Grid-Edge Phase Identification Analytics Market Report 2026

Grid-Edge Phase Identification Analytics Market Overview

• Grid-Edge Phase Identification Analytics market size has reached to $1.1 billion in 2025 • Expected to grow to $2.34 billion in 2030 at a compound annual growth rate (CAGR) of 16.4% • Growth Driver: Increasing Penetration of Distributed Energy Resources (DERs) Driving Grid-Edge Phase Identification Analytics Growth • Market Trend: Technological Advancements in AI-Driven Grid Edge Analytics for Automated Phase Mapping and Enhanced Grid Visibility • North America was the largest region in 2025 and Asia-Pacific is the fastest growing region.

What Is Covered Under Grid-Edge Phase Identification Analytics Market?

Grid-edge phase identification analytics is a data-driven software solution that determines the correct phase connectivity of customers and devices at the distribution grid edge. It analyzes voltage, current, and time-series data from smart meters, sensors, and distributed energy resources (DERs) to identify phase mismatches and errors. It is used to enhance load balancing, outage management, and distributed energy resource integration by ensuring correct phase identification across the grid. The main components of grid-edge phase identification analytics are software, hardware, and services. Software refers to 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 deployment modes. They are used across various applications, including grid optimization, outage management, asset management, load forecasting, and other applications, and are distributed via direct sales, distributors, and online sales channels. The solutions cater to multiple end-users, including utilities, industrial, commercial, residential, and other end-users.

What Is The Grid-Edge Phase Identification Analytics Market Growth Forecast?

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.

Global Grid-Edge Phase Identification Analytics Market Segmentation

1) By Component: Software; Hardware; Services 2) By Deployment Mode: On-Premises; Cloud 3) By Application: Grid Optimization; Outage Management; Asset Management; Load Forecasting; Other Applications 4) By Sales Channel: Direct Sales; Distributors; Online Sales 5) By End-User: Utilities; Industrial; Commercial; Residential; Other End Users Subsegments: 1) By Software: Phase Identification Software; Data Analytics Software; Visualization Software; Integration Software; Reporting Software 2) By Hardware: Sensor Modules; Metering Devices; Communication Interfaces; Data Acquisition Units; Signal Processing Units 3) By Services: Consulting Services; Deployment Services; Maintenance Services; Training Services; Technical Support Services

What Is The Driver Of The Grid-Edge Phase Identification Analytics Market?

The increasing penetration of distributed energy resources (DERs) is expected to propel the growth of the grid-edge phase identification analytics market going forward. Distributed energy resources refer to small-scale power generation and storage units connected to the electricity grid at or near the point of consumption, including rooftop solar panels, battery energy storage systems, and electric vehicle charging infrastructure. The increasing penetration of distributed energy resources (DERs) is due to the growing shift toward decentralized renewable energy generation at the consumer level. Grid-edge phase identification analytics supports distributed energy resources (DERs) by accurately mapping DER connections to distribution phases, enabling utilities to optimize load balancing, prevent phase imbalances, and ensure reliable integration of distributed generation at the edge of the grid. For instance, in March 2025, according to the International Renewable Energy Agency, a UAE-based intergovernmental organization, global renewable power capacity additions rose to 585 GW in 2024, accounting for over 90% of total power expansion, up from previous years. Therefore, the rising adoption of distributed energy resources is driving the growth of the grid-edge phase identification analytics market.

Key Players In The Global Grid‑Edge Phase Identification Analytics Market

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.

Global Grid‑Edge Phase Identification Analytics Market Trends and Insights

Major 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 instance, 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.

What Are Latest Mergers And Acquisitions In The Grid‑Edge Phase Identification Analytics Market?

In December 2023, Uplight, a United States–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 expand its capabilities by integrating AutoGrid’s advanced virtual power plant (VPP) and distributed energy resource management system (DERMS) technology into a unified platform to better serve utilities and energy players with enhanced 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 that support renewable energy, EVs, storage, and other grid assets.

Regional Insights

North America was the largest region in the grid-edge phase identification analytics market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the grid‑edge phase identification analytics market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the grid‑edge phase identification analytics market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

What Defines the Grid‑Edge Phase Identification Analytics Market?

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.

How is Market Value Defined and Measured?

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.

What Key Data and Analysis Are Included in the Grid‑Edge Phase Identification Analytics Market Report 2026?

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