The Grid Mod Pod
Elizabeth Cook is Vice President of Technical Strategy at AEIC. To view episodes of The Grid Mod Pod, visit AEIC’s YouTube channel @aeicnews.
In an era where the energy sector stands at the cusp of transformation, AEIC has a strong sense of responsibility to our industry to convene and lead discussions on the challenges and innovations shaping this changing landscape. One way we are doing this is through The Grid Mod Pod, an ongoing podcast series in which I engage with executive leaders, technical experts, and innovators across all industry sectors.
During the first set of episodes this season, we intentionally focused on inviting leaders of companies that serve as partners and suppliers to utilities to share their perspectives. These discussions uncovered layers of insights, particularly emphasizing the potential for the revolutionary impact of Artificial Intelligence and machine learning in grid modernization.
This narrative unfolds like a tapestry, interlinking the profound contributions of experts with the promise of wide-ranging technological advancements, to create a story of an industry in flux, yet increasingly equipped with the tools and technology needed for a new energy economy.
Engaging non-utility entrepreneurs and innovators who are bringing new ideas and unique approaches to our industry is essential to harnessing the power and potential of the opportunities that grid transformation has to offer. Here is a summary of a few of the topics we've explored so far this season:
Grid Planning and Predictive Analytics
In a conversation with Brett Ryhal, Program Manager at Sargent & Lundy, we delved into the realm of grid planning (notably my favorite topic) and predictive analytics. We discussed how the transformative role of AI (artificial intelligence) transcends traditional methodologies, offering a set of insights that can foresee load increases due to electrification and data from the edge that understands the true use of our energy engagement. This foresight will enable utilities to adapt to changing load patterns with a newfound efficiency, reshaping the very fabric of grid operation.
Electrification and Integration of DERs
Following this, our journey transitioned into the exploration of building electrification and the integration of Distributed Energy Resources. In my discussion with Katrina Kelly-Pitou, Principal Grid Strategist at SmithGroup, we explored the buildout of electrification and the integration of Distributed Energy Resources and, in the process, uncovered how data and building models emerge as vital tools in optimizing the distribution and use of these resources.
These behind-the-meter building technologies, akin to skilled weavers, craft an energy system that is not only efficient but also responsive and self-regulating. They adapt seamlessly to real-time demands and changing environmental conditions, illustrating the dynamic nature of modern energy systems.
e-Mobility and EV Integration
Ken Munson, the CEO of Rhythmos, shared his insights on the burgeoning field of e-mobility and electric vehicle integration that further accentuate AI's critical role. Here, AI emerges as an analytical enabler, discerning usage patterns of electric vehicles and optimizing charging schedules to harmonize with the grid's capabilities, thereby managing the additional load brought about by electric adoption effectively.
Data Integration
Tom Martin, Vice President of the Customer Commercialization Group at ESource, emphasized data integration from sources like AMI, GIS, and SCADA, which underlines AI's transformative potential in grid management. AI, in this context, acts as a unifier of diverse data streams, providing deeper insights for informed decision making and developing effective customer-centric programs. This integration helps craft a more personalized and predictive narrative of energy solutions, maintenance strategies, and enhanced grid reliability.
Consumer Behavior and Demand Response
Miki Deric, Managing Director - Strategy, North America Utilities at Accenture, brought a unique angle to the table, discussing the critical role of consumer behavior and demand response in grid modernization. His insights highlighted the necessity of considering the human element in technological advancements.
Miki's views on the dynamic nature of energy consumption and the potential of smart technologies in managing demand were particularly compelling. His statement, "The intersection of technology and consumer behavior is where real transformation happens," resonates deeply regarding the industry's transition.
Transactive Energy
In a significant discussion with Doug Shannon, CEO of SageWerks Energy Consulting, we took a deep dive into the intricacies of transactive energy and game theory, realms where AI and machine learning can have profound influence. These technologies have the potential to craft algorithms that manage energy transactions in a decentralized digital landscape with remarkable efficiency, predicting and balancing the supply-demand relationship in real-time. It is a narrative of possibilities, weaving together insights on electric vehicles' impacts on the grid, the rise of decentralized energy resources, and the pivotal role of data in transforming grid operations.
Looking Ahead
The insights gleaned from my discussions with these industry leaders emphasized the need for a paradigm shift in utilities' approaches to infrastructure and operation. Each new conversation highlights the growing importance of the tools and technologies that are not just enhancements but central to the future of energy systems.
These discussions often circle back to a critical point: the traditional, century-old grid, designed in a fundamentally different context, is now being reimagined. This reimagining is not merely a technical overhaul but a complete rethink of how energy systems operate and impact our lives.
As put forth by Doug Shannon, "We need a new approach to managing interactions between energy producers, consumers, and prosumers in a decentralized digital landscape." These technologies are not just theoretical concepts but practical realities, poised to be deployed in our journey toward a modernized energy ecosystem.
They include addressing the challenges of aging grid infrastructure, the emergence of AI and machine learning as viable solutions for predicting maintenance needs, and optimizing asset use. AI and machine learning are transformative tools for the electric utility industry. AI allows computer systems to analyze data and make decisions, more rapidly, enhancing efficiency and reliability compared to current systems available.
Machine learning, which is a subset of AI, involves computers learning from data patterns to make informed decisions aiding in predictive maintenance and operational optimization. Contrary to fears, these technologies complement human expertise, focusing on data analysis for improved safety and grid performance.
Access to high computational powered machines and to significantly more data points has enhanced the transition from previously available technology and data accessibility. This approach is like refurbishing an old tapestry, extending the life of existing infrastructure, and thereby postponing costly replacements and upgrades.
Also, as we navigate through regulatory challenges and energy affordability concerns, AI's potential to offer cost-effective solutions becomes increasingly evident. It helps utilities to devise strategies that balance efficiency with consumer affordability, navigating complex regulatory environments with finesse. As we weave through these discussions, it becomes clear that the energy sector is not just undergoing change; it is actively shaping a future that is resilient, efficient, and sustainable.
AEIC's Grid Mod Pod series, enriched by the insights of experts in the industry, paints a vivid picture of AI's and machine learning's potential in revolutionizing grid management. This narrative is not just a story of innovation; it's a blueprint for the future of energy, a future where the grid is not just a network of cables and substations but a dynamic, intelligent, and responsive entity, ready to meet the challenges and opportunities of the twenty-first century and beyond.
