By Srinivas Tumuluru, Executive Director, Engineering, GRID-India
India’s grid is one of the largest and the most complex in the world. It operates across a network of five regional grids – Northern, Western, Southern, Eastern and North-eastern regions, each with unique characteristics in terms of generation sources, load profiles and infrastructure challenges. The Indian grid’s flexibility needs are growing with the increasing penetration of variable renewable energy, distributed generation and shifting demand patterns. This also means that the grid now depends more on ancillary services such as frequency regulation and reserves to keep operations stable. Hence, operational resilience in the face of contingencies is now a critical requirement. These challenges are further compounded by evolving market mechanisms and regulatory mandates.
India’s commitments under COP26 include a reduction of 1 billion tonnes of CO2 emissions by 2030, achieving 50 per cent of its energy requirement from renewable sources and installing 500 GW of non-fossil fuel capacity. Toward this transition, investments in flexibility enhancement, particularly through storage, are being fast-tracked. Renewables are projected to contribute 20-30 per cent of the daily energy demand and even higher during specific periods, increasing the urgency of flexible, intelligent grid operations. Meanwhile, emerging system challenges include prolonged frequency deviations above 50.05 Hz and insufficient down reserves, particularly during times of high solar generation. These conditions are further exacerbated by changing load profiles due to electric vehicle (EV) charging, induction cooking and other behavioural shifts. In this context, the ability to harness flexibility and essential reliability services from inverter-based resources becomes vital. To cater to these diverse set of complex requirements and fulfil load generation balance in all scenarios, artificial intelligence (AI) is increasingly seen as a necessary tool to support forecasting, optimisation, control and security functions at scale. GRID-India has identified several practical AI applications aligned with the need for flexible and real-time grid operations. In the domain of load forecasting, machine learning (ML) models are being deployed to deliver time-series predictions. These are being used for day-ahead electricity demand forecasting, which allows for better alignment of scheduling with expected consumption patterns. For renewable energy forecasting, similar ML models assist in handling variability in solar and wind generation. In both cases, use of AI/ML techniques enables better planning and operational visibility across despatch centres. Demand response is another promising use case. Utilising AI recommendations to support dynamic load control through management of smart appliances or EV charging infrastructure has been started in several countries. These interventions help reduce peak demand and enhance grid stability. Further, with respect to outage management of grid elements, a few predictive maintenance applications that use AI to detect anomalies and predict equipment failures have been developed and are in use at some utilities. For example, ML algorithms can analyse operational data to flag emerging issues in transformer health before they lead to outages. Additionally, AI also supports grid optimisation functions such as facilitating real-time balancing and congestion management. Further, AI can also be applied in energy storage despatch to determine optimal charging and discharging schedules. This is particularly valuable for battery operations during peak demand periods, where intelligent algorithms can maximise storage utility. In fault detection and isolation, AI-based pattern recognition and diagnostics are being tested for faster localisation of outages within distribution systems. This reduces downtime and enhances operational resilience. There are several AI techniques that are currently in use, which aid in flexible grid operations. Some specific use cases include ML for both demand and renewable energy generation forecasting, Deep Learning for adaptive optimisation and natural language processing (NLP) for analysing operational reports, logs and incident records. The benefits of AI in these areas are already becoming evident. It supports optimised grid performance, smoother renewable integration and reduced operational and maintenance costs. Operators gain a better understanding of system conditions and are able to make data-driven, timely decisions. AI also enhances resilience by identifying and addressing issues before they escalate.
Key initiatives by Grid-India
Short-term load forecasting
Grid-India is actively developing internal use cases as part of this AI transition. One such initiative is the development of AI models for short-term load forecasting, where advanced algorithms such as artificial neural networks based on the Levenberg Marquardt algorithm and decision-tree-based gradient boosting models (XGBoost) are being used at the National Load Despatch Centre. These models are robust in handling non-linear relationships and large datasets. Some of the key input features include lagged demand, weather parameters and time-based indicators. The models have achieved a Mean Absolute Percentage Error of 2–5 per cent. Furthermore, forecast accuracy is being improved with the development of recurrent neural network-based LSTM models that are currently under refinement. In the future, some of these improvements will include hybrid modelling that combines strengths of different algorithms and enhanced weather data integration such as inclusion of the heat index to better account for consumer behaviour during extreme temperatures.
