Artificial Intelligence (AI) is emerging as a transformative for electric utilities across generation and transmission and distribution (T&D). Within the T&D space, AI’s ability to analyze vast amounts of data, make predictions, and automate complex tasks gives it immense potential to enhance efficiency, reliability, and profitability. Understanding the practical applications of AI is crucial for strategic decision-making and risk reduction. As you review the use cases below and determine the applicability for your organization, an important consideration is to determine whether AI solutions are developed in-house or licensed from external vendors (buy vs. build).
AI Use Cases in Transmission and Distribution
We have identified a wide range of use cases for AI across various T&D activities and functions. These use cases are real-world examples that demonstrate AI’s evolving capabilities.
Generative AI
AI that learns from existing data to generate insights and improve decision-making.
Specific Examples
Analyze historical outage data to generate customized reports, identifying recurring issues, patterns, and seasonal trends
Rapidly process and respond to customer inquiries and identify content gaps by analyzing knowledgebases, past customer interactions, and FAQs
Create personalized customer notifications for outages, maintenance schedules, and billing changes and educate on energy use
Optimize work order planning by analyzing past projects, resource utilization, and field conditions for efficient scheduling
Predictive Maintenance and System Monitoring
AI tools for predicting maintenance needs and monitoring systems.
Specific Examples
Analyze real-time sensor data from transformers, circuit breakers, and power lines to detect anomalies and preempt potential failures
Integrate maintenance records, operational data, failure logs, etc. to build equipment profiles and forecast specific maintenance needs
Optimize maintenance schedules, considering peak loads, critical service periods, and crew availability
Generate adaptive work instructions, updating in real time, based on field conditions and resource availability
Asset Lifecycle Management
AI for optimizing asset performance and extending asset life.
Specific Examples
Model consumption, weather, economic indicators, and demographic shifts to forecast capacity needs and optimize investments
Create efficient work crew schedules, considering weather, team availability, equipment lead times, and historical project timelines
Detect early indications of equipment failure by comparing real performance data against expected metrics
Recommend cost-effective disposal methods by analyzing environmental impact and regulatory compliance
Grid Management and Optimization
AI for managing grid reliability, emergency response, and detection and response to outages.
Specific Examples
Implement AI-driven load balancing systems to predict demand and adjust generation and distribution
Use machine learning algorithms to optimize power flows, adjusting voltage levels to minimize transmission losses
Simulate restoration scenarios based on grid conditions and historical data for efficient power restoration
Monitor vegetation near power lines, using drone imaging to detect and manage potential hazards
Efficiently generate optimized engineering designs for T&D grid projects
Enhanced Outage Management
AI-drive systems for faster detection and response to power outages.
Specific Examples
Pinpoint outage locations through AI-driven analysis of data from smart meters and sensors
Predict restoration times based on outage type, location, weather, and available resources
Pinpoint fault locations during outages, using data from smart meters and sensors
Optimally allocate repair crews and equipment based on outage severity, location, and estimated restoration times
Demand Response and Load Forecasting
AI for predicting energy demand and managing demand response programs.
Specific Examples
Forecast energy consumption patterns, integrating weather data, historical usage, and socioeconomic factors
Optimize demand response, adjusting incentives and communications based on predicted grid stress
Identify high–demand periods and suggest load-shedding or customer engagement strategies
Field Safety
AI for enhancing safety for T&D maintenance crews.
Specific Examples
Perform predictive risk assessments, using past operating experience and incident data, real-time weather data, equipment status, and worker experience
Develop individualized employee training and assign tasks in line with worker expertise, qualifications, competencies, and incident history
Incorporate safety factors into scheduling, such as worker fatigue, weather conditions, and task complexity, to reduce accident risks
Utilize chatbots to provide immediate answers to safety questions, helping field workers make informed decisions in real time
OT Cybersecurity
AI-driven measures to protect T&D infrastructure from cyber threats.
Specific Examples
Analyze SCADA logs and network traffic to distinguish cyber threats from false positives
Detect security breaches by analyzing communication patterns and behavioral anomalies (e.g., multiple device failures for equipment that is not at end of life)
Analyze security alerts across control systems, differentiating between incidents and benign events
Practical Implementation Hurdles
Implementing AI in T&D is not without its challenges. These include:
- Data Management: Companies may struggle with handling large amounts of data, especially in real time. Poor data quality can lead to inaccurate predictions.
- Legal and IP Concerns: There are legal issues around storing sensitive data and defining ownership of AI-generated IP, especially when third-party providers are involved.
- Complexity and Time Investment: The process of loading massive amounts of documents and training AI models can be complex and time-consuming. This can pose a significant challenge, especially for organizations that are new to AI and machine learning (ML) technologies.
- Talent and Skill Shortages: Implementing and maintaining AI/ML solutions may require specialized skills and expertise, including data scientists, ML engineers, and domain experts not currently found within your generation organization. T&D groups may face challenges in recruiting and retaining the necessary talent, especially in highly competitive markets. There is also a shortage of experienced technical employees or engineers who provide quality assurance/control and challenge detailed AI-generated calculations and reports that junior engineers are unfit to review.
- Cybersecurity and Data Privacy: The integration of AI/ML systems with operational technology and the increased reliance on data can expose organizations to new cybersecurity threats and data privacy risks.
- Confidence in AI: Gaining confidence in AI’s ability to produce reliable results can be a barrier. Understanding the AI application’s design can help overcome this.
A well-structured pilot program can address many of the hurdles associated with implementing AI. Pilots allow you to start small to test feasibility and performance, learn from the outcomes, and determine which initiatives to scale up to best support your organization. Key strategies for launching a successful AI pilot include selecting high-value use cases, assembling skilled and diverse teams, engaging stakeholders early, and ensuring proper data management.
How ScottMadden Can Help
AI is transforming T&D operations by enhancing efficiency, reliability, and safety.
ScottMadden offers comprehensive support to utilities looking to harness the power of AI in their T&D operations. Here is how we can assist:
- Use Case Identification/Prioritization: We help you identify and prioritize the most impactful AI use cases tailored to your specific needs.
- Business Case Analysis: Our team conducts thorough business case analyses to determine the potential impacts of AI and modernization efforts.
- Strategy/Roadmap Development: We develop strategic roadmaps to guide your AI and analytics initiatives, ensuring alignment with your organizational goals.
- AI/Analytics Program Development: We assist in the development of robust AI and analytics programs to drive innovation and efficiency.
- Piloting Use Cases: We support the piloting of AI use cases to validate their effectiveness and scalability.
- Enabling Scale: Our expertise helps you scale AI and innovation programs to support full implementation across your organization.
- Embedding AI Capabilities: Through change enablement, we embed AI capabilities within your organization, ensuring sustainable adoption and long-term success.
By partnering with ScottMadden, you can leverage our extensive experience and proven methodologies to transform your T&D operations with AI. Contact us today to start your journey toward a smarter, more efficient grid.
Alex Tylecote and Madison Prinzbach also contributed to this article.
