Case study: grid digitalization and digital twins — what's working, what isn't, and what's next

Executive summary

Asia-Pacific's electricity grid is being reshaped by two powerful forces: rising demand from population growth and electrification, and the rapid influx of distributed energy resources such as rooftop solar, battery storage and electric vehicles. Traditional planning tools are ill-suited to manage this complexity. Digital twins - detailed, data-driven replicas of physical networks and assets - offer a way to test scenarios, optimize capacity and anticipate problems before they occur. This case study surveys leading implementations of grid digitalization across the region, highlights what's working and where challenges remain, and provides a framework for product and design teams tasked with building the digital nervous system of the next-generation grid.

Why it matters

Across Asia-Pacific, electricity consumption is growing quickly as economies expand and electrification of transport and industry accelerates. Integrating high shares of renewable energy while maintaining reliability requires better visibility and control of grids. Digital twins provide a virtual environment where operators can simulate the impact of new loads and generation, test grid upgrades, and forecast asset health without disrupting service. These models support more efficient investment in transmission and distribution, enable faster interconnection of clean energy projects and improve resilience to extreme weather. For product and design teams, digital twins represent a shift from one-off infrastructure to continuously evolving, data-driven systems.

Key concepts: digital twins and grid design

A digital twin is a virtual representation of a physical system that is continually updated with real-time data. In power systems this concept is typically split into two layers:

Network twins replicate the topology and electrical behaviour of transmission and distribution networks. They use advanced modelling and simulation to assess how additional loads - such as electric vehicles or rooftop solar - will affect voltage, frequency and capacity. In Singapore, the network twin leverages a Multi-Energy System Modelling and Optimisation framework to evaluate the impact of new demands and recommend necessary upgrades to the grid.
Asset twins create digital replicas of individual components - transformers, switchgear, cables - and monitor their condition using sensor data. Asset twins enable predictive maintenance by identifying potential faults and estimating remaining useful life. For example, Singapore's asset twin monitors equipment health remotely and supports risk-based investment planning.

Digital twins are not monolithic. They require high-quality data from sensors and smart meters, robust communication networks, interoperable software platforms and analytics that turn data into actionable insights. The projects highlighted below illustrate different approaches to building these layers.

What's working: case studies across Asia-Pacific

Singapore: network and asset twins guide future-proof grid planning

Singapore's Energy Market Authority (EMA) and SP Group launched the Grid Digital Twin in 2021 to enhance resilience and support cleaner energy sources. The network twin uses advanced simulation to analyse how electric-vehicle charging and distributed solar will affect the grid and identifies upgrades required under different scenarios. The asset twin monitors transformers, switchgear and cables, providing remote condition analysis to pre-empt failures. A suite of research projects under the SP Group-NTU Joint Laboratory is developing tools such as a risk-based asset investment index, 3D switchgear degradation models and online condition monitoring systems. These tools aim to prioritise maintenance, extend asset life and optimise investment decisions.

Progress reports show that Singapore's digital twin initiative has delivered tangible benefits. A media release notes that the Digital Asset Twin enables operators to monitor asset condition and performance, allowing prioritized renewal and maintenance. The Digital Network Twin, developed with research partners, uses advanced modelling to assess the impact of additional electric-vehicle demand and suggest infrastructure upgrades. The twin provides insights into how substation capacity can be optimized, helping planners design a grid capable of supporting the city-state's decarbonisation pathways. By offering a risk-free environment to test scenarios, the twin allows operators to explore different strategies for integrating distributed energy resources and managing rising demand.

Australia: unlocking hidden capacity with digital twins

In May 2023, New South Wales distributor Essential Energy announced that it had created an engineering-grade digital twin covering 95 per cent of its network, which spans most of NSW and parts of southern Queensland. The twin uses artificial-intelligence-driven analytics to model how the network will perform under different loads, providing insights that were previously impossible. By accurately modelling how overhead wires heat up and sag as electricity flows, Essential Energy discovered that some lines can carry up to twice the capacity previously assumed. This unlocked capacity allows more renewable energy to connect to the grid without expensive upgrades.

The digital twin also proved invaluable during recovery from the devastating 2019/2020 bushfire season. Instead of simply rebuilding damaged infrastructure, the twin enabled engineers to optimise the new network design, considering ground clearances, resilience and community outcomes. This approach shortened design time and allowed crews to restore power more quickly. Essential Energy's success demonstrates how a well-designed digital twin can transform both day-to-day operations and emergency response.

