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The global power grid is facing an imminent crisis. We are moving toward a problem that most people aren't noticing. Today the lights still turn on when you flip the switch. By 2035, electricity demand is expected to rise by 50% while the infrastructure that supports it will age past its designed lifespan.
Renewable energy is essential to tackling climate events. Catastrophic failures are already happening, often spread across large service areas. But renewable energy also increases the complexity of operating the grid, which is already incredibly complicated. What is the solution? It isn’t one thing, and it's clear that more minor improvements aren’t enough. A complete overhaul of the electric grid, power systems, and our interaction with them is necessary.
The solution isn't just about buying new equipment or hiring more people. We need something much more radical: Active Grid Intelligence (AGI) and Embodied Energy Intelligence (EEI). In simple terms, give the grid a brain through AI, then equip it with hands and feet using robots.
I understand how that sounds, but I believe that within 10 years, robots powered by AI will handle 80% of the risky maintenance work that currently causes injuries. By 2050, we’ll see a power grid that predicts its own failures weeks in advance, repairs itself automatically in real-time without human intervention, and continues to improve at its task.
This isn't just speculative futurism or some far-fetched idea. Early adopters are already reporting 60% faster restoration times and 40% lower maintenance costs. The question for the utility industry isn't whether this future will happen; it's who will lead the transition and capture the trillion-dollar value it creates, and who will be scrambling to catch up.
Imagine this: It's midnight and a strong windstorm sweeps through a suburban neighborhood. A large tree, weakened by years of drought and climate change, crashes onto a power line. In today's grid, this scenario happens frequently.
Thousands of homes go dark. Emergency operators receive alerts, but it takes precious minutes to piece together what happened. Dispatching crews take even longer because they are already stretched thin, responding to other storm damage. The crew arrives an hour later, identifies the fault, cuts down the tree with chainsaws, repairs the damage, and finally restores power, resulting in a total outage time of 4 to 6 hours.
What is the total cost to customers? During that time, there may be concerns over heating or cooling living spaces, spoiled food in refrigerators, missed work the next morning due to inability to access resources or attend meetings, and other disruptions to daily routines. This is typically when, for me and others, we start thinking about installing solar panels and batteries to reduce our dependence on the utility.
Now picture the same scenario with an AGI-powered grid.
The response is instant and nearly invisible. The line sensors detect a sudden voltage drop within milliseconds. The AI that acts as the grid's brain receives the alert and processes it faster than a human could notice the alarm. In less than 50 milliseconds, AGI isolates the faulty section using automated sectionalizing switches, preventing the outage from spreading to adjacent neighborhoods. In less than 200 milliseconds (still faster than a human blink), autonomous switching stations reconfigure network paths, rerouting power from alternative feeders. The nearby hospital and senior living facility experience no disruption. Their service continues uninterrupted through alternate routes.
Of the approximately 2,000 homes originally on that feeder, only 43 are directly connected to the faulted section and lose power. The rest continue to receive electricity through the intelligently reconfigured network. Those 43 homes experience a ¼ second flicker that is barely noticeable and not enough to reset clocks.
Within 12 seconds, an alert is sent to the utility's drone fleet. A quadcopter assigned to this region receives coordinates and automatically launches from its charging station at a nearby substation. The drone arrives on scene in less than 2 minutes, with its AI navigation system adjusting for wind gusts that would challenge even experienced human pilots. Guided by computer vision and thermal cameras, it locates the downed line in complete darkness.
By the time most residents finish calling the utility to report an outage in the old system, the new grid has already diagnosed the problem, contained the damage, rerouted power to restore service to ninety-eight percent of affected customers, and dispatched the necessary resources for permanent repairs.
A maintenance robot arrives at 4 AM once the winds die down, equipped with cutting tools and manipulator arms. It removes the tree branch and inspects the conductor for damage, all while human crews sleep safely at home. By morning rush hour, those last 43 homes are powered again, and most residents never noticed anything beyond a brief flicker.
This isn't science fiction. This is the inevitable evolution of power infrastructure, and the building blocks for this are available today. It is the shift from a grid that simply moves electrons along fixed paths to one that thinks, learns, adapts, and heals itself. The utility industry is at a similar turning point as computing was in 1975 with the rise of the personal computer, telecommunications in 1990 when the internet was commercialized, or transportation today, as electric and autonomous vehicles transform mobility.
This paper traces the evolution from today's reactive grid to tomorrow's intelligent organism. We will explore what these technologies really are beneath the buzzwords, why they are urgently needed now rather than in the distant future, the revolutionary opportunities they offer across all aspects of grid operation, the major challenges that must be addressed with practical mitigation strategies, what the future realistically looks like at different points in time, and most importantly, how to reach that future with specific, actionable steps utilities can implement immediately.
