Digital Twins for India’s Smart Grid Transformation

India’s energy landscape is undergoing rapid transformation due to increasing decarbonisation and decentralisation. With the increasing digitalisation, the use of Internet-of-Things (IoT), and evolving Artificial Intelligence (AI) technologies, the ongoing efforts in Smart Grids (SG) implementation shall enable effective integration of stochastic renewable energy (RE) sources and mushrooming micro-grids, catering to grid-powered electric vehicles, enhancing grid resilience, security, and sustainability. Through data transparency, SG helps implement strategies to optimise power generation, transmission, and distribution asset utilisation, and frameworks to achieve a commercialisable and sustainable energy system. A typical SG (Fig.1) shall comprise seven domains with energy and information flow among them. The realisation framework includes four layers. Red lines denote the legacy energy flow, and blue lines represent information flow across all domains through Information Communication Technology (ICT) infrastructure, allowing them to interact and impart the intelligence required for an SG.

Fig.1. Typical framework of an SG

SG implementation frameworks include perception, network, platform, and application layers (Fig.1). In the perception layer, all elements of the power network are interconnected to enable complete visualisation of the state-of-the-grid. The network layer ensures ubiquitous and round-the-clock, reliable communication, while the platform layer enables digital management, making the grid controllable. The top application layer through dynamic observability (understanding changes in real-time) facilitates the implementation of intelligence / smart capabilities such as energy/power demand response, resilience (ability to withstand, adapt to, and recover quickly from disruptions due to natural disasters, equipment failures, or cyberattacks), analyse market dynamics (interplay of factors that influence the electricity supply and demand), as well as identify techno-economic opportunities (such as incorporation of microgrids, decentralized renewable energy systems, and vehicle-to-grid/V2G technologies). Cybersecurity is an important aspect that must be ensured across all layers using firewalls, intrusion detection systems, and data encryption to prevent unauthorised access and data breaches that could disrupt power delivery and compromise the confidentiality and integrity of sensitive data.

Of the 180 smart grid projects implemented worldwide, effective execution is crucial for creating a robust, intelligent, and secure grid. This approach can lead to an energy saving of 15 percent and a 20 percent reduction in emissions, resulting in a return on investments in approximately five years.

The Indian government-initiated National Smart Grid Mission (NSGM) in 2015 primarily focused on realising the perception layer (Fig.1), which includes an Advanced Metering Infrastructure (AMI) and rugged ICT, which form the backbone for effective SG implementation. In line with the requirements of the perception layer, the Grid Controller of India Limited is implementing various projects on Wide Area Management System and Control (WAMCPS) using synchro-phasors (time-stamped grid operating parameters) at the transmission level, operationalising 60 synchro-phasor measurement units (PMU).

Under the Unified Real Time Dynamic State Measurement (URTDSM) Project, the Power grid is installing 1740 PMUs to cover networks >400kV and ensure enhanced dynamic security monitoring and visualisation of the overall network at 356 substations with 34 control centres. As of early 2025, 1093 PMUs that were installed on 400 kV lines and 148 on 765 kV lines enable grid monitoring at the national level. As of March 2025, about 25 million smart meters were installed at the consumer level, and this needs to be accelerated. The Government of India launched the Revamped Distribution Sector Scheme (RDSS) in 2021, not only to extend financial support for regional smart meter deployment and maintenance, but also to expand the domestic manufacturing capacity to produce smart meters within India.

Digital twin (DT) is a virtual, real-time replica of a physical system or process that uses data from sensors to mirror its real-world counterpart's behavior and performance

With the ongoing realisation of the perception layer, to understand practical implementation of perception along with the network layer /ICT and subsequent upstream layers, under the NSGM, 12 SG demonstration pilot projects involving US$30 million are undertaken in the regional grids. The SG knowledge centre in Manesar (Haryana) supports the transformation and hosts training infrastructure. These SG pilots, encompassing smart metering, radio frequency/power line communication/GPRS communication, peak load management, distribution transformer monitoring units, net metering, outage management systems, and roof-top solar integration, showcase India’s holistic approach to SG implementation.

Digital twin (DT) is a virtual, real-time replica of a physical system or process that uses data from sensors to mirror its real-world counterpart's behavior and performance (Fig.2). By integrating the DT with SG, the operation personnel, in addition to monitoring the grid health in real-time, can simulate influence of changes/expansions in the grid (including large-scale renewable integrations), examine resilience to weather events or cyber-attacks, perform predictive maintenance, optimise grid operations for better efficiency and reliability, and make informed decisions before implementing changes in the physical system or policy decisions.

SG implementation allows real-time interaction with the physical electricity network, while DT facilitates the creation of a virtual replica that updates with information from various sources, including geographic information systems, engineering and maintenance data, and operational supervisory control and data acquisition from sensors, energy meters, actuators, and field controllers. Imparting AI capability to the DT offers solutions to achieve resilience and effectively manage electricity networks in real-time, as well as implement long-term strategies. Based on technological maturity and application, DTs are classified into descriptive, informative, predictive, prescriptive, and cognitive types. The maturing Cognitive Digital Twins (CDT) are expected to become self-conscious in the near future, capable of making decisions by themselves.