India: cutting losses and avoiding upgrades in New Delhi

In New Delhi, utility BSES Rajdhani Power partnered with technology firm Panitek to build a digital twin of the city's distribution network. The twin acts like a live map: operators can see which feeders are stressed or overloaded in real time, just as a navigation app shows traffic congestion. This visibility allows crews to address problems before they cause outages, rather than waiting for customer complaints. By mapping consumer demand on each transformer, the digital twin has helped the utility avoid costly infrastructure upgrades and reduce energy losses, saving over US$3,600 per transformer each year. The pilot has also enabled solar panels at a community institute to feed excess energy back into the grid, demonstrating that digital twins can support the integration of distributed renewables.

India's Rajasthan: building digital public infrastructure

The International Solar Alliance (ISA) has selected the Indian state of Rajasthan to pioneer a digital twin of its electricity distribution network. The initiative aims to create a secure and standardised digital public infrastructure for the power sector, aligned with the Ministry of Power's vision for an "India Energy Stack". ISA is collaborating with the local utility JVVNL to develop digital tools for grid modelling, load-flow analysis and AI-based management. Officials expect the project to strengthen grid efficiency and operational planning as the state integrates higher shares of renewable energy. The programme has attracted strong interest from industry - fifteen consortiums responded to ISA's call for proposals - highlighting market confidence in the emerging digital twin ecosystem. The project will be showcased at an AI summit in February 2026 and could become a template for other Indian states.

China's Hainan: digital backbone for intelligent substations

To support the integration of offshore wind and solar on Hainan Island, the regional utility and Huawei have built a 100 gigabit per second optical transport network (OTN). This high-speed digital "artery" enables real-time data exchange for services such as video surveillance, automated dispatch and digital twin substations. The dual-ring architecture provides self-healing protection and ensures continuous communication even during extreme weather. The network supports full-site route selection, enabling bidirectional communication across generation, grid, load and storage. By delivering millisecond-level transmission for renewable plants and allowing real-time coordination across more than 100,000 distributed energy resources and EV charging stations, the digital backbone lays the foundation for digital twins that can accurately model and control the island's evolving power system.

What isn't working: challenges and gaps

Despite early successes, grid digitalization remains uneven. Several challenges persist:

Data quality and standardisation: Digital twins require high-resolution data from sensors, smart meters and legacy systems. In many parts of Asia-Pacific, data is sparse or inconsistent. Utilities may operate multiple SCADA and outage management systems that do not communicate seamlessly, making it difficult to build unified models.
High capital costs: Developing detailed network and asset twins demands significant investment in sensors, communication infrastructure and analytics. The benefits are clear, but the upfront costs can be prohibitive, especially for smaller utilities.
Skills and organisational silos: Building and using digital twins requires expertise in data science, power engineering and software development. Utilities often struggle to attract and retain talent with this combination of skills. Cross-functional collaboration between operations, IT and planning teams is essential but not always easy.
Cybersecurity and privacy: As more devices connect to the grid and data flows grow, cybersecurity risks increase. Digital twins must be designed with robust security measures to protect sensitive operational data.
Scalability: Pilot projects like those in New Delhi and Singapore show promise, but scaling digital twins to cover entire national networks is complex. Integration with existing regulatory frameworks and market structures is still evolving.

Framework for building digital twins

For product and design teams tasked with delivering digital-twin solutions, the following framework offers a structured approach:

Define clear objectives. Start by identifying the problems you want the digital twin to solve. Is the goal to model network capacity for EV adoption? Improve asset maintenance? Reduce losses? Clear objectives help prioritise data collection and modelling efforts.
Establish data foundations. Inventory existing sensors, meters and information systems. Develop a roadmap to fill data gaps, install sensors on critical assets and upgrade communication networks. Consider partnerships with telecom providers to secure high-bandwidth, low-latency connectivity, as seen in Hainan's 100G OTN.
Develop modular models. Build network and asset models incrementally. Start with a single feeder or asset class and validate the model against real-world measurements. Use interoperable simulation platforms that can integrate various data sources (e.g., MESMO in Singapore). Modular design allows models to be expanded over time.
Integrate analytics and AI. Combine physical models with machine-learning algorithms to predict asset failures, optimise dispatch and identify latent capacity. Essential Energy's success shows how AI-driven analytics can reveal hidden headroom on power lines.
Test scenarios in a safe environment. Use the digital twin as a sandbox to explore what-if scenarios, such as extreme weather events or rapid EV uptake. This helps operators plan upgrades and design emergency responses.
Plan for governance and security. Develop protocols for data governance, privacy and cybersecurity. Ensure that roles and responsibilities for maintaining the twin are clearly defined across departments.
Iterate and scale. Treat the digital twin as a living system that evolves with the grid. Regularly update models with new data, incorporate lessons from pilots and expand coverage. Engage regulators and policymakers early to align digital-twin development with planning and market reforms.