The transformation takes 50 years, but the choices that decide success or failure must be made now. The opportunity to lead instead of follow is limited and closing.
The infrastructure cliff: The North American power grid was not designed for 2025, and it definitely isn't built for 2050. Every major component of the power system, including transmission towers, overhead wires, underground cables, power transformers, and HV substations, reflects the needs and capabilities of the mid-20th century. That wouldn't necessarily be a problem if those needs and capabilities had remained the same. But they haven't. Three interconnected forces have created an urgent crisis that requires immediate action rather than slow, gradual change.
Walk through any utility's service area and you'll see the issue firsthand. Seventy percent of transmission lines have been carrying power for more than 25 years. Many of the main transmission lines are nearing 40-50 years of service. In the United States, transformers installed during Kennedy's presidency are still operating in substations, with their insulation gradually deteriorating, cooling systems wearing out, and internal components stressed by decades of thermal cycling.
By 2030, transformers over 40 years old may start to fail in large numbers. Utilities call this the "infrastructure cliff," and it's approaching fast. This is similar to our current climate crisis, where efforts are underway to limit global warming to 1.5 degrees Celsius by 2030.
Traditional maintenance cannot scale to meet this challenge. Manual inspections, which have been the backbone of utility asset management for generations, miss 30-40% of critical faults because human observers cannot detect the subtle signs of early failure. A transformer might sound fine to a technician during a quarterly inspection, but it has internal partial discharge that sensors and AI would quickly identify. A transmission tower might look stable, but could suffer from foundation degradation that is invisible from ground level, which a drone with ground-penetrating radar would detect instantly.
Meanwhile, utilities face a shortage of skilled workers as baby boomers retire. The average age of a utility lineworker is 52. Within five years, 40% of the experienced workforce will be eligible for retirement, taking with them decades of institutional and tribal knowledge. Younger workers, who grew up in the digital age, often find traditional utility jobs, such as climbing poles in extreme weather, working near high-voltage lines, and doing manual labor in remote locations, less appealing compared to careers in technology companies or other industries that offer similar wages but better working conditions.
Utilities are already implementing proactive sensor technology to detect issues, such as arcing wires or overheating transformers, before failures happen [6][7]. These sensors produce large amounts of data, including terabytes (TB) of readings from temperature, acoustic, and vibration sensors, as well as other devices. However, utilities are overwhelmed by the data and lack meaningful insights. One substation might have hundreds of sensors updating every second. When you multiply that across thousands of substations, you get millions of data points every day. Human operators cannot possibly analyze all of that in real time.
The utility industry faces $2 trillion in deferred maintenance across North America alone. We can't afford to replace everything, but we also can't risk catastrophic failures. The only way forward is to use AI to maximize the lifespan of existing equipment to prevent failures that could lead to billion-dollar disasters. This involves replacing components precisely when analytics predict an imminent failure, rather than on arbitrary schedules that waste money by replacing working equipment or, worse, waiting until after a failure when the damage is more severe.
By 2035, 60% of electricity generation is expected to come from intermittent renewable sources. Solar and wind introduce variability that's 1,000 times greater than that of coal and nuclear plants. Those legacy generators were dispatchable and predictable. Operators could call a coal plant and say, "Give me 500 MW," and receive exactly that, consistently, for days or weeks at a time.
Solar and wind don't operate in that way. Solar output varies greatly as clouds pass overhead. An extensive weather system can reduce solar energy production by 30% in just 15 minutes. Wind output varies with weather conditions, atmospheric pressure, and other factors, making accurate prediction difficult. The well-known "duck curve," which shows a steep evening increase when solar production drops sharply just as dinner-time demand rises, creates operational challenges that human operators find hard to manage manually every day in high-solar regions like California.
Grid operators must now balance supply and demand within milliseconds, instead of hours. The tolerance for imbalance has always been strict, requiring that frequency stay within a fraction of a hertz of 60 cycles per second and that voltage remain within a few percent of its nominal value. Still, with traditional generators, operators had time to think and react because of their inherent inertia from spinning, rotating masses. With renewables, conditions change faster than humans can process information and give commands.
Machine learning models are already used to predict solar and wind output, assisting grid operators in balancing supply and managing renewable energy more efficiently [13]. However, forecasting alone isn't enough.
