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
|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||
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.