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Artificial Intelligence (AI) is rapidly transforming electric utility engineering, transmission and distribution operations, infrastructure reliability management, storm restoration coordination, and grid modernization activities across Florida and throughout the broader utility industry. Modern electric utilities operate within increasingly complex environments shaped by renewable energy integration, distributed generation growth, electric vehicle adoption, advanced digital infrastructure, cybersecurity threats, severe weather exposure, aging utility assets, and rising customer expectations regarding reliability and resilience. As electric grids become more decentralized, interconnected, and data-intensive, AI technologies are becoming essential operational tools for maintaining safe, reliable, and efficient utility operations.
Florida utilities face particularly demanding operating conditions involving hurricanes, coastal corrosion, flooding exposure, extreme heat, lightning activity, rapid population growth, vegetation-related outage risk, and highly weather-sensitive electrical demand behavior. These conditions create major engineering and operational challenges involving infrastructure hardening, outage prediction, restoration coordination, voltage stability, transmission congestion management, distributed energy integration, and long-term asset reliability. AI-assisted utility systems provide electric utilities with advanced analytical capabilities capable of processing enormous volumes of operational data while supporting faster, more adaptive, and more informed engineering decision-making.
Modern utility systems generate continuous streams of operational data through supervisory control and data acquisition systems, advanced metering infrastructure, phasor measurement units, intelligent electronic devices, digital substations, smart relays, distributed sensors, weather monitoring systems, drone inspections, thermal imaging systems, outage management platforms, and distributed energy resource communication networks. Artificial intelligence and machine learning technologies allow utilities to convert this operational data into actionable engineering intelligence capable of supporting predictive maintenance, load forecasting, renewable energy coordination, transmission optimization, outage restoration, cybersecurity defense, resilience planning, and infrastructure management.
This course examines the engineering applications of artificial intelligence within modern electric utility systems and Florida power infrastructure environments. Topics include AI-assisted load forecasting, predictive maintenance strategies for transmission and distribution assets, grid optimization, volt/VAR management, renewable energy integration, battery storage coordination, outage prediction, storm restoration operations, distributed energy resource management, cybersecurity protection, AI governance, and cyber-physical risk management. The course also evaluates the operational limitations, engineering responsibilities, governance considerations, and cybersecurity challenges associated with integrating AI technologies into critical utility infrastructure systems.
Extensive emphasis is placed on practical utility engineering applications involving transmission systems, substations, transformers, distribution automation, digital substations, outage management systems, synchrophasor analytics, distributed energy coordination, and resilience planning under severe weather conditions common throughout Florida utility service territories. The course further explores how AI systems support infrastructure hardening initiatives, restoration prioritization, emergency operational coordination, predictive asset management, and operational forecasting during rapidly changing grid conditions.
The course incorporates detailed engineering analysis and real-world operational scenarios involving:
· AI-assisted transformer failure prevention
· Predictive maintenance for utility infrastructure
· Renewable energy integration challenges
· Battery storage coordination
· Transmission and distribution optimization
· Hurricane restoration operations
· Distributed energy management
· Cybersecurity threats affecting utility operational technology systems
· AI governance and engineering accountability
· Grid resilience and infrastructure modernization
Three comprehensive case studies examine practical implementation of AI technologies within utility operating environments. These case studies evaluate predictive maintenance deployment for aging transmission transformers, AI-assisted hurricane restoration
coordination across Florida utility infrastructure, and cyber-physical risk management associated with AI-enabled renewable energy integration and distributed grid optimization systems. Each case study emphasizes engineering oversight, operational resilience, cybersecurity governance, and infrastructure reliability under real-world utility operating conditions.
Applicable industry standards, frameworks, and regulatory references are integrated throughout the course, including:
· North American Electric Reliability Corporation Reliability Standards
· NERC Critical Infrastructure Protection cybersecurity requirements
· IEEE power system and substation standards
· NFPA 70 and NFPA 70E considerations
· Federal Energy Regulatory Commission oversight requirements
· Department of Energy grid modernization initiatives
· Federal Emergency Management Agency resilience guidance
· Florida utility storm hardening initiatives
· Utility asset management and reliability best practices
The course emphasizes engineering judgment, operational accountability, infrastructure resilience, cybersecurity protection, and responsible integration of AI technologies into critical electric utility environments. Participants will develop a deeper understanding of how artificial intelligence is reshaping utility engineering and grid operations while preserving the core reliability, safety, and public service responsibilities of the electric utility industry.
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Learning Objectives:
Upon completion of this course, participants will be able to:
1. Evaluate the operational role of artificial intelligence and machine learning technologies within modern electric utility systems, transmission operations, distribution infrastructure, and Florida utility operating environments.
2. Analyze how AI-assisted load forecasting systems improve generation scheduling, transmission planning, reserve management, renewable energy coordination, and demand prediction under dynamic grid conditions.
3. Apply predictive maintenance concepts and AI-driven asset management strategies to transformers, substations, transmission infrastructure, breakers, underground systems, and other critical utility assets.
4. Assess AI applications used for transmission and distribution optimization, including volt/VAR management, feeder balancing, congestion mitigation, distributed energy coordination, and grid stability improvement.
5. Evaluate how artificial intelligence supports renewable energy integration, battery energy storage coordination, electric vehicle charging management, and distributed energy resource optimization within modern utility systems.
6. Analyze the use of AI-assisted systems for outage prediction, storm preparation, emergency restoration coordination, infrastructure resilience planning, and hurricane response operations affecting Florida utilities.
7. Identify cybersecurity vulnerabilities and cyber-physical risks associated with AI-enabled operational technology environments, digital substations, distributed energy communication systems, and interconnected utility infrastructure.
8. Evaluate the application of NERC Reliability Standards, NERC CIP cybersecurity requirements, IEEE standards, FEMA resilience guidance, and other regulatory frameworks affecting AI deployment within electric utility systems.
9. Assess AI governance principles involving operational accountability, engineering oversight, model validation, cybersecurity protection, data governance, and infrastructure reliability management.
10. Apply engineering judgment to AI-assisted utility operational scenarios involving predictive maintenance, transmission optimization, distributed energy integration, storm restoration, and cyber-physical risk management within critical utility infrastructure environments.
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