The grid must coordinate millions of distributed resources in real time, and that's where human capacity reaches its limit. When should home batteries be charged versus discharged? When should industrial loads reduce production to ease grid stress? When should electric vehicles stop charging to free up capacity? When should smart thermostats adjust temperatures by a degree or two across thousands of buildings to shave peak demand?
These aren't decisions humans can make quickly enough or at the necessary scale. One control center might need to monitor 100,000 data points, updating every second. Multiply that across the thousands of control centers managing the North American grid, and you get billions of decisions each day. Human operators can handle dozens or even hundreds of decisions per shift. They cannot manage billions. The cognitive load is too great.
As well, reverse power flows introduce complexity that challenges the equipment that was designed and implemented decades ago. When rooftop solar in a neighborhood generates more electricity than the homes nearby need, power flows backward from the distribution system into the transmission system. Protection schemes designed under the assumption that power always moves in one direction may not operate correctly. Voltage-regulation devices built for one-way flow struggle with two-way operation. As a result, power transformers are subjected to thermal stresses they were not designed to handle.
Without AGI, the renewable energy revolution reaches a limit of around 40-50% penetration, and we fail to achieve climate goals. With AGI, I believe that 90% or higher becomes possible.
Extreme weather events have risen by 400% since 2000, and climate models forecast a 600% increase by 2040. Manual emergency response, already strained during major incidents, will be unable to keep pace under this growing pressure.
The issue isn't just frequency but unpredictability. Traditional grid planning depended on past patterns and trends. Engineers would examine the worst storm in the last 50 years and prepare for that. But climate change has disrupted that approach. The "hundred-year storm" now happens every 3-5 years. Heat waves surpass anything recorded in history. Wildfires behave in ways that seasoned veterans with 40 years of experience have never seen.
Detecting and fixing wire faults early can prevent catastrophic failures and resulting widespread fires or outages [10]. However, utilities currently inspect power lines at best once a year, or only after failures occur. That's too slow when climate risks change from season to season. A vegetation management program based on past growth rates fails when drought stress causes tree limbs to weaken unpredictably. A flood protection scheme designed for historical rainfall amounts is inadequate when atmospheric rivers bring record precipitation.
If you consider wildfires specifically, they have become a crisis, transformed from occasional tragedies into annual catastrophes across the western United States. Power lines cause over 10,000 fires each year in America. Each spark has the potential to turn into a wildfire that affects human lives, destroys thousands of structures, and costs billions in damages and liability. Our traditional approach involves manually de-energizing entire regions during high fire risk periods, leading to significant economic disruption, businesses losing revenue, hospitals operating on backup generators for long periods, and the potential failure of life-sustaining home medical equipment.
Without autonomous resilience operating at machine speed, we risk cascading failures that could impact millions amid the growing megadisasters driven by climate change. Hurricane recovery, which traditionally takes weeks for damage assessment and repairs, might be shortened to days with robot-assisted operations. However, only by developing these capabilities now, before the next catastrophic event exposes our vulnerabilities, costing lives and devastating communities, can we effectively prepare.
Today's grid manages thousands of generation sources and millions of endpoints, already pushing the limits of human coordination.
By 2050, distributed energy resources are projected to exceed 500 million nodes. Every home with solar becomes a small power plant. Every electric vehicle turns into a mobile storage unit. Every smart thermostat functions as a load-balancing resource. Every industrial facility with backup generation becomes a potential asset for the grid. Every battery, building automation system, and controllable appliance is a node that requires monitoring and possibly coordination. This will result in a surge in the number of possible system states. The number of potential configurations will surpass the number of atoms in the observable universe.
Coordinating these efficiently requires solving problems that are mathematically impossible for human operators and even for traditional computing methods operating at human timescales. Only AI running at quantum speed, with algorithms specifically designed for this kind of massive optimization, can manage this complexity.
Furthermore, these resources require not just monitoring but active coordination. During a supply shortage, which specific electric vehicles should be prioritized for charging? Which specific smart thermostats should be adjusted by 1 degree? Which individual battery systems should discharge? These decisions must balance grid reliability with customer preferences, economic signals with fairness, and environmental objectives with operational limits, all of which adapt in real-time as conditions change. No human-operated system can scale to coordinate hundreds of millions of intelligent agents negotiating their behavior dynamically.
Traditional utility operation centers, with a few dozen people monitoring wall displays and issuing manual commands, were effective when the grid consisted of a few hundred generating plants and passive loads. However, they cannot possibly manage a future with distributed intelligence at every endpoint.
Grid failures cost the U.S. economy $150 billion every year in lost productivity, spoiled inventory, damaged equipment, and emergency responses.
