Ahead of The Curve:
2025 Innovation Imperatives for Utilities

1. Modernized grids to enable renewables at scale

The rise of decentralized energy generation—such as solar farms, wind turbines, and home-based battery systems—has made the grid multidirectional. This shift creates new challenges for grid stability, as these energy sources are intermittent and less predictable. Predicting and optimizing energy flow in a decentralized environment will be increasingly essential as more renewable sources come online.

The IEA predicts renewables will account for 35% of global electricity generation by 2025. Many of our clients have faced challenges managing real-time fluctuations in renewable energy generation, and together, we are making AI-driven grid management systems a top priority. Smart grids, microgrids, and energy storage solutions are becoming crucial for addressing these issues. AI-driven systems can now adjust within seconds to fluctuations in energy output, maintaining grid balance and ensuring reliability.

The widespread deployment of IoT devices and edge digitization also transforms how utilities monitor and manage their operations. We advise many utilities to focus on three IoT priorities: improving IT-OT convergence, integrating IoT with satellite and drone data for better grid monitoring, and investing in systems that support real-time communication between operational technology and IT systems. When combined with Geographic Information Systems (GIS) and AI, IoT sensors also enable the creation of digital twins — virtual replicas of physical assets and processes. These digital twins can reduce downtime, extend asset longevity, and anticipate and address potential disruptions by simulating grid behavior under varying conditions.

2. GenAI and machine learning for predictive maintenance and demand forecasting

Over the past year, many utilities have asked for ways to transition from reactive to predictive maintenance. By integrating Generative AI and machine learning, utilities are better equipped to forecast demand and predict equipment failures. Traditionally, maintenance follows a fixed schedule, but today’s AI-powered systems collect real-time data from IoT devices to predict when specific assets will likely fail. This shift to condition-based maintenance significantly reduces costs and ensures that repairs are conducted only when necessary.

Additionally, AI-driven demand forecasting has become more accurate, using historical and real-time inputs to anticipate energy demand. In the coming year, utilities will be presented with new opportunities to leverage GenAI to generate more granular insights into demand patterns and pair AI with satellite and drone data to strengthen remote monitoring and risk detection, such as for grid degradation.

4. EVs and V2G technology reinforcing grid stability

As EV adoption grows, utilities face the dual challenge of supporting a robust charging infrastructure while integrating Vehicle-to-Grid (V2G) technology into their operations. In pilot projects and emerging trials, we’ve seen utilities begin to explore V2G technology, turning electric vehicles into mobile energy storage units that can feed energy back into the grid during high-demand periods. While still in the early stages, V2G holds significant potential as EV adoption grows and two-way metering systems become more mature.

Now is the time for utilities to begin exploring V2G infrastructure and EV aggregation software as part of their future strategy to maximize grid resilience. As V2G technology matures and EV adoption grows, utilities could aggregate numerous EVs to create virtual power plants (VPPs). These VPPs hold the potential to reduce the strain on traditional power plants and enhance grid flexibility, but widespread implementation will depend on further development of two-way metering systems and regulatory support.