AI smart – AI is moving smart grids from reactive control to real-time forecasting—helping utilities balance renewables, reduce outages, and prepare for EV-driven demand growth.
Electricity grids were built for a simpler era: centralized power plants, predictable demand, and one-way power flows.. Misryoum says the shift to clean energy is forcing a redesign in real time—powered by AI-enabled smart grids that can forecast conditions and steer electricity flows before problems appear.
Across energy networks, the new goal is not just “more renewables,” but steadier operation when the weather changes.. AI systems can combine data from sensors. satellite observations. and operational records to run predictive analytics. using machine learning to align supply with demand at fine time scales.. Misryoum’s read of the latest smart grid reporting is clear: utilities want grids that behave more like software—dynamic. responsive. and resilient.
A practical example is solar.. Instead of waiting for solar output to change and then reacting. AI-enabled forecasting can now-cast solar irradiance and predict photovoltaic (PV) generation.. Misryoum describes this as a capability that helps grid operators curtail less solar energy unnecessarily and schedule reserves more accurately.. When solar output is forecast well. the “backup” capacity can be positioned more efficiently—meaning fewer expensive compromises during periods of volatility.
Wind faces a similar challenge, but with different physics.. Forecasting wind generation depends on atmospheric conditions and performance data.. AI-driven models can improve the accuracy of those predictions, giving operators better visibility into variability and reducing wasted capacity.. Misryoum also points to the reliability side: where digital intelligence identifies potential failures earlier. utilities can address issues before they escalate into downtime.
The emerging smart grid toolkit is increasingly built around connected measurements—smart meters. IoT-enabled infrastructure. and distributed sensors—feeding analytics platforms.. Those analytics then inform decisions on load balancing, grid stability, and security.. Misryoum notes that as demand is projected at micro-scales. utilities can fine-tune how electricity moves through local networks. not just at the highest transmission levels.. That matters because the renewable transition is often felt most strongly at the distribution edge. where voltage and frequency stability can become harder to manage.
What makes this moment distinctive is the convergence of several technology directions.. Digital twins—virtual models of grid assets—are being used to simulate behavior and test responses without risking real-world operations.. Microgrids and distribution automation are also expanding. creating smaller. controllable power systems that can island or adapt when centralized networks face stress.. Misryoum sees electric vehicle (EV) integration as a key accelerant in that evolution: EV charging adds new demand patterns and opens the door to bidirectional energy flow. turning some vehicles and batteries into flexible resources.
Misryoum also highlights how the investment case is getting more concrete.. Reported transmission investment figures indicate utilities are preparing for upgrades at substations and transmission lines—work that pairs physical expansion with digital control.. Smart grids are not only about software intelligence; they require hardware modernization so that data can be captured. power can be routed efficiently. and protection systems can respond faster.. In other words, AI is becoming the “brain” of a grid that still needs a stronger skeleton.
At the systems level, Misryoum says distributed energy resource management systems (DERMS) are gaining prominence.. DERMS helps coordinate multiple distributed assets—solar panels. wind generation. and battery storage—so the grid doesn’t experience sudden spikes or fluctuations.. Virtual power plants (VPPs) push the concept further by aggregating many small resources. like residential solar and batteries and even controllable loads. into a single dispatchable entity.. Misryoum’s framing emphasizes why this is important: grid operators can treat dispersed flexibility as a coordinated tool. not a collection of isolated devices.
Taken together, the promise of AI-enabled smart grids is straightforward: more reliable clean power with less waste.. Yet the path is not only technical.. Misryoum sees real-world impact in the day-to-day experience of households and industries—fewer outages. better stability during peak periods. and a grid that can accommodate electrification trends like EVs and heat pumps.. As demand patterns shift. a grid that can predict and adapt will likely be the difference between a smooth transition and costly disruptions.
Looking ahead. Misryoum expects smart grid progress to hinge on two-way communication. automation. and improved forecasting accuracy—especially as renewables and electrification continue to expand.. The likely direction is toward a more decentralized, intelligent power system that can balance energy security with sustainability goals.. If AI-enabled smart grids deliver on that promise. clean energy won’t just grow—it will operate with the steadiness modern life depends on.