Fig.2. Architecture of AI-enabled Digital Twin

To reap its advantages, DT are being implemented in various power distribution networks and SG across the world. Predictive DT used in Japan and Singapore power networks monitors transmission assets and predicts outages, based on vulnerabilities to extreme weather conditions and historical performance of similar assets. In the United Kingdom (UK), National Grid, with Utilidata and Sense, is planning to establish a DT to map power flow, voltage, and the health of the infrastructure from the substation to consumers. In the United States (US), San Diego Gas & Electric has developed an AI system that averts power outages by monitoring infrastructure and alerting local power companies to any potential failures, well in advance. The State Grid Corporation of China (SGCC) uses DT to model and analyse the performance of its grid infrastructure, improve fault detection, and enhance grid resilience in response to natural disasters and rapid urbanisation. TenneT (European electricity transmission system operator) has deployed DT to model the impact of renewable energy (RE) sources on the grid and optimise cross-border energy flows between Germany and neighbouring countries.

A CDT could help analyse the power grid in near real-time, well in advance, to understand grid resilience and the influence of changes by analysing multiple hard-to-predict “what-if” scenarios.

A CDT could help analyse the power grid in near real-time, well in advance, to understand grid resilience and the influence of changes by analysing multiple hard-to-predict “what-if” scenarios. These measurements from PMU, combined with other data sources such as smart meters, enable CDT to closely mirror the physical grid’s behaviour, facilitating improved monitoring, control, and optimisation. The CDT shall enable enhanced dynamic observability for increased situational awareness, improved grid stability and reliability, real-time predictions, dynamic protection coordination, optimised grid operations in real-time, cybersecurity enhancements, training and simulations handling operational scenarios and emergencies (Fig.3).

Fig.3. Role of Cognitive DT in smart grid

In an SG, the PMU-fed CDT with robust communication networks, interoperability standards, and an AI-enabled application layer will enable seamless integration of various grid components and systems, leading to a more intelligent, resilient, and efficient SG. The influence of time-transients (ranging from microseconds to several hours) likely in modern power systems can be analysed using accurate and high-fidelity CDT models. Ensuring real-time communication and low latency for critical resilient operations during high-volume data scenarios, especially amid network congestion, requires careful attention.

In addition to the support of transforming the reactive grid to a proactive and intelligent SG, a machine-learnt prescriptive twin (in non-real time) shall take into consideration the installed power generating capacity, current power generation portfolio ( coal, hydro, solar, and wind), capital expenditure (CAPEX) and multiple prevailing policies, shall define the infrastructure essential to achieve a resilient SG topology and the resource requirements (land, water, transmission, investments) for strategic expansions. The AI-enabled CDT, considering the power generation portfolio, the likely demand change and the OPEX and the grid-stability requirements, can support the grid operations in real-time by providing advisories for the best techno-economic energy resource (as a response to meet the demand), dynamic observability to simulate “what-if” scenarios of interest and support healing decisions during a grid fault. Self-healing refers to a system's ability to automatically isolate a faulty section of the power network, allowing the healthy parts to continue operating. Real-time visibility is crucial as it helps identify potential faults or inefficiencies before they develop into significant issues. This proactive approach reduces downtime and enhances the grid’s reliability. (Fig.4).

CDT can model the integration of RE sources, assess the impact of new policies or market conditions, and optimise the operation of distributed RE resources. This capability is particularly vital in an SG environment where decisions must account for complex variables and the need for real-time responses. By understanding how different factors influence energy consumption, utilities can better manage peak power loads, reduce energy costs, and enhance customer satisfaction through personalised services.

Fig.4.Role of DT from planning to holistic grid assurance 

The AI-enabled Self-conscious DT can carry out actions (on its own, without human intervention) towards self-healing during system disturbances and cyber-attacks. It can enable advanced fault-detection and isolation techniques, helping utilities quickly identify and mitigate faults (prevent reoccurrence) in the large grid. By simulating fault conditions and response strategies, a self-conscious DT can support the development of more robust grid architectures and emergency response plans, enhancing overall grid resilience against natural disasters, cyber attacks, and other disruptions.

The AI-enabled Self-conscious DT can carry out actions (on its own, without human intervention) towards self-healing during system disturbances and cyber-attacks.

While the implementation of SG in India is progressing, the development of the digital twin of the Indian electricity network (which could be envisioned as a twin of twins), which shall be implemented in the application layer of the SG, shall help in the transformation to a future-ready, sustainable, and resilient energy ecosystem for India.

Dr N. Vedachalam is a Senior Scientist and Programme Director at the National Institute of Ocean Technology, an autonomous ocean research centre under the Ministry of Earth Sciences. He has previously worked with the Birla Group, General Electric, and Alstom Power Conversion France.

• Developing and Emerging Economies
• Artificial Intelligence
• India
• Advanced Metering Infrastructure
• AI enabled Digital Twin
• Cognitive Digital Twin
• Digital Twin in Smart Grid
• Grid Resilience and Reliability
• Indian Power Grid Modernization
• National Smart Grid Mission
• Phasor Measurement Units
• Smart Grid Transformation in India
• Smart Meter Deployment in India

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