Grid Modernisation and Smart-Grid Deployment Forecast (2025 - 2032)
As the global energy ecosystem undergoes a profound transformation, grid modernisation and smart-grid deployment have emerged as strategic priority for governments, utilities, and private-sector stakeholders alike.
Driven by the convergence of decarbonisation imperatives, electrification trends, and the proliferation of distributed energy resources, the modern power grid must evolve from a unidirectional, centralised structure into a dynamic, intelligent, and decentralised network.
This market research study, covering the period from 2025 to 2032, examines the trajectory of smart grid development and the associated technologies that underpin grid modernisation across major global regions.
The study, which is available exclusively to Premium members, provides a comprehensive analysis of investment trends, technological penetration forecasts, policy catalysts, and the evolving competitive landscape. Special attention is paid to advanced metering infrastructure, distribution automation, vehicle-to-grid integration, grid-edge software platforms, and DER coordination mechanisms.
By synthesising quantitative forecasts with qualitative insights, this study aims to provide decision-makers with actionable intelligence to navigate the future of grid infrastructure and capitalise on emerging opportunities.
Market Definition and Segmentation
Understanding the structure and boundaries of the grid modernisation and smart grid market is critical to contextualising the technological, regulatory, and economic trends shaping its evolution.
This section defines the scope of the market, detailing key components of grid modernisation, and delineates the core segments driving transformation. It explores how the smart grid ecosystem is organised, spanning infrastructure, digital intelligence, and decentralised energy integration, and sets the foundation for granular analysis of investment areas such as Advanced Metering Infrastructure, Distribution Automation, Vehicle-to-Grid integration, grid-edge software, and Distributed Energy Resources.
By segmenting the market along functional and operational lines, the study provides a framework for forecasting growth patterns, mapping value chains, and identifying key actors across the global smart grid landscape.
Definition of Grid Modernisation
Grid modernisation refers to the strategic upgrading of electric transmission and distribution networks to enhance reliability, flexibility, resilience, and efficiency. It encompasses both physical infrastructure improvements, such as replacing ageing substations and transmission lines, and the digital transformation of grid operations through advanced sensing, communication, and control technologies.
At its core, grid modernisation seeks to accommodate a two-way flow of electricity and data, support variable renewable energy sources, and enable greater participation by consumers and prosumers.
The modernised grid is expected to facilitate real-time monitoring, decentralised control, predictive maintenance, and interoperability between centralised utilities and distributed assets.
Scope of Smart Grid Technologies
Smart grid technologies are the cornerstone of grid modernisation. They enable the digitalisation of electricity networks through a combination of hardware, software, and communication systems. These technologies aim to:
The scope of smart grid technologies includes, but is not limited to:
These technologies are increasingly being deployed in tandem, creating interoperable ecosystems that span generation, transmission, distribution, and consumption layers of the electricity value chain.
Segment Focus: AMI, DA, V2G, grid-edge software, DER integration
The modernisation of electric grids is no longer a monolithic infrastructure challenge, it is a multifaceted transformation that hinges on the integration of digital technologies, distributed energy resources, and interactive consumer participation.
Within this dynamic ecosystem, five core segments have emerged as foundational pillars of smart grid development: ; ; ; (4) Grid-Edge Software; and (5) Distributed Energy Resources Integration. Each represents a distinct functional domain but is increasingly interdependent in delivering the outcomes of a resilient, efficient, and intelligent power system.
Advanced Metering Infrastructure (AMI)
AMI is the digital backbone of modern utility-customer interactions. It encompasses smart meters, communications networks, and data management systems that collectively enable real-time measurement, remote monitoring, and dynamic pricing models.
Distribution Automation
DA refers to the deployment of sensors, intelligent control devices, and automated switching systems across medium- and low-voltage distribution networks. It facilitates real-time monitoring, remote fault isolation, and load balancing.
Vehicle-to-Grid Integration
V2G represents the interface between electric vehicles and the power grid, enabling bidirectional power flows. Through smart chargers and energy management systems, EVs can supply stored energy back to the grid during peak demand or participate in ancillary service markets.
Grid-Edge Software
Grid-edge software refers to the suite of applications, platforms, and analytics tools deployed at the interface between the grid and end-users or edge devices. This includes distributed energy management systems, home energy management systems, and AI-powered optimisation platforms.
Distributed Energy Resources (DER) Integration
DER integration involves the seamless incorporation of small-scale, decentralised energy sources, such as rooftop solar, battery storage, small wind turbines, and demand-side management assets, into the grid.
Interdependencies and Strategic Importance
While each of these segments contributes unique functionalities to the smart grid, their convergence is what enables the full spectrum of benefits envisioned under grid modernisation. AMI supplies the data for grid-edge software to interpret; DA ensures physical responsiveness; V2G provides mobile flexibility; and DER integration transforms consumers into active grid participants.
Together, they form a decentralised, data-rich, and dynamically managed grid ecosystem, capable of supporting the energy transition well into the post-2032 era.
Methodology
Research Approach and Data Sources
This study was developed using a hybrid research methodology, combining primary data collection with secondary data analysis and proprietary forecasting models.
Data triangulation was used to validate key findings and ensure consistency across diverse information sources.
Forecast Methodology
The forecasting approach employs both top-down and bottom-up techniques:
Forecasts are presented in both unit terms (for example, million meters, automated switches, V2G-enabled vehicles) and value terms (for example, USD billions in cumulative investment).
Limitations and Assumptions
While care has been taken to ensure accuracy, the following limitations and assumptions apply:
Where data was unavailable or conflicting, conservative estimates and cross-validation methods were applied to minimise forecast bias.
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