentally reshaping how power systems are planned, operated, and secured.
At the heart of this transformation lies an often-understated reality: the future grid will be software-defined. Artificial intelligence (AI), digital twins, and secure digital architectures are no longer peripheral enablers — they are becoming core infrastructure for managing a renewable-first energy system.
In a renewable-heavy system, variability translates directly into financial and operational risk. Solar and wind generation are inherently intermittent. In markets such as India, this variability affects:
• Deviation Settlement Mechanism (DSM) penalties
• Market participation in the Day-Ahead Market (DAM) and Real-Time Market (RTM)
• Curtailment and lost revenue
• Grid balancing costs
AI-driven forecasting changes this equation. Using narrow AI models — including gradient boosting, random forests, and time-series algorithms — combined with satellite data, weather models, and plant-level telemetry, developers can significantly improve forecasting accuracy for short-term horizons. In specific contexts, accuracy can approach 98 per cent. Even a 2–3 per cent improvement in forecast precision can translate into a substantial reduction in DSM penalties and meaningfully improved market positioning.
In India, where scheduling and settlement mechanisms demand operational discipline, the application of AI is increasingly shifting operations from reactive correction to predictive control.
Electricity demand in India is growing at 6–8 per cent annually — among the fastest rates globally. Simultaneously, the system is integrating renewables at massive scale, managing extreme weather events (heatwaves, monsoons, cyclones), expanding critical infrastructure, electrifying mobility, and digitising operations. The challenge is one of scale, speed, and complexity — all at once.
“India is building the plane while flying it.”
This creates a level of operational complexity for which traditional approaches are insufficient. Software precision — through forecasting, simulation, and optimisation — becomes essential to maintaining reliability.
A digital twin of the grid represents a significant evolution in system planning and operations. It is not merely a visualisation tool, but a living, dynamic replica of the power system that integrates physical infrastructure, weather inputs, demand patterns, and market signals.
From a business perspective, this enables optimised asset utilisation, reduced curtailment losses, better transmission planning, lower reserve requirements, and improved investment decisions. A digital twin can simulate:
• Renewable variability across regions
• Transmission congestion during peak generation
• Demand surges due to extreme weather or electric vehicle (EV) adoption
• Market volatility, including DSM exposure and RTM pricing
• Storage dispatch strategies
• Cybersecurity and physical disruptions
This allows decision-makers to optimise both operational and financial outcomes before risks materialise. In a renewable-first system, uncertainty is not merely a technical challenge — it is a cost driver.
“Digital twins convert uncertainty into informed decision-making.”
As power systems become increasingly digital, cybersecurity emerges as a critical priority. The integration of smart meters, distributed energy resources, AI platforms, and market automation significantly expands the attack surface. In this context, cyber risk is no longer confined to IT systems — it directly threatens grid stability and national infrastructure.
Smart grids rely on an expanding ecosystem of connected devices — smart meters, inverters, EV chargers — alongside cloud-based platforms, API-driven integrations, and automated market systems. While these enable efficiency and scalability, they also introduce new vulnerabilities. A cyber incident in such an environment can lead to operational disruptions, significant financial losses through market exposure and penalties, reputational damage, and potential regulatory consequences. Global incidents have already demonstrated that cyberattacks on energy infrastructure are not theoretical — they are a present and material risk.
For India, this risk is amplified by large-scale digital deployments, highly distributed operational structures, extensive third-party system integration, and the rapid adoption of AI and automation.
Addressing this requires a design-first approach to cybersecurity, including:
• Zero-Trust Architecture — verifying all systems and users at every access attempt
• Strict OT–IT Separation — keeping operational systems insulated from IT vulnerabilities
• Fail-Safe AI — designing systems that degrade safely under uncertainty or anomaly
• Real-Time Anomaly Detection — leveraging AI to surface actionable insights
• Secure firmware and device-level standards
Cyber resilience must scale alongside digitalisation. It cannot be retrofitted.
A new dimension is now coming into focus: AI itself as a source of energy demand. India’s AI growth is likely to be inference-heavy — with models deployed continuously across applications in energy, mobility, governance, and industry. While individual inference tasks consume relatively little energy, their cumulative impact can be substantial.
This creates a challenge for regulators: understanding where AI-driven energy demand originates and how it evolves over time. Traditional metrics such as Power Usage Effectiveness (PUE) focus on infrastructure efficiency, but fall short of capturing computational productivity. What is needed are new metrics centred on value created per unit of energy consumed.
Emerging examples include:
• Energy per AI task (kWh per inference or training job)
• Carbon intensity per workload
• Utilisation-adjusted efficiency (productive versus idle compute)
India’s energy transition is no longer a question of deploying technologies in isolation. It demands integrated system thinking — where AI, digital infrastructure, and cybersecurity work in concert to enable a reliable, scalable, and sustainable grid.
• Forecasting enables predictability.
• Digital twins enable preparedness.
• Cybersecurity enables resilience.
Together, they form the software backbone of a renewable-first power system. India has a unique opportunity to lead grid modernisation — not merely by adopting global best practices, but by defining new ones. By embedding intelligence, resilience, and efficiency into its energy systems from the outset, India can set a standard for others to follow.
About the Author
Ashish Panigrahi is a technology and business leader with over 25 years of global experience in digital transformation, data-led innovation, and sustainability. He is Co-founder of Domainxeed, a digital product startup focused on AI-enabled, cyber-resilient solutions for the Energy & Utilities sector. Previously, he held senior leadership roles at IBM Consulting and Tata Consulting, leading large-scale transformation programmes across APAC and North America. Ashish holds an M.Tech. in Earth Sciences from IIT Kharagpur and a business management qualification from IIM Bangalore. He also serves as a Director at the Indian Institute of Sustainable Development, contributing to policy and innovation at the intersection of technology and sustainability.