By 2040, without transformation, economists project that this will reach $500 billion annually as digitalization makes power quality increasingly critical. Data centers processing artificial intelligence workloads, hospitals performing robotic surgery, manufacturing facilities with precision automation, and telecommunications networks enabling modern life all demand 99.999% reliability, often referred to as "five nines." That's fewer than five minutes of downtime per year. Traditional grid infrastructure delivers two to four hours of downtime annually, missing the 99.999 target by a wide margin.
Meanwhile, utilities face $2 trillion in deferred maintenance on infrastructure built for fifty-year lifespans that are now entering their seventh or eighth decade. They operate under rate-of-return regulation, which limits spending based on what regulators consider prudent and what ratepayers can afford, and the gap between infrastructure needs and economic constraints widens each year.
The only way to achieve affordable reliability is through radical efficiency, which means doing much more with much less and getting the most value from existing assets, rather than replacing them, by smart management.
The window to take on a leadership role is closing fast. Utilities that start AGI and EEI pilots now will lead by 2035, setting standards, building expertise, and gaining early advantages that increase over time. Those who wait, hoping someone else will figure it out first or for regulators to push modernization before it becomes urgent, will face a tough decision.
They will face regulatory mandates driven by crises rather than strategic planning - rules created in panic after major failures that may be poorly designed and costly to enforce. They are already noticing shifts in customer preferences toward community microgrids and solar-plus-storage systems, as some customers choose to opt out of an unreliable grid. They'll have trouble attracting top talent, as the best engineers and technicians prefer working for innovative utilities that utilize advanced technology instead of maintaining outdated systems.
This is no longer optional; it's a matter of survival. The grid can't keep operating as it has for another fifty or even twenty years. Physics, economics, and climate all demand change. The only question is whether individual utilities will lead that change through careful planning and strategic investment, or if they'll be caught unprepared, struggling to keep up while losing money and credibility.
Before we can determine the way forward, we need to clearly define what Active Grid Intelligence (AGI) and Embodied Energy Intelligence (EEI) truly mean. These are not vague buzzwords or abstract concepts. They are specific technical capabilities with defined structures and measurable outcomes.
Active Grid Intelligence involves deploying advanced artificial intelligence and machine learning across the power system, making it dynamic, adaptive, and capable of real-time self-optimization. An AGI-enabled grid becomes a responsive, two-way network where intelligence is integrated at every level, from individual devices to continental-scale coordination. Basically, the grid acquires a brain with executive functions that coordinate millions of connected neurons.
AI algorithms constantly process streams of data from smart meters reporting consumption patterns, sensors monitoring equipment health, weather reports predicting energy generation and demand, market signals indicating price conditions, and other sources. They analyze this information to make real-time decisions that control grid operations. Instead of waiting for operator commands after human analysis, an active and intelligent grid can reroute power around congestion, isolate failing components before they cause widespread outages, and continually rebalance supply and demand as conditions change.
An analogy for this is the difference between a thermostat and a smart home climate system. The thermostat is passive; you set a temperature, and it responds when the readings drift from the set point. A smart thermostat is active; it learns your preferences, predicts your schedule, monitors weather forecasts, optimizes for efficiency, and coordinates with other building systems to ensure comfort at the lowest cost. AGI transforms the grid from a passive thermostat into a smart thermostat system, but at the grid level rather than just at the edge.
Active Grid Intelligence functions across three integrated layers, each covering different timescales and decision-making scopes.
Edge AI controllers: Deployed at substations and critical nodes throughout the distribution and transmission network, they can run real-time inference on specialized hardware like NVIDIA Jetson AGX processors. These processors deliver 32 trillion operations per second of AI performance while consuming only 10 to 20 watts of power. These edge devices execute low-latency decisions, including detecting faults and isolating them, regulating voltage as loads fluctuate, managing power flows through distribution feeders, and coordinating local resources such as batteries and solar inverters. ETAP Microgrid Controllers (MGC), Intelligent Load Shedding (ILS), and Power Plant Controllers (PPC) are examples of such devices that are already deployed as part of Software-Defined Power technology. These edge devices communicate via industrial protocols that have been developed specifically for grid applications. IEC 61850 handles substation automation with precise timing requirements. IEEE 2030.5 manages demand response and distributed energy resources. DNP3 connects SCADA telemetry systems.
Why adopt this architecture? The key advantage of edge computing is eliminating dependence on cloud connectivity for time-critical operations. When a fault occurs, the edge controller must respond within 100 milliseconds to prevent cascading failures. Sending data to a distant cloud server, processing it, and returning commands takes seconds, which is far too slow for grid protection. Edge controllers place intelligence exactly where they're needed, with sufficient computing power to run sophisticated machine learning and rules-based models locally, while remaining rugged and straightforward enough to operate reliably for decades in harsh substation environments.