AI-based renewable energy data cleaning
In collaboration with IIT Kanpur, GRID-India has developed an AI/ML engine to clean real-time renewable energy data for better forecasting outcomes. The pilot has been implemented at a 250 MW solar power plant in the southern region. As part of this effort, three models have been developed using approximately 200 features, including direct point values, historical point values and engineered mean values. These models are designed to address various operational anomalies, such as non-updating data from multiple inverters, partial data availability, missing weather data and station-wide real-time data lapses. This cleaned and validated data is now being used to improve forecasting algorithms and enhance grid predictability.
AI applications in SOC
The deployment of AI in grid operations by GRID-India is supported by a robust system architecture, which comprises integrated information technology (IT), operational technology (OT) and connectivity platforms. This layered control environment enables seamless data exchange, real-time system monitoring and actionable intelligence for despatch centres.
To manage the distributed critical resources in real time on pan India level, GRID-India has established a 24×7 security operations centre (SOC) for both IT and OT systems to strengthen the cybersecurity of its digital and decentralised infrastructure. The SOC integrates AI-powered tools such as Security Information and Event Management, User and Entity Behaviour Analytics (UEBA), Network Behaviour Anomaly Detection, and Security Orchestration and Automated Response (SOAR).
UEBA
A closer look at the UEBA framework deployed by GRID-India highlights its layered approach to behavioural threat detection and response. Behaviour profiling and baseline modelling that were built using unsupervised learning algorithms can help define normal user patterns, which allow the system to flag deviations indicative of potential threats. For real-time anomaly detection, models such as isolation forests, autoencoders and LSTM networks are used to track temporal anomalies. Through peer group analysis, UEBA clusters users with similar operational roles to identify outliers, while AI-driven dynamic risk scoring aids security analysts in prioritising high-risk incidents. Predictive modelling supports insider threat detection by identifying long-term behavioural drift or misuse patterns. Additionally, the integration of UEBA with SOAR platforms enables automated threat response workflows, significantly reducing response time and limiting damage.
The way forward: From pilot projects to operations
As AI applications in grid operations move from pilot projects to operational practice, the early results have demonstrated measurable improvements in efficiency, reliability and security. The broader AI toolkit already in place includes ML for forecasting and classification, deep learning for non-linear patterns and complex datasets, reinforcement learning for adaptive grid control and NLP for parsing unstructured data such as incident logs. These tools have helped streamline grid performance, manage variability and reduce operational costs. The perceived advantages of AI in grid operations are multifold. This includes optimisation of grid performance, variability from renewable energy sources is better managed and operator workloads are reduced through automation. Security posture has improved through proactive intrusion detection. Most importantly, decision-making across operational teams is now increasingly based on real-time data and machine intelligence.
However, challenges remain. For instance, the quality and consistency of input data need continuous improvement. Model development requires domain expertise and technical depth, and integration with legacy infrastructure can be complex. Addressing these gaps will be essential to scale AI from niche applications to a universal layer in grid management.
Looking ahead, GRID-India plans on building an operational landscape, where AI not only supports human decision-making but also begins to enable semi-autonomous grid management. From dynamic resource despatch to self-healing fault detection and distributed asset coordination, the future grid should be shaped by systems that can learn, adapt and optimise in real time. As the share of renewable energy increases and as digitalisation is integrated into every layer of power sector operations, the adoption of AI is no longer optional. As the foundational capabilities are already in place, the task now is to institutionalise best practices, build internal capacity and scale proven models across the country’s power ecosystem.
