In the first part of the interview we talked with Ruslan Slobodian, Commissioner of Ukraine’s National Energy and Utilities Regulatory Commission (NEURC), about Ukraine’s energy challenges and how AI and smart meters could help build resilience during tough times. Now, let’s step back and look at the bigger picture.

Artificial intelligence is changing the way we think about energy, especially when it comes to smart meters. These devices aren’t just about tracking energy use anymore - they’re becoming powerful tools for managing grids, bringing in renewable energy, and making things more efficient for both consumers and utilities.

So, what does that actually mean? How can AI make energy data smarter, decisions faster, and grids more flexible? In this second part, we’ll dig into these questions and explore how AI could transform the future of energy.

With challenges that we discussed before, how can the energy industry overcome them to fully leverage AI in smart metering?

Smart metering systems are becoming increasingly affordable per metering point, which is helping to accelerate their implementation worldwide. At the same time, software development is progressing rapidly, enhancing the functionality of smart metering and grid management systems.

For Ukraine, the key factor for future progress is, of course, bringing stability to the region and ending the war. On a global scale, the main challenge lies in ensuring an effective mechanism for human oversight as AI continues to evolve. While AI’s progression cannot be halted, maintaining human control and interaction with AI is crucial - not only for individual technological systems but for the well-being of humanity.

How can AI in smart meters improve energy data accuracy and optimize management and storage?

Smart meters can and should provide a large amount of data not only on electricity consumption but also on the parameters of the power grid. However, this raises the problem of verification and further use of this data. Of course, there is software that makes it possible to automate these processes. But how do we assess the reliability of this data? How to effectively use this huge amount of information? Without effective algorithms for processing this information, there is a risk that most of this valuable information will not be used effectively and will only take up space in archives.

Despite the variety of smart metering systems currently available, humans still play a decisive role in evaluating information and making decisions. However, it's becoming clear that humans are a weak link in this process. AI enhances data verification, automates analysis, and proposes actionable insights, reducing reliance on human decision-making and improving system reliability.

The integration of Energy Storage Systems (ESS) is accelerating advancements in AI-driven power grid management. AI-powered control algorithms optimize ESS charging and discharging processes, ensuring seamless integration with RES and the external grid.

 Effective AI management not only maximizes ESS efficiency but also extends battery lifespan by refining charging and discharging patterns. This intelligent approach enhances energy efficiency, promotes system sustainability, and improves overall grid performance.

How does AI influence demand response programs, and what role do smart meters play in this process?

AI can analyze customers' energy consumption patterns using data from smart meters and recommend adjustments to shift energy use to times when electricity is cheaper. By integrating with smart home systems, AI can directly control appliances like boilers, washing machines, and lighting, etc., leading to significant savings for consumers through more efficient energy use and optimized consumption timing. The greatest benefits will be realized when customers have their own energy generation sources, such as solar panels, and energy storage systems. Managing such complex, combined systems is already beyond human capabilities, although Energy Management Systems (EMS) are in place to effectively manage energy equipment for such users. However, the integration of AI will maximize the efficiency of this management.

Additionally, AI can facilitate interaction with external power grids, enabling customers to provide ancillary services to DSOs, and generating additional revenue. While Ukraine, like many other countries, currently lacks a market for ancillary services for DSOs, the rapid growth of renewable energy will inevitably lead to the creation of such markets, as grid operators will increasingly require these tools to manage the power grid effectively.

As the energy landscape evolves with the integration of advanced technologies, it raises important questions about regulatory frameworks and policy adaptations. How might AI-driven smart meters influence energy regulations and policies?

The main role of the Regulators in the energy sector is to ensure a balance between the development of energy systems and the interests of consumers. Of course, the Regulators cannot allow the rapid large-scale introduction of expensive technologies in the energy sector, as this will have a major impact on prices in energy markets, which will directly affect the economies of countries and the welfare of consumers. However, regulators can and should create conditions for the gradual implementation of these technologies over the next 10-15 years without creating obstacles to further energy sector’s development.

In light of these considerations, what should regulators keep in mind when integrating AI into the energy sector, particularly with smart meters?

For energy regulators, the specific technology driving the efficient development of the energy sector, whether AI or otherwise, may not be the primary concern. However, it is becoming increasingly clear that there may soon be a need for dedicated regulatory bodies to oversee the development and use of AI. As AI becomes more integrated into critical sectors like energy, ensuring responsible and ethical growth will be crucial.

The rise of AI-driven smart systems also brings questions around new services: what types of services should regulators permit in this evolving landscape, and how can they effectively oversee these services? DSOs, traditionally responsible for grid stability, must now accommodate a vast array of participants, including those who generate, store, and feed energy back into the grid, creating a complex and dynamic ecosystem. This shift raises fundamental questions: is the DSO’s role and service still fully regulated, and who is allowed to provide these services, set prices, and approve related investments? According to the Clean Energy Package, DSOs are required to support consumer and community rights, creating an environment where flexibility and innovation are encouraged. Regulators will need to carefully consider the frameworks that govern these rights and responsibilities, ensuring that the transition to a more decentralized, AI-integrated energy system remains fair, accessible, and resilient.