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September 30, 2025
Sand Technologies
The traditional control room was built for a grid that no longer exists. Designed around steady demand and predictable supply, it now faces a complex system defined by volatility, decentralization and torrents of real-time data. What once could be balanced with routine oversight has become a scale of complexity no operator can manage alone.
The growth of distributed energy resources (DERs), from rooftop solar to small-scale wind turbines and electric vehicles (EVs), is redefining grid dynamics. Where power once moved in a single direction from large plants to end users, it now flows both ways as households and businesses generate, store and feed energy back into the grid.
This shift creates a more decentralized and interactive model, where millions of small, scattered energy producers and consumers (often referred to as “prosumers”) actively shape supply and demand. For operators, it also presents the challenge of maintaining stability across a fragmented system, which in turn requires advanced tools for visibility, forecasting and coordination.
Layered onto this decentralization is increased digitalization of the grid. The deployment of smart power grids, IoT devices and advanced sensors has expanded rapidly, with global smart meter installations alone projected to surpass 2.1 billion by 2033. This digital infrastructure provides operators with more visibility into grid conditions than ever before, but it also creates a data deluge.
For example, utilities once carried out monthly or quarterly transformer checks, such as infrared scans or oil sampling, with a handful of specialists reviewing results. Today, permanent infrared scanners, real-time dissolved gas analysis (DGA), and bushing monitors can generate continuous data streams for thousands of transformers.
What was once a manageable trickle of information has become a flood, far beyond the capacity of manual review. Without intelligent filtering and analysis, the abundance of information can overwhelm operators, slowing down decision-making.
Compounding these shifts is the continued integration of renewable energy. Variable renewable sources such as wind and solar already account for more than 40% of global electricity generation.
Growth rate of solar generation in 2024, a six-year high
Electricity demand growth in 2024, which was amplified by heatwaves
As flexibility becomes central to balancing renewable supply, the market itself is also shifting. The grid is no longer shaped solely by utilities and large generators; households, communities and new entrants are stepping in as active participants.
This change blurs the line between producers and consumers. Rooftop solar feeds excess power back into the system, EVs double as mobile storage and aggregators bundle distributed assets to compete in wholesale markets. To keep pace, market design must evolve. Pricing models, participation rules and oversight frameworks need to accommodate a system where millions of smaller actors collectively influence stability and efficiency.
Enhance grid visibility with dashboards
Stay ahead with predictive alterts
Streamline action with coordinated response systems
Prepare with simulations and forecasting
What the AI-powered control room looks like in practice
In today’s grid, operators are often overwhelmed with information from dozens of siloed systems, including generation data, weather updates, asset health and much more. Yet research shows that utilities analyze only 2–4% of the data collected from intelligent grid devices, leaving most insights untapped. This data deluge and fragmentation slow down decision-making and make it more difficult to understand how one variable affects another.
A unified dashboard, powered by AI and advanced analytics, simplifies this complexity by integrating these streams into a single, contextual view. By viewing demand, generation, DER activity, weather and grid health simultaneously, operators can understand cause-and-effect relationships in real-time and act with greater confidence. It transforms data from noise into a clear operational picture.
The greatest value of a modern control room lies not in reacting to failures, but in preventing them from occurring. Predictive alerts take this further by using advanced analytics to identify early indicators of stress, such as transformer overheating, sudden demand spikes or irregular DER output. Flagging these patterns before they escalate enables operators to intervene earlier and limit the impact on grid stability.
This capability is powered by advanced data analytics, which draws on sensor readings, weather data, historical performance and operational records to surface patterns that would otherwise go unnoticed. Instead of relying on fixed schedules or reacting to issues after they arise, utilities can shift toward condition-based monitoring and earlier intervention.
In the control room, this translates into clearer, prioritized insights rather than scattered data points or late-stage alarms. Operators gain a sharper view of emerging risks, enabling quicker and more targeted decisions that strengthen overall grid resilience.
Identifying a problem is only the first step; resolving it quickly is what keeps the grid stable. Traditionally, this meant that operators had to relay instructions across multiple teams and systems, a process that can be slow and fragmented. Workflow orchestration closes that gap by linking alerts to the right teams, assets and processes, ensuring that action happens quickly and in a coordinated way.
