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Market Research Report

AI for Predictive T&D Network Management: Advanced Analytics for Outage Mitigation, DER Integration, Workforce Management, and Predictive Asset Management

Published by Guidehouse Insights (formerly Navigant Research) Product code 954111
Published Content info 43 Pages; 19 Tables, Charts & Figures
Delivery time: 1-2 business days
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AI for Predictive T&D Network Management: Advanced Analytics for Outage Mitigation, DER Integration, Workforce Management, and Predictive Asset Management
Published: August 12, 2020 Content info: 43 Pages; 19 Tables, Charts & Figures
Description

AI technology has profound implications for the operation of electric transmission and distribution (T&D) networks. In many applications, the superior insights derived from machine and deep learning solutions can reduce costs, improve reliability and service quality, and enhance efficiency throughout the grid. Traditional decision-making associated with grid management is not expected to remain adequate in the near future. Utilities will likely become increasingly dependent on AI-based solutions to incorporate distributed energy resources (DER) at an accelerating pace while maintaining acceptable performance metrics and keeping costs low. AI technologies for T&D management can help utilities minimize outages, make mobile workers more effective, improve load planning, manage real-time power quality, perform predictive asset maintenance, and more.

AI analytics solutions may be developed as a module for traditional operational technology (OT) systems such as advanced distribution management systems (ADMSs) or energy management systems (EMSs), or they may come from analytics solutions providers with an emphasis on utility operations. These vendors specialize in analyzing data files from smart meters or sensors connected to network assets and providing insights. Alternatively, some utilities may rely on general purpose business intelligence platforms that include AI capabilities. These platforms may be implemented in collaboration with partners or use internally developed tools as do-it-yourself (DIY) platforms.

This report describes the current landscape for AI technology solutions for T&D network management and presents drivers and barriers to implementation. It details AI-supported applications and explores the global market forecasts for these solutions by region and application.

KEY QUESTIONS ADDRESSED:

  • In what applications can AI technology enhance the operation of a T&D network?
  • What are the options for acquiring AI-based technology for T&D networks?
  • What are the benefits of implementing AI-based technology?
  • What OT systems are involved in leveraging AI-based technology?
  • What challenges are associated with implementing these applications?

WHO NEEDS THIS REPORT:

  • OT providers
  • Service providers
  • Providers of general purpose analytics platforms
  • Utilities
  • Equipment vendors
  • Systems integrators
  • Utility regulators
  • Investor community
Table of Contents
Product Code: MF-AINM-20

Table of Contents

1. Executive Summary

  • 1.1. Introduction
  • 1.2. Market Forecast

2. Market Issues

  • 2.1. Introduction
  • 2.2. AI-Based Applications for T&D Network Management
    • 2.2.1. Power Quality Management
    • 2.2.2. Conservation Voltage Reduction
    • 2.2.3. DR
    • 2.2.4. DER Performance Management
    • 2.2.5. State Estimation/Switch Order Management
    • 2.2.6. Drone Video Monitoring
    • 2.2.7. Weather Analysis
    • 2.2.8. Predictive Asset Outages
    • 2.2.9. Non-Asset Failure Predictions
    • 2.2.10. OMSs
    • 2.2.11. Work Scheduling
    • 2.2.12. Vehicle Crash Response
    • 2.2.13. Theft of Service
    • 2.2.14. Grid Optimization and Network Planning
    • 2.2.15. Skills Planning
  • 2.3. Deployment Models for AI-Based Solutions
    • 2.3.1. OT Vendor Solutions
    • 2.3.2. Utility-Focused Data Analytics Providers
    • 2.3.3. General Purpose Analytics Tools
  • 2.4. Market Drivers
    • 2.4.1. Outage Mitigation and Restoration
    • 2.4.2. Workforce Planning
    • 2.4.3. Load Planning and DER Integration
  • 2.5. Market Barriers
    • 2.5.1. Data Collection, Poor Data Quality, and Multiple Source Datasets
    • 2.5.2. Cost
    • 2.5.3. Complexity
    • 2.5.4. Employee Impact
    • 2.5.5. Non-Traditional Procurement Models
  • 2.6. Market Trends
    • 2.6.1. Burgeoning DER
    • 2.6.2. Increased Penetration of ADMS and DER Management Systems
    • 2.6.3. New Market Entrants and Mergers
    • 2.6.4. Utilities Creating R-Based Solutions and Using Data Lakes
  • 2.7. Supporting Technologies
    • 2.7.1. AMSs/APM Systems
    • 2.7.2. GIS Solutions
    • 2.7.3. Load Forecasting Solutions
    • 2.7.4. C&I DR/DERMSs
    • 2.7.5. Grid Optimization Systems
    • 2.7.6. Weather Forecast Systems
    • 2.7.7. Traffic Forecast Systems

3. Key Industry Players

  • 3.1. Hitachi ABB Power Grids
  • 3.2. C3.ai
  • 3.3. Clevest Solutions
  • 3.4. General Electric
  • 3.5. Grid4C
  • 3.6. Open Systems International
  • 3.7. OSIsoft
  • 3.8. SAS Institute
  • 3.9. Schneider Electric
  • 3.10. Siemens

4. Market Forecasts

  • 4.1. Introduction
  • 4.2. Global Forecasts
    • 4.2.1. Regional Forecasts
      • 4.2.1.1. North America
      • 4.2.1.2. Europe
      • 4.2.1.3. Asia Pacific
      • 4.2.1.4. Latin America
      • 4.2.1.5. Middle East & Africa
    • 4.2.2. Forecast by Procurement Source
    • 4.2.3. Forecast by Functional Category
  • 4.3. Conclusions and Recommendations

5. Acronym and Abbreviation List

6. Table of Contents

7. Table of Charts and Figures

8. Scope of Study, Sources and Methodology, Notes

LIST OF CHARTS AND FIGURES

  • AI-Based Applications and Services Revenue by Region, World Markets: 2020-2029
  • AI-Based Applications and Services Revenue by Procurement Source, World Markets: 2020-2029
  • AI-Based Applications and Services Revenue by Functional Category, World Markets: 2020-2029

LIST OF TABLES

  • AI-Based Applications and Services Revenue by Region, World Markets: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, World Markets: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, North America: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, Europe: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, Asia Pacific: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, Latin America: 2020-2029
  • AI-Based Applications Revenue by Procurement Source, Middle East & Africa: 2020-2029
  • AI-Based Applications Revenue by Functional Category, World Markets: 2020-2029
  • AI-Based Applications Revenue by Functional Category, North America: 2020-2029
  • AI-Based Applications Revenue by Functional Category, Europe: 2020-2029
  • AI-Based Applications Revenue by Functional Category, Asia Pacific: 2020-2029
  • AI-Based Applications Revenue by Functional Category, Latin America: 2020-2029
  • AI-Based Applications Revenue by Functional Category, Middle East & Africa: 2020-2029
  • Overlap of AI-Based Applications for T&D Networks
  • Benefits Associated with AI Applications
  • Dataset Requirements for AI-Based T&D Management Applications
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