Utility grid modernization

Perspectives

Five in 5: Utility grid modernization

Innovation in the utilities industry

The utilities industry is facing new challenges such as rising demand, evolving energy sources and distributed energy resources. If not managed, these can lead to new hardships. In this Five in 5, specialists Christian Grant and Craig Rizzo go over leveraging data architecture and artificial intelligence (AI) to modernize grid operations.

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1. Implementing a strategy of mixing traditional utility business (connecting producers of electricity with customers) with development of new production of renewable electricity (grid modernization) requires innovation. Do you see many utilities moving in this direction?

Christian: Absolutely. The need for innovation in the utilities industry is paramount. We’re seeing utilities across the board reinvigorate their innovation capabilities and focus on addressing the accelerating business cycle and the needs of their customers, stakeholders, and regulators. The model that works for one may not be universally applicable, but the drive toward innovation is a common trend across the industry, especially given the rapid changes we are witnessing.

Craig: Yes, because if you are just in wind or storage development, for example, the margins are getting so tight there that it’s extremely competitive. So, I think what we’re seeing, including in Europe, is utilities diversifying their portfolios into other types of renewables, storage coupled with renewables, and looking hard at the hydrogen space and how they can leverage renewables to produce and then store hydrogen to distribute and deliver. Ultimately, they’re looking to expand their value proposition and then the value chain because the margins are so tight now, and in some of these areas, it’s going to start to become more of a commodity.

2. Do you see a possibility that there will be more of a federal push toward broad deregulation to allow more innovation/competition similar to what we saw with the telecommunications industry in the 1980s? If so, how would grid ownership (including security and safety) change to a more open model?

Christian: That’s a good question that has been debated for years. Some states have taken steps toward deregulation, but few have reached pure deregulation. The more pertinent question is whether the federal or, more likely, state governments will act to prevent the erosion of the competitive barriers that utilities have historically enjoyed. In a decentralized world with distributed energy resources such as rooftop solar, electric vehicles, and storage—customers can produce and consume electricity independent of the utility, and that can create business model challenges. The question is whether governments will allow utilities to expand their services beyond the meter, enabling them to compete in these new areas given the deterioration of the cost-of-service model. As we are seeing with data centers, nonutility entities are bringing innovative solutions to the industry, and it will be interesting to see the knock-on effects of these ideas.

Craig: There are a number of key considerations including grid reliability, resiliency, customer service, and affordability. Affordability is the challenge for utilities modernizing today while meeting regulatory mandates. If additional deregulation can be shown to impact near-term affordability, we could see more movement in that direction.

3. With the push for electric vehicles (EVs) and other distributed energy resources (DERs), how urgently are utilities moving to market with these services?

Christian: I think it’s important, but the urgency varies by region. In areas with low EV and distributed energy resource penetration, utilities are not in a hurry because their customers are not demanding these services. However, in jurisdictions where these technologies are being rapidly deployed, utilities are under pressure to perform. Interconnection is crucial. Customers want to connect their assets and be compensated for them, all in a timely manner. Utilities should keep up and effectively serve these customers, or they could potentially risk third parties stepping in to fill the gap. And so, it’s important to realize the importance of effectively meeting customer needs from a competitive perspective.

Craig: Most are moving at a pace that reflects DER and EV adoption rates in their region or service territory, though some are clearly behind and not accounting for the time and effort to correctly prepare for this transformation. There’s a potential risk of moving too fast and moving too slow. Utilities can mitigate both by maintaining a current view of what net-new capabilities are needed, when they should be in place, and the effort of implementation. Customer adoption models and local regulations will continue to evolve, as we’re seeing now with a leveling off of EV adoption rates. So, building flexibility into their plans and keeping them current will likely be important in helping to minimize risk.

4. From the perspective of improving grid operations and modernization, how have you seen the industry structure data such that it is collected over time and allows the value to be extracted from those efforts?

Christian: There is a pressing need for a comprehensive data architecture in addition to focusing on data for specific applications or projects. Creating a single source of truth is critical to efficiently support various grid operations and applications, including AI in utilities. I suggest adopting a data-first culture, similar to the safety culture that utilities embraced, stressing the importance of high-quality, accurate, and up-to-date data. Underpinning this shift, recognize that data and the quality of that data is not information technology’s (IT) responsibility but one owned by the business. IT and operational technology (OT) organizations are responsible for providing access and analytics to use that data for operational advantages. Today we spend more time with clients planning and designing the application, data, network, and security layers of their architecture to take advantage of advanced analytics, AI, and future technological changes.

Craig: I have found that utilities are facing new challenges with the volume and diversity of data, especially from customer-owned assets—and what has grown out of that is the need for merging, synchronizing, and governing this data effectively. There is a high level of uncertainty and unknowns in data management, and often we see that utilities resort to piecemeal solutions due to the lack of a coordinated data architecture. This brings up the importance of building a scalable and secure data foundation to avoid technical and data debt; and ensuring data quality, ownership, decision rights, and a single source of truth to maintain flexibility for future needs. The future state of this architecture is structured to create value across the utility, from operations to customer systems.

5. What are the primary applications and use cases you see clients currently deploying for AI in utilities? Do you foresee that changing as AI matures?

Christian: We are seeing numerous AI use cases across the utility enterprise, particularly in operational areas. For example, through our market relationship with NVIDIA, we are exploring AI applications in automated metering infrastructure (AMI). AI at the grid edge can operate in a way almost akin to a self-driving car, making real-time decisions without needing to revert to a centralized system. This is crucial for managing high penetration of solar panels, electric vehicles, and storage systems on a feeder or circuit. AI can enable efficient and dynamic grid operation, maintain cybersecurity, and honor commercial contracts in near real time. As AI in utilities matures, we expect a rapid increase in its adoption and transformative impact on the utility sector.

Craig: There are tremendous opportunities for our utility clients to leverage AI to evolve the utility operating model. There is so much inefficiency in the structure of a utility today that evolved to serve the needs of traditional power delivery models with little automation and almost no reliance on data and data insights. The opportunity here is to leverage data, automation, and AI to create a much more modern utility from the standpoint of business models, technology, processes, and people/organizational innovation. The utility planning process is a great example. It’s highly manual today and therefore a lengthy process. There are opportunities to leverage AI in utilities for process automation, to support better forecasting and propensity modeling for future customer EV and distributed energy uptake, and for data mining/insights on load patterns. This transformation has begun but is going to take time.

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