5 Artificial Intelligence Grand Challenges for the Electric Power Industry

Overview

The Artificial Intelligence (AI) and electric power industries are two large, impactful industry sectors that can both benefit greatly from interactions and collaborations but have not traditionally interacted significantly – both due to a lack of sufficient industry data as well as the electric power industry’s need for reliability, stability, and safety to deploy AI on physical assets.  Both industries have progressed rapidly in recent years with AI techniques rapidly being benchmarked and deployed in other industries as well as digital transformation efforts in the electric power industry underway that are collecting vast tomes of data.  EPRI is helping to accelerate these technologies by issuing 5 Grand Challenges to the AI and Electric Power industries to facilitate collaborations to benchmark, enhance, and accelerate adoption of these technologies across the electric power industry.

For many years, individual organizations have developed and implemented artificial intelligence (AI) and machine learning (ML) technologies for specific use case applications.  These models typically require significant amounts of data for training and evaluation purposes as well as significant computing resources and solve individual problems.  Computing costs have continued to drop while digital transformation activities at utilities have drastically increased the amount of available data, creating an optimal point in time to accelerate efforts related to AI adoption across the globe.

EPRI and Stanford University co-hosted a series of meetings in 2021, bringing together >100 different utilities, universities, and AI organizations to identify opportunities to apply AI/ML techniques to address industry issues, improve business operations, enhance overall safety and efficiency, etc.  These events involved an Executive Panel discussion, a training session and expert panel on AI, and culminated in a Reverse Pitch event where utilities and AI organizations met to share challenges and potential solutions.  Through these events, common themes were identified and collected into a set of grand challenges and key enablers for the AI and electric power industries.

AI Grand Challenges for the Electric Power Industry

1. Grid-Interactive Smart Communities – The energy system of the future will connect owners and operators of buildings, homes, and power grids sharing the benefits from the advances in AI that could improve building-to-grid-operator communication, optimize cost, and improve energy utilization and energy equity for producers and consumers.  This grand challenge seeks to develop, benchmark, and scale the adoption of AI technologies that support networks of homes and buildings that interact with the power grid to optimize energy efficiency, load shifting, and usage of low or zero-carbon generation sources for economy-wide decarbonization in equitable ways for the entire community. 

2. Energy System Resilience – Aging infrastructure, combined with severe weather events and climate change are expected to affect every aspect of the electricity sector - from generation, transmission, and distribution, to demand for electricity. Catastrophic events such as the 2021 Texas winter storm event severely disrupt the normal functioning of critical electrical grid infrastructure for significant durations.  This grand challenge seeks to develop, benchmark, and scale the adoption of AI technologies that can help to predict weather, demands, generation plant and grid conditions, and continuously optimize the system to minimize unplanned outages and intelligently control energy flow to minimize or eliminate the impact of such events in the future and reduce unplanned outage durations.

3. Environmental Impacts – AI technologies can help power grid operators to better predict loads, optimize usage of low-and zero-carbon generation sources, improve efficiencies, and lower emissions.  AI/ML can also help identify and lessen wildfire risks, improve vegetation management, and reduce impacts on wetlands and endangered species.  This grand challenge seeks to utilize AI technologies to further reduce carbon emissions and minimize environmental impacts.

4. Intelligent and Autonomous Plants – With the growing need for flexibility in large-scale power plants and optimizing their interaction with resources on the grid, controls and automation have become increasingly important in the utility industry. Automating tasks using AI helps reduce costs, improve efficiency, and preserve energy system assets through optimized maintenance and utilization. AI applications such as digital twins, ML/Reinforcement learning, machine vision, and automatic diagnostics, among others will enable energy system operators to focus on the most valuable maintenance, asset management, and integration tasks.  This grand challenge seeks to develop, benchmark, and scale AI applications for automation.

5. AI-Enhanced Cybersecurity – Cybersecurity is foundational to energy systems and operations, protecting critical utility data, such as personally identifiable information, critical operational data, operational technology systems, and data for AI models.  The current and future energy generation and distribution systems rely on an increasingly digital, interconnected landscape.  AI shows promise to enhance cybersecurity improving capabilities like network monitoring, identifying suspect activity, and automatically detecting vulnerabilities in software codes.  This grand challenge seeks to advance the state-of-the-art in cybersecurity practices through effective implementation of AI solutions, benchmark those identified solutions, and scale them across the electric power industry.

Key Enabler #1:  Industrywide Data Sharing and Governance – Larger, more robust datasets will be needed to scale AI applications, frequently larger than any one utility has at its disposal.  For this reason, sharing data across industry to identify, collect, and curate key sets of data will be critical to these efforts. Data must be collected, labeled, anonymized, and stored in a secure fashion with the proper data management infrastructure. Continued efforts must be made among electric power utilities to address the data sharing challenge, as industrywide data sharing and governance will enable critical AI technology adoption.

Key Enabler #2:  Data Science Expertise and Training – There is a talent shortage for data science and AI expertise in the industry.  EPRI is working to augment the existing data science skills of staff by accelerating training efforts, including hosting training events, providing links to publicly available materials, and via a co-funded effort with the U.S. Department of Energy. All these efforts, and many more, will be needed to develop the necessary skills to match AI talent with electric power experts to scale up adoption of AI throughout the electric power sector.

Conclusions

Five grand challenges have been identified and are being issued to the AI and electric power industries to accelerate technology development and deployment for near term and long-term benefits to both industries. Successful implementation of these grand challenges and key enablers will provide substantial benefit to both industries both now and in the future in terms of increased safety, reliability, resiliency, reduced environmental impact, and improved economics.  These benefits will improve the quality of living for people around the world through reduced emissions, improved reliability of electricity, and lower energy costs.

How to Get Involved

Learn more about these Grand Challenges and how to get involved by attending and participating in the grand challenge breakout sessions at EPRI’s AI and Electric Power Summit on September 28-29. Register here.