What's next: fast-moving trends

Digital public infrastructure: Initiatives like Rajasthan's digital twin are part of a broader push to create standardized, interoperable data platforms for the power sector. This digital public infrastructure could lower barriers for smaller utilities and third-party innovators.
Integration with distributed energy resource management systems (DERMS): Singapore's digital twin is being paired with a DERMS to optimise the deployment of solar, battery storage and EVs. Expect more projects that link twins with real-time control platforms for distributed resources.
Artificial intelligence and automation: AI will increasingly augment digital-twin models, enabling autonomous fault detection, adaptive protection settings and self-healing networks. Essential Energy's twin already uses AI to derive insights; future systems will go further, using reinforcement learning to optimize operations.
Cross-sector integration: Digital twins could extend beyond electricity to model interactions between power, transport, buildings and industry. In Singapore, researchers are integrating grid twins into national decarbonisation models that optimise energy systems over decades.
International collaboration: The ISA's call for proposals and the interest from 15 consortiums highlight that digital-twin ecosystems require cross-border collaboration. Shared standards and open-source tools will be critical for scaling.

Action checklist for product and design teams

Audit your data ecosystem. Map existing data sources (smart meters, SCADA, GIS) and identify gaps.
Partner early. Collaborate with research institutions, technology providers and regulators to co-develop models. Joint labs like the SP Group-NTU partnership can accelerate innovation.
Design for flexibility. Use open standards and modular architectures so the twin can evolve with changing technology and policy.
Prioritise cybersecurity. Implement robust security measures and ensure compliance with data privacy regulations.
Develop talent. Invest in cross-disciplinary teams that blend power system expertise with data science and software development.
Pilot, then scale. Start with a focused pilot (e.g., a single feeder or asset class), refine the model and processes, and expand iteratively.
Communicate value. Quantify benefits - such as avoided upgrade costs, increased capacity or reduced downtime - to secure organisational and regulatory support.

FAQ

What is a digital twin in the context of power grids?

A digital twin is a data-driven virtual replica of a physical grid or component that mirrors its behaviour in real time. Network twins model electrical flows and capacity, while asset twins monitor the condition of individual equipment. Digital twins are continuously updated with sensor data and can be used to simulate scenarios, predict failures and optimise operation.

Why invest in digital twins now?

Grids across Asia-Pacific are experiencing rapid demand growth and increasing variability due to renewables and electric vehicles. Digital twins enable utilities to plan upgrades more accurately, unlock hidden capacity and avoid unnecessary infrastructure spending. For example, Essential Energy found that some power lines could carry twice the capacity previously assumed, while BSES Rajdhani Power saved thousands of dollars per transformer by avoiding needless upgrades.

How do digital twins support renewable integration?

Digital twins allow operators to test how large amounts of solar, wind and battery storage will affect the grid. Singapore's network twin, for instance, simulates the impact of EV charging and distributed solar to identify when upgrades are needed. By modelling the network in detail, utilities can plan to accommodate renewables without compromising reliability.

What is digital public infrastructure for the power sector?

Digital public infrastructure refers to shared platforms and standards that make grid data and models interoperable. Rajasthan's initiative aims to create such an infrastructure, enabling utilities and private companies to build digital twins and other applications without reinventing the data layer. This approach can accelerate innovation and reduce costs.

How does communication technology support digital twins?

Digital twins depend on reliable, high-bandwidth communication to transmit data between sensors and models. Hainan's 100 gigabit per second optical network, with dual-ring self-healing architecture, supports digital twin substations and real-time coordination across generation, grid, load and storage. Without such infrastructure, twins cannot be updated quickly enough to provide actionable insights.