Regional AI orchestrators: Above the edge layer, orchestrators coordinate thousands of edge nodes across utility service territories. These systems run on regional data centers equipped with substantial GPU clusters, typically 8 to 16 NVIDIA A100 or H100 GPUs, which provide the computational horsepower for more complex optimization. They can perform minute-to-minute management of power flows across interconnected transmission and distribution networks, renewable generation forecasting with horizons ranging from 15 minutes to 72 hours, economic dispatch coordination across hundreds of generating units, and voltage and frequency regulation at the system level.
Communication occurs via 5G/6G mesh networks, which provide the bandwidth and low latency necessary for real-time coordination, with satellite backup ensuring resilience in the event of terrestrial network failures during disasters. These regional systems make tactical decisions, such as how to respond to a generator tripping offline, how to manage the duck curve as solar ramps down at sunset, how to optimize power flows to minimize transmission losses, and where to position mobile generation and storage resources before an approaching storm.
Cloud-Based training systems: At the top of the architecture, cloud-based training systems provide continuous improvement to the entire system. These centralized platforms, such as AVEVA Connect, process petabytes of historical data from multiple utilities through federated learning approaches that preserve individual utility privacy while enabling collective intelligence. They will identify patterns invisible to human analysis, such as subtle sensor signatures that appear 90 days before transformer failure, correlations between weather conditions and fault rates, optimal control strategies for different grid topologies and load patterns. Training for this purpose would occur on massive GPU clusters in hyperscale data centers, but the resulting models compress down to run efficiently on edge hardware. This follows the pattern of modern AI systems: expensive training in the cloud and inexpensive inference at the edge. A model might take 1,000 GPU-hours to train once, then run on edge devices that consume just 10 watts continuously for years. The cloud systems represent the evolutionary process that makes the entire organism smarter over time, learning from every event across every utility and propagating improvements back to the edge.
EEI is the integration of AI into physical robotic systems to physically perform essential tasks. If AGI gives the grid a brain, EEI gives it hands, eyes, and feet through robots and drones.
These embodied agents conduct inspections, detect problems that human eyes miss, perform maintenance, prevent failures, and apply repairs in conditions too dangerous for humans. They're the automated actuators of the intelligent grid [2]. EEI encompasses a remarkable range of robotic solutions that are expected to evolve through distinct generations over the next fifty years, each building on the capabilities of the previous generation while introducing genuinely new functionalities.
The robots being deployed today and throughout the rest of this decade will operate primarily under human supervision, performing tasks autonomously but within carefully bounded operational envelopes and with humans ultimately responsible for the outcomes.
Line-crawling robots will traverse live high-voltage transmission lines and use diverse sensors to detect faults invisible to human inspectors. These robots could employ LiDAR for precise distance measurement, thermal cameras to reveal hot spots indicating poor connections or excessive resistance, visual cameras to capture high-resolution imagery, and even X-ray systems to inspect inside conductors and detect internal strand breaks. China's Crownpower Technology Ltd. has deployed robots that move at several meters per minute while scanning for damage, taking over the potentially deadly work of high-voltage line inspection [8][9]. Human lineworkers would no longer risk electrocution or falls from great heights for routine inspections. The robots would handle that exposure while workers analyze the data from safe control rooms or nearby bucket trucks.
Quadruped inspection robots will patrol power plants and substations with remarkable agility. They may be able to read gauges with computer vision at near-perfect accuracy, listen for anomalies with sensitive acoustic sensors that detect bearing wear or arcing, identify thermal hotspots indicating equipment stress, and even detect gas leaks with chemical sensors. A recent study has demonstrated that the use of quadruped robots, wheeled robots, and uncrewed aerial vehicles for autonomous inspection and maintenance significantly improves station safety, reduces personnel risks, and enhances fault detection and handling capabilities [5]. Rather than technicians spending hours walking through substations in extreme heat or cold to manually check equipment, robots would perform these monotonous but critical tasks tirelessly. At the same time, humans would be freed to focus on analysis and decision-making. Robot charging stations would be installed at each substation. As science fiction often does, we have been offered a preview of this capability in the Seven of Nine character in Star Trek.
