Grid-Edge Phase Identification Analytics Market Report 2026

The grid‑edge phase identification analytics market size has grown rapidly in recent years. It will grow from $1.1 billion in 2025 to $1.28 billion in 2026 at a compound annual growth rate (CAGR) of 16.1%. The growth in the historic period can be attributed to expansion of smart meter deployments, early digitization of distribution networks, growing data availability at grid edge, initial adoption of distribution analytics tools, increasing focus on outage management accuracy.

The grid‑edge phase identification analytics market size is expected to see rapid growth in the next few years. It will grow to $2.34 billion in 2030 at a compound annual growth rate (CAGR) of 16.4%. The growth in the forecast period can be attributed to increasing investments in distribution grid modernization, rising penetration of distributed energy resources, growing demand for automated grid validation, expansion of utility cloud analytics adoption, increasing focus on grid resilience and reliability. Major trends in the forecast period include increasing adoption of machine learning-based phase detection, rising use of smart meter data analytics, growing integration of real-time topology validation tools, expansion of cloud-based grid-edge analytics platforms, enhanced focus on distribution grid accuracy.

The rising penetration of distributed energy resources (DERs) is expected to drive the growth of the grid-edge phase identification analytics market in the coming years. Distributed energy resources refer to small-scale electricity generation and storage systems connected to the power grid at or near the point of use, including rooftop solar installations, battery energy storage systems, and electric vehicle charging infrastructure. The growing penetration of distributed energy resources (DERs) is driven by the increasing shift toward decentralized renewable energy generation at the consumer level. Grid-edge phase identification analytics supports distributed energy resources (DERs) by precisely mapping DER connections to distribution phases, allowing utilities to optimize load distribution, reduce phase imbalances, and ensure dependable integration of distributed generation at the grid edge. For instance, in March 2025, according to the International Renewable Energy Agency, a UAE-based intergovernmental organization, global renewable power capacity additions reached 585 GW in 2024, representing more than 90% of total power capacity expansion, an increase compared to previous years. Therefore, the growing adoption of distributed energy resources is driving the growth of the grid-edge phase identification analytics market.

Key companies operating in the grid-edge phase identification analytics market are focusing on developing innovative solutions, such as AI-enabled grid-edge analytics platforms that integrate advanced real-time phase mapping and operational intelligence, to meet the rising demand for enhanced grid visibility, rapid distributed energy resource (DER) integration, and improved outage and load management driven by grid modernization initiatives and the increasing complexity of distribution networks. AI-based grid-edge phase identification analytics platforms leverage machine learning and artificial intelligence to continuously process high-volume grid data from smart meters, IoT sensors, and other edge devices, automatically identify phase imbalances and connectivity patterns, and enable utilities to optimize load balancing and grid reliability capabilities that traditional phase identification methods, which relied on manual surveys and limited data sampling, could not deliver at scale or in real time. For example, in November 2025, Schneider Electric, a France-based energy management and automation technology company, launched its One Digital Grid Platform, a modular, AI-enabled software platform designed to help utilities modernize grid operations by combining planning, operations, and asset management with real-time analytics and predictive insights to improve outage restoration, resilience, and cost efficiency across distribution networks. The One Digital Grid Platform leverages AI algorithms to integrate diverse grid data streams, estimate restoration times during outages, and enhance decision-making without requiring costly infrastructure overhauls, making it a significant advancement over traditional grid management systems that lacked cohesive, AI-driven operational tools.

In December 2023, Uplight, a US-based provider of energy management and utility software solutions focused on customer engagement, load flexibility, and decarbonization platforms, acquired AutoGrid from Schneider Electric for an undisclosed amount. With this acquisition, Uplight aimed to broaden its capabilities by incorporating AutoGrid’s advanced virtual power plant (VPP) and distributed energy resource management system (DERMS) technologies into a unified platform to better support utilities and energy stakeholders with improved grid flexibility and DER orchestration solutions. AutoGrid is a US-based provider of AI-driven software for managing distributed energy resources (DERs), including VPP, DERMS, and real-time optimization tools supporting renewable energy, electric vehicles, storage, and other grid assets.

Major companies operating in the grid‑edge phase identification analytics market are Siemens AG, Hitachi Energy Ltd., International Business Machines Corporation (IBM), Cisco Systems, Inc., Oracle Corporation, Schneider Electric SE, Honeywell International Inc., ABB Ltd., Capgemini SE, Eaton Corporation plc, Itron, Inc., Landis+Gyr Group AG, Schweitzer Engineering Laboratories, Inc. (SEL), S&C Electric Company, Aclara Technologies LLC (a Hubbell Company), Enel X S.r.l., Kamstrup A/S, C3.ai, Inc., Uplight, Inc., Trilliant Holdings Inc.

Tariffs are impacting the grid-edge phase identification analytics market by increasing costs of imported sensors, metering hardware, communication modules, and data acquisition devices used alongside analytics platforms. Utilities in North America and Europe are most affected due to reliance on imported grid-edge hardware, while Asia-Pacific faces cost pressures on large-scale smart grid rollouts. These tariffs are raising deployment costs and slowing some grid modernization programs. However, they are also encouraging software-centric analytics adoption, domestic hardware sourcing, and greater reliance on cloud-based phase identification solutions that reduce physical infrastructure dependency.

The grid‑edge phase identification analytics market research report is one of a series of new reports that provides grid‑edge phase identification analytics market statistics, including grid‑edge phase identification analytics industry global market size, regional shares, competitors with a grid‑edge phase identification analytics market share, detailed grid‑edge phase identification analytics market segments, market trends and opportunities, and any further data you may need to thrive in the grid‑edge phase identification analytics industry. This grid‑edge phase identification analytics market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Grid-edge phase identification analytics is a data-driven software tool that determines the accurate phase connectivity of customers and devices at the distribution grid edge. It examines voltage, current, and time-series data from smart meters, sensors, and distributed energy resources (DERs) to identify phase errors and mismatches. It enhances load balancing, outage management, and DER integration by ensuring correct phase identification throughout the grid.

The main components of grid-edge phase identification analytics include software, hardware, and services. Software encompasses analytics solutions that collect, process, and interpret grid-edge data to identify phase connections, optimize performance, and support decision-making. These solutions are deployed through on-premises and cloud modes. They are applied across grid optimization, outage management, asset management, load forecasting, and other applications, and are distributed via direct sales, distributors, and online channels. The solutions serve multiple end-users, including utilities, industrial, commercial, residential, and other stakeholders.

The grid-edge phase identification analytics market consists of revenues earned by entities by providing services such as grid-edge data collection and processing, advanced analytics and machine learning-based phase detection, real-time and periodic network topology validation, data visualization and reporting, and utility workflow automation support. The market value includes the value of related goods sold by the service provider or included within the service offering. The grid-edge phase identification analytics market includes sales of machine learning-based phase detection tools, data processing and visualization modules, application programming interfaces (APIs), cloud-based analytics products and subscriptions, and associated digital platforms. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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