These actions may include dispatching a field crew, adjusting energy storage, activating demand response or rebalancing load across the network. Because these actions are connected to the same operational context, they happen faster and with fewer manual handoffs. For operators, this means less time spent managing coordination and more time ensuring that the right decisions are carried through effectively.
One of the biggest challenges in grid operations is preparing for events that have not yet occurred. A sudden drop in solar output at peak demand, a surge in EV charging overnight, or extreme weather can all destabilize the system. Simulation and forecasting tools provide operators with a means to explore these “what-if” scenarios before they occur, enabling them to anticipate vulnerabilities and plan responses in advance.
Digital twins take this a step further by creating virtual models of grid assets and systems that mirror real-world conditions. By mirroring real-time data from the network, digital twins allow operators to test interventions in a risk-free environment. They also help cut through the noise by prioritizing data, applying risk scoring and flagging what requires immediate attention. These insights not only support real-time decision-making but also inform long-term planning for capacity, investment and resilience. The video below shows this in action:
AI-powered control rooms are transforming operations across industries. From energy grids to manufacturing and logistics, organizations are leveraging AI-enabled control centers to boost efficiency, resilience and service delivery.
For example, in healthcare, AI-powered control rooms are helping governments improve care and expand access. Rwanda’s National Health Intelligence Center, built with Sand Technologies, aggregates health data to predict trends, optimize resources and guide policy. Connected clinics receive real-time insights, enabling workers to make informed decisions even in areas with limited infrastructure.
These capabilities are also helping rural healthcare providers expand access to underserved communities. By integrating data on existing facilities, population distribution, transportation and geography, Sand Technologies’ Rural Health Operating System uses AI to identify where new clinics and resources are most needed. Watch the following video to see the results:
Within the power sector, utilities are beginning to reimagine the control room with AI-driven applications. For instance, the U.S. National Renewable Energy Laboratory (NREL) developed eGridGPT, which brings genAI into grid operations. The system ingests real-time SCADA, state estimation, contingency analysis and planning data, then uses digital twins to test AI-generated actions. In initial trials, eGridGPT cut the time required to align EMS operational models with planning models from weeks to just minutes.
These examples illustrate how AI-enabled control rooms enhance operators’ capabilities, transforming complex data into actionable insights. As the electricity grid becomes more dynamic and distributed, similar approaches can help operators anticipate challenges, coordinate resources and make informed decisions that maintain stability and improve overall system performance.
With AI-enabled control rooms, the role of the grid operator is no longer just about monitoring and reacting. Operators must now approach their work in the following ways:
Transitioning to AI-enabled grid operations extends beyond the implementation of new technology. It requires a structured approach that addresses the interplay between tools, people, processes and policy.
Deploying AI, machine learning, automation, and edge computing is only part of the equation. Effective transformation also depends on modernizing legacy infrastructure, integrating data systems and ensuring the tools support operational objectives.
Similarly, technology alone does not guarantee better outcomes. Operators and teams require support to adapt to new decision-making processes, learn to interpret advanced insights and work in more collaborative and agile ways. Training, reskilling and redefining roles are essential to ensure the workforce can fully leverage AI-enabled systems.
Managing the grid effectively requires thinking beyond individual components. Planning, operations and stakeholder engagement must be coordinated across the entire energy ecosystem, including distributed resources, markets, regulatory requirements and community interests.
Finally, policy and regulation shape what is possible. Forward-looking frameworks can enable DER integration, clarify responsibilities for AI-assisted decisions, encourage data sharing and provide incentives for resilience, reliability and flexibility. Regulatory alignment is critical to supporting both innovation and operational effectiveness.
As AI reshapes control rooms, it also alters our perspective on the grid itself. Operators are no longer just reacting to events; they can identify patterns across distributed networks, anticipate emerging risks and test interventions before they happen. This creates space for more strategic decision-making: determining where to invest in infrastructure, how to integrate new energy sources, and how to coordinate multiple stakeholders effectively.
In practice, this means the grid can evolve from a reactive system to a continuously learning network, where operational insights inform long-term planning, market design and policy decisions. Operators using AI-enabled control rooms can develop approaches that make the grid more flexible, resilient and responsive to the evolving demands of today’s energy landscape.
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