Inspection drones equipped with advanced sensor packages would scan transmission corridors from the air, covering distances in hours that would take human crews days or weeks to inspect on foot or by helicopter. These inspection drones would stay operational indefinitely, responsible for their line sections, charging wirelessly from the overhead lines or charging stations installed at nearby substations. Electric companies have begun using drones to patrol transmission lines and identify damage, thereby dramatically reducing the need for manual line walks or expensive helicopter flyovers [3]. Fixed-wing drones achieve 2 hours of endurance and 50 km of range, efficiently mapping long transmission corridors. Multirotor drones would enable close-up inspections of specific components, hovering steadily in winds that would buffet human pilots, capturing 4K thermal imagery that reveals hot spots and LiDAR mapping of the three-dimensional structure of towers and conductors to detect displacement or corrosion.
During this first generation, humans remain firmly in the loop for any physical interventions. A robot detecting a problem flags it for human review. Only after human approval does the robot take corrective action, if it has that capability at all. Many early robots are purely inspection platforms to find problems and report them, and humans still perform repairs. This graduated approach builds trust among workers, regulators, and the public. It allows operators to verify that robot recommendations make sense before granting increasing autonomy. It provides time to refine algorithms, improve sensors, and discover edge cases where robots might struggle.
The second generation of embodied intelligence would operate autonomously for routine tasks but maintain close collaboration with humans for complex problems. The balance shifts in favor of robots, which handle approximately 60-70% of tasks independently, with humans engaged for the remainder that require judgment, dexterity beyond current robotic capabilities, or the handling of novel situations outside robot training.
Swarm coordination will emerge as a key capability in this generation. Rather than individual drones operating in isolation, fleets of 100+ aircraft will map transmission corridors in parallel, completing in hours what would have taken human crews weeks or months. The drones would have to communicate via mesh networks, autonomously dividing territory to maximize coverage, avoiding collisions through cooperative algorithms, and sharing discoveries in real time. When one drone identifies damage, others will converge automatically to provide multiple angles for 3D reconstruction, creating detailed models that enable human engineers to assess repairs without requiring site visits.
Manipulation robots with sophisticated multi-jointed arms will perform hot-line repairs with millimeter precision while equipment remains energized. Seven-degree-of-freedom robotic arms, capable of matching the mobility of human arms and shoulders, will replace damaged insulators, tighten bolts to precise torque specifications, and splice conductors using tools with mechanical fingers. Advanced haptic feedback systems allow remote operators to "feel" what the robot touches through force-feedback controllers, enabling delicate work that requires sensing tension, alignment, and fit. Tasks that currently require extensive outages and elaborate safety procedures, such as disconnecting equipment, grounding lines, and hanging protective apparatus, will increasingly be performed with lines remaining live, dramatically reducing customer impact, avoiding arc-flash-related human safety concerns, and utility revenue loss. These substation "caretakers" will represent a new category of permanently stationed robots that will autonomously maintain 10 to 20 substations. These wheeled or tracked platforms will patrol daily along pre-programmed routes refined through machine learning, perform routine switching operations under AI direction, conduct oil sampling to detect equipment degradation, and respond to alarms by immediately inspecting affected equipment and reporting conditions. They will effectively replace the need for 24-hour human staffing at remote facilities, which becomes economically prohibitive as substations proliferate to support the growing number of distributed energy resources and AI Factories.
Underwater and underground specialists will tackle environments that are particularly hazardous or difficult for humans to navigate. Submersible robots will inspect cables crossing rivers and lakes, submarine transmission links connecting islands or offshore wind farms, and cooling water intakes at power plants in zero-visibility conditions using sonar and tactile sensors. Underground robots will be able to crawl through conduit systems, map cable locations, identify degradation before it causes failures, and, in some cases, perform repairs without excavation. These specialized platforms will extend embodied intelligence to domains where humans physically cannot operate safely or efficiently.
Self-charging infrastructure matures in this generation, with robots autonomously returning to wireless inductive charging stations when batteries run low. Solar-powered charging stations at remote locations will enable indefinite autonomous operation without human logistical support. Robots will essentially become self-sufficient organisms within their operational territory, requiring human intervention only for major maintenance or when encountering problems beyond their programmed capabilities.
By mid-century, robots will achieve autonomy that rivals or exceeds that of human workers in their operational domains. They will require human oversight only for strategic decisions, novel situations genuinely outside their experience, and ethical judgments involving trade-offs between competing values.
Nanobot inspectors will emerge, representing a groundbreaking capability in this era. These microscopic robots will enter equipment to detect molecular and atomic-level degradation. They will identify corrosion at grain boundaries in metal structures and polymer chain scission in insulation systems to indicate imminent failure, conductor annealing that reduces mechanical strength, and contamination invisible to any macro-scale sensor. Nanobots deployed in transformer oil will circulate through equipment, reporting conditions from within. Others applied to surfaces will migrate into tiny cracks, mapping internal structures and stress. These will provide early warnings months before macroscopic damage appears, allowing for timely intervention. There is an obvious extension of such technologies into the aerospace industry, enabling even safer travel.
