Dublin, Nov. 13, 2026 (GLOBE NEWSWIRE) -- "Grid-Edge Phase Identification Analytics Market Report 2026" has been added to ResearchAndMarkets.com's offering.
The global grid-edge phase identification analytics market is experiencing rapid expansion as utilities invest in smarter, more resilient distribution networks. The market is projected to grow from $1.1 billion in 2025 to $1.28 billion in 2026, representing a compound annual growth rate (CAGR) of 16.1%. Historical growth has been supported by expanding smart meter deployments, the digitization of distribution networks, greater availability of grid-edge data, early adoption of distribution analytics tools, and increased demand for more accurate outage management.
Growth is expected to remain strong through the end of the decade. The grid-edge phase identification analytics market is forecast to reach $2.34 billion by 2030, expanding at a CAGR of 16.4%. Key growth factors include rising investments in distribution grid modernization, increasing penetration of distributed energy resources, wider adoption of automated grid validation, growing use of cloud-based utility analytics, and a stronger focus on grid resilience and reliability.
Major grid-edge analytics market trends include the adoption of machine learning-based phase detection, increased use of smart meter data analytics, integration of real-time topology validation tools, and expansion of cloud-based grid-edge analytics platforms. Utilities are also prioritizing distribution grid accuracy as electricity networks become more decentralized and complex.
The growing adoption of distributed energy resources is a significant driver of the grid-edge phase identification analytics market. Rooftop solar systems, battery energy storage, electric vehicle charging infrastructure, and other distributed assets are creating new operational requirements for utilities. Accurate phase mapping helps utilities manage these connections, optimize load distribution, reduce phase imbalances, and integrate distributed generation without compromising network reliability.
Renewable energy capacity growth reinforces this outlook. According to the International Renewable Energy Agency, global renewable power capacity additions reached 585 GW in 2024 and accounted for more than 90% of total power capacity expansion. As distributed renewable generation continues to increase, utilities are expected to require more advanced grid-edge phase identification and distribution network analytics capabilities.
Innovation in artificial intelligence is also reshaping the competitive landscape. Technology providers are developing AI-enabled grid-edge analytics platforms that combine real-time phase mapping, predictive insights, and operational intelligence. These solutions process data from smart meters, connected sensors, and other grid devices to identify connectivity patterns and phase imbalances. The resulting insights can support load balancing, outage restoration, asset management, and distribution grid reliability.
In November 2025, Schneider Electric launched its One Digital Grid Platform, a modular AI-enabled software platform designed to combine utility planning, operations, and asset management. The platform uses real-time analytics and predictive capabilities to improve outage restoration, resilience, operational decision-making, and cost efficiency. Its ability to integrate multiple grid data streams reflects the market's shift toward unified, software-driven grid management.
Strategic acquisitions are further strengthening distributed energy management capabilities. In December 2023, Uplight acquired AutoGrid from Schneider Electric for an undisclosed amount. The transaction expanded Uplight's capabilities in virtual power plant technology, distributed energy resource management, grid flexibility, and real-time asset optimization. The combined platform is intended to help utilities coordinate renewable energy, energy storage, electric vehicles, and other distributed grid assets more effectively.
The market spans software, hardware, and services, with deployment options including on-premises and cloud-based solutions. Key applications include grid optimization, outage management, asset management, load forecasting, network topology validation, data visualization, reporting, and utility workflow automation. Demand comes from utilities as well as industrial, commercial, and residential energy stakeholders. Cloud deployment is expected to gain momentum as organizations seek scalable analytics, faster implementation, and reduced dependence on extensive physical infrastructure.
Tariffs are influencing the grid-edge phase identification analytics market by increasing the cost of imported sensors, metering hardware, communication modules, and data acquisition equipment. Utilities in North America and Europe face particular exposure because of their reliance on imported grid-edge hardware, while Asia-Pacific is encountering cost pressures related to large-scale smart grid deployments. Although higher costs may delay some modernization programs, the situation is encouraging domestic hardware sourcing, software-centric analytics adoption, and increased use of cloud-based phase identification solutions.
Markets Covered:
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
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Key Attributes:
| Report Attribute | Details |
| No. of Pages | 250 |
| Forecast Period | 2026 - 2030 |
| Estimated Market Value (USD) in 2026 | $1.28 Billion |
| Forecasted Market Value (USD) by 2030 | $2.34 Billion |
| Compound Annual Growth Rate | 16.4% |
| Regions Covered | Global |
Companies Featured
The companies featured in this Grid-Edge Phase Identification Analytics market report include:
- 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.
For more information about this report visit https://www.researchandmarkets.com/r/zhclhe
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