3 AI technologies innovation leaders must consider for utility asset management (Part 1: Introduction)

Managing the distribution grid is hard

With the rise of AI-powered technologies, innovation leaders at utility companies have the opportunity to transform power distribution utility asset management.

The distribution system is a critical component in every grid system. It is responsible for delivering “usable” energy directly to businesses and homes. However, extreme weather events combined with increased reliance on electricity are putting more and more strain on the distribution grid each year. In this series of articles on AI technologies for heads of innovation at utility companies to consider in their asset management strategy and operations, we'll take a deep dive into the four types of AI systems that can impact utility asset management as we know it today.

Unlike the transmission and generation aspects of the power system, the distribution grid involves a vast network of relatively low-voltage power lines that deliver electricity directly to homes, businesses, and industries. This decentralized structure makes it challenging to detect and address issues promptly. Additionally, the distribution grid faces diverse and unpredictable loads, including fluctuating demands from various customers. This requires constant monitoring and fine-tuning to ensure stability and reliability. 

As a result, managing all of these assets (there are 185 million distribution poles within the US alone!) is a huge endeavor. Major utilities’ spending on distribution have seen steady increases year over year. Spending on power grid distribution in 2019 was $57.4 billion, which is 64% higher compared to 2000. 

Utilities are now under more pressure than ever to modernize their distribution grid and increase grid reliability and safety all while keeping costs down. As the demand for electricity continues to rise, utilities need to adapt and innovate in new ways to deliver more power, more reliably, and without more on hand.

Stricter regulations and compliance require utilities to innovate

Deregulation in the electric power industry has had a significant impact on the regulatory landscape, compliance standards, and the overall operation of the grid. The move toward deregulation, which took hold in the U.S. in the late 1990s and early 2000s, was designed to increase competition in the marketplace, reduce costs for consumers, and stimulate innovation in the sector.

As a result of deregulation, Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) were created to serve critical roles in maintaining the fairness and integrity of the marketplace. These organizations manage the wholesale electricity markets and are responsible for ensuring that all participants follow established rules and regulations. RTOs and ISOs also maintain the stability and reliability of the grid, which is a critical function given the complexities of electricity supply and demand.

On top of the direct, immediate impact of deregulation on stricter regulations and compliance standards in the utility industry in the U.S., demand has increased significantly over the past two decades. People are increasingly dependent on having reliable access to power. Therefore, both public utilities and investor-owned utilities across the country implement stricter mandates for grid management. Utilities need to conduct more frequent inspections on more of their grid and constantly improve service reestablishment performance metrics to abide by stricter regulations and compliance standards.

This regulatory pressure combined with more frequent extreme weather events and an aging grid infrastructure results make it impossible for utilities to continue with legacy grid management methods. 

With this demanding context for utilities come new opportunities for innovation and efficiency. Advanced technologies, including artificial intelligence, have the potential to significantly enhance utilities' abilities to meet increasingly stricter standards. 

AI can assist in various aspects of utility operations, including: predictive maintenance, grid management, regulatory compliance, energy efficiency and customer service.

AI can help solve distribution grid challenges

The main issue that utilities face when it comes to the distribution grid can be summarized in one word: scale. In order to manage all these assets, utilities must not only address the issue of scale when it comes to data acquisition, but also data analysis. Current methods that involve dedicated truck rolls and manual inspections and scattered data repositories are not the answer.

AI can help address these issues. 

On the data acquisition side, the integration of AI with advanced data collection methods is poised to revolutionize distribution asset management. AI can significantly streamline data collection processes by autonomously identifying optimal travel paths or swiftly detecting and categorizing objects of interest. 

Moreover, AI can help manage large amounts of data. By employing machine learning algorithms, AI can process and analyze collected data to identify patterns, anomalies, and predictive maintenance opportunities. AI can intelligently filter and prioritize data, selecting only the most relevant information for further analysis, minimizing the risk of information overload. 

This focused approach allows utilities to make data-driven decisions more rapidly and allocate resources efficiently. Furthermore, AI can learn from historical data and performance trends, enhancing the accuracy of asset condition assessment and optimizing maintenance schedules.

The integration of AI with data collection technologies empowers utilities to proactively address distribution grid issues, optimize asset performance, and ultimately improve grid reliability and customer satisfaction.

Novel technologies for distribution asset management

There are several emerging technologies that are being developed to address these very needs. These include:

• Drones
• Satellites
• Vehicle-mounted cameras

At first glance, it may seem difficult to know the advantages and disadvantages of each solution. In this next series of articles, we’ll go through each option and discuss the pros and cons when considered from several angles:

• Data type and quality: Here, we discuss the different types of data you can collect with this technology. We also go over the granularity / fidelity level of the data and its potential applications.

• Cost: Every utility is interested in keeping O&M as low as possible while maximizing capital investments. This is no different when it comes to asset management. We look into how these technologies compare to others that provide similar results, the associated upfront investments to get started and any ongoing costs for continued use.  

• Ease of use: New technologies typically come with some type of barrier to entry. Some you can use out of the box while others require specialized training or certifications. 

• Scalability: While some technologies may enable utilities to perform certain asset management tasks faster than traditional methods, that does not mean that they can truly be deployed at scale. We will help you evaluate which technologies have the potential to scale and which may still have some bottlenecks. 

You can now read our first article in this series focused on drones for utility asset management. We will be discussing the pros and cons of the drone technology with an emphasis on distribution asset management.