Self-replicating maintenance systems arise as robots manufacture replacement parts on-site through advanced additive manufacturing. Imagine a substation robot has detected a failing component. It doesn't have to wait for supply chain logistics, since it's able to print a replacement from raw materials using high-precision 3D printing and then perform the swap with manipulation arms that have human-level dexterity. As well, it can recycle the old part by breaking it down into basic materials for future printing. This creates a fully closed loop, making substations self-sufficient for routine component replacements, significantly reducing repair times from days or weeks to hours.
Adaptive morphology will enable robots to reconfigure themselves for various tasks by utilizing modular design and programmable matter. For example, a wheeled inspection robot that encounters stairs will deploy legs to lift itself for climbing. The same robot could then activate rotors to fly over obstacles that can't be crossed, then retract them to conserve energy while traveling on the ground. Specialized attachments would be added or removed as needed, such as manipulation arms for repairs, sensing booms to reach difficult areas, or specific tools for specific jobs, much like we see in the television series, “Transformers”. In more advanced systems, programmable matter, which is a highly advanced material, will be able to change shape and properties on command through electric, magnetic, or other stimuli, allowing a single robot to perform many functions by physically reshaping itself.
Emergency construction teams of mobile robots will rapidly build temporary microgrids in disaster zones within hours of arrival. They will be able to deploy portable solar panels and battery containers, run cables to critical facilities like hospitals and emergency shelters, install switching and protection equipment, establish islanded networks that can operate independently of the damaged primary grid, and supply power to support response efforts and keep essential services running. By the time human emergency management teams set up command posts and evaluate needs, the robot construction teams would be able to restore electricity to priority locations.
In the final quarter of the twenty-first century, embodied energy intelligence could surpass the boundary between technology and life, integrating biological components and exhibiting emergent behaviors that challenge our definitions of artificial versus natural, and of machine versus organism.
Biological integration may be the most significant evolutionary development, in which synthetic organisms designed for infrastructure maintenance would combine the adaptability and self-repair capabilities of living beings with targeted functions. Engineered bacteria would eliminate corrosion while releasing protective layers that prevent further decay. Vine-like organisms with conductive features would self-heal damaged cables by growing across breaks, providing temporary electrical continuity within hours and permanent repairs as they mature over days.
Programmable matter would broaden this idea to inorganic systems. Materials would reconfigure themselves based on grid needs and AI commands, and power cables would reroute internally around damage by changing their conductive pathways at the molecular level, transformers would modify their turn ratios by reorganizing their winding structure through programmable magnetic materials, and substations would physically reconfigure their layout by moving components via modular platforms. In short, the infrastructure will adapt its physical form to meet changing electrical requirements, eliminating the need for replacement or significant renovation.
Swarm consciousness would emerge as robot groups demonstrate problem-solving skills that go beyond the sum of their individual programming through emergent distributed cognition. Individual robots with limited intelligence would form superorganisms with collective intelligence rivaling that of human engineering teams. These swarms could then tackle unprecedented challenges using strategies that no single programmer planned. They would begin to explore solution spaces simultaneously, share discoveries instantly, coordinate complex multi-step tasks, and adapt smoothly to changing conditions, blurring the line between coordinated multi-robot operation and unified swarm intelligence, which becomes more philosophical than technical.
Telepresence mergers via brain-computer interfaces would begin to allow human operators to remotely "inhabit" robots with full sensory feedback and natural control. Supervision operators would see through robot eyes with high-resolution, full-spectrum vision. They would feel through robot touch sensors with a sensitivity surpassing that of human fingertips and control robot limbs as naturally as their own bodies via direct neural interfaces that interpret motor intentions. This human-machine integration would be the ultimate combination of human judgment, creativity, and adaptability with robotic strength, precision, and environmental tolerance. Operators would perform maintenance on energized equipment or in extreme environments while remaining physically safe in control rooms, much as we can remotely pilot a submersible today in the ocean's depths.
The energy infrastructure, once it expands into space itself, will witness machine intelligence overseeing operations across this ultimate frontier.
Throughout this evolution, the value of embodied intelligence in energy infrastructure is already emerging. Studies show that robotic systems for autonomous inspection and maintenance significantly improve safety, reduce personnel risk, and enhance operational capabilities [5]. This isn't speculative futurism for future generations, but a proven reality today for Generation 1, with capabilities expanding into those of Generation 2 through pilot projects worldwide. EEI exemplifies what happens when the grid's digital intelligence combines with robotic embodiment. The grid doesn't just think - it moves and acts in the physical world with increasing ability, approaching and eventually surpassing human workers.
