Artificial Intelligence is making its way into all types of industries, including the energy sector. IEEE Senior Member Shawn Chandler shares his thoughts on the ways that AI will affect and impact our lives as energy consumers.
IEEE Transmitter: We’ve heard of AI impacting transportation, healthcare and security. In what major ways is AI impacting the energy sector?
Shawn Chandler: Making a distinction for what is meant by “AI” is important here. When I refer to AI, I mean the the application of machine learning for the purposes of automation and computational support of decision-making in a complex system. The energy industry is largely impacted just the same as the other industries you mention, with significant growth in the use of AI to leverage big data and draw inference from very large data sets. Many aspects of the utility enterprise will benefit, but in particular, AI has great potential to coordinate and optimize the use of distributed energy resources, electric vehicles, and IoT. Use of AI also aligns well with the current pace of change that utilities, regulators and customers expect with improvements to common utility operations including:
- reliability —self-healing grids, operations improvement and efficient use of renewable resources and energy storage
- safety —outage prediction and outage response
- cybersecurity of systems —threat detection and response
- optimization —asset, maintenance, workflow and portfolio management
- enhancements for the customer experience —faster and more intuitive interactive voice response, personalization, product and service matching
IEEE Transmitter: How will the impacts of AI on energy help the everyday consumer?
Chandler: Generally in the ability to optimize the use of a consumer’s resources, categorized as energy storage, load and generation. For example, demand side management automation using AI – such as moving or scheduling a particular use of energy into a specific demand period – can result in a decrease to the cost of service for a consumer. AI may also alert or inform a consumer based on real-time sensing. For example, informing on an emergency issue, such as a failing appliance in the home, or a downed power line in an expected path of travel, or for recommending choices and services, such as energy-related equipment settings, replacements or upgrades.
IEEE Transmitter: How will AI impact power consumption, supply and demand across the energy system to save money?
Chandler: First, AI is being used to optimize the supply side. One aspect of operation of the electrical grid is the ubiquitous use of reserve resources (power plants) across the grid standing by in case of an emergency outage of another power plant somewhere in the system, or a change in the availability of system generation due to renewable resource intermittency, such as that experienced with wind and solar. AI can be used to better predict these changes, and craft a response that is tuned to expected system behavior over time.
These optimization efforts result in energy savings, through management of a thinner margin of reserves that is less costly to the operation of the system as a whole. In terms of consumption, AI will be used to better predict the failure of critical equipment in the distribution chain, lessening costs associated with outage and unexpected service disruption by allowing for less costly planned replacement or reliability centered maintenance. Finally, on the demand side, AI will better manage the use of energy for each consumer, through IoT system interaction, smart device integration and so forth.
IEEE Transmitter: Where do you see the energy sector in five years and how involved will AI be then compared to now?
Chandler: In the next five years the use of energy storage and IoT is expected to increase significantly, along with an increased development of distributed energy resources with two-way power flow in the distribution grid, and new roles for energy service suppliers, utilities and consumers that produce energy, or prosumers. This evolution of the grid has been called the “Energy Cloud,” and the use of AI can be considered critical to the management of such a system, given the number of points of control in the grid increasing from many tens of thousands to hundreds of millions, or even billions. Compared to now, where AI is a tool being explored for optimization opportunities, in the future, it will be a requirement for effective grid participation.
Shawn Chandler is an IEEE Senior Member and Associate Director at Navigant Consulting, Inc., within the Energy practice. Mr. Chandler contributes to engagements relating to grid modernization and smart grid program development, having extensive experience in power operations, utility system integration, and real-time energy system development.
Written by IEEE on June 1, 2017