AGI and EEI will work together as complementary capabilities. AGI will provide the decision-making intelligence on what needs to be done, where, when, and how. EEI will provide the means to implement those decisions on the power grid equipment itself.
Figure 1.1: Summary of Opportunities
Understanding what's possible and what is desired is important, but little can be achieved without a clear pathway from the present reality to the future goals. Our journey toward Active Grid Intelligence and Embodied Energy Intelligence, from my perspective, will unfold through five distinct phases over fifty years, each building on the achievements of the previous phase while introducing genuinely new capabilities. Success will demand careful sequencing. Attempting to deploy advanced capabilities before establishing foundations invites failure, while moving too slowly allows competitors and circumstances to overtake hesitant utilities.
The convergence of AI and robotics will fundamentally redefine energy infrastructure over the next fifty years. This isn't speculation; instead, it's physics and economics demanding transformation, demonstrated technology proving feasibility, and competitive dynamics ensuring adoption.
This future arrives whether individual utilities and jurisdictions actively pursue it, or resist change and hope that someone else will take the risks. But the outcomes differ dramatically based on our choices today.
Leaders who commit to transformation now will capture multi-decade advantages compounding over time. They develop expertise that becomes institutional knowledge and a competitive moat. They shape industry standards that recognize and value their approaches and architectures. They attract top talent excited to work on cutting-edge technology rather than maintain legacy systems. They build relationships with technology providers, becoming preferred partners for new capabilities. They demonstrate results that influence regulators toward frameworks that enable rather than constrain innovation. They achieve cost structures and reliability levels that make them preferred service providers as customer choice expands. They become the utilities that other utilities visit to learn what's possible.
The window to lead narrows daily. Every utility that hesitates while competitors advance falls further behind. Network effects and increasing returns to scale mean early movers capture advantages that become insurmountable. The power sector stands at an inflection point, where decisions made from 2025 through 2030 determine the winners and losers from 2035 through 2055.
The ultimate stakes are whether humanity's energy future is plentiful or limited, whether climate change speeds up or slows down, and whether civilization grows or shrinks under environmental limits. Without smart infrastructure, clean energy remains at 40-50% renewables, and we miss climate goals as energy shortages hinder progress.
When AGI/EEI is deployed thoughtfully, a drastically different future becomes possible: 95% renewable energy with complete reliability, atmospheric carbon capture reversing climate change, near-zero energy costs, universal access, and abundant power that solves water, food, and development challenges. Active Grid Intelligence and Embodied Energy Intelligence mark humanity's boldest infrastructure overhaul since the creation of the original grid. Our generation's opportunity and responsibility is to evolve that system for the next century.
When done right, this creates a future where energy is plentiful, clean, reliable, and nearly free, powering solutions to almost every challenge humanity faces. The time to build that future is today. The Smart Grid 5.0 isn't coming; it's already being created right now in pilot projects, research labs, and the minds of innovative engineers. You have a choice: lead this transformation - by shaping intelligent infrastructure and capturing significant value - or wait as competitors define the future you will need to adapt to under pressure.
The choice seems pretty obvious to me. The intelligent grid awaits. Let's build it.
[6] [7] [10] AI and Sensors Are Transforming Electric Grid Maintenance | StateTech Magazine
[2] Potential Implementation of Embodied Intelligence Technology in the Power Grid: A Review | SpringerLink
[3] [13] [14] [15] The Role of AI in Smart Grids: Transforming Energy Distribution | Tribe AI
[5] Embodied Intelligence Robotics Technology for Safety Operation and Maintenance in Large-Scale Energy Storage Stations | SpringerLink
[8] [9] How a Chinese robotics firm maintains the country's high-voltage power lines | South China Morning Post
[11] [12] How AI Can Predict and Prevent Power Grid Failures
The U.S. Power Grids Need AI Disclosure Requirements | RAND
Artificial Intelligence – Exploring its use in grid modernization | California ISO
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AI-driven fault detection and predictive maintenance in electrical power systems
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CEO, ETAP
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Tanuj began his career with ETAP over 21 years ago, and his contribution has proven instrumental in achieving and advancing ETAP’s growth strategy goals. In his previous role as Chief Technology Officer and Global VP of Business Development, Tanuj successfully orchestrated and managed teams, driving ETAP as the leading solution for power system design and operation.