The field of engineering is being transformed by the rise of artificial intelligence (AI). From civil engineering to electrical engineering and beyond, AI is enabling engineers to work faster, smarter, and more efficiently on complex projects. One engineering domain seeing rapid AI adoption is transmission and distribution engineering.
Transmission and distribution engineering focuses on moving electricity from power plants across long distances to substations and finally to homes and businesses. Historically, grid planning and management has relied heavily on human expertise, modeled scenarios, and rules of thumb.
But as grids become more complex with the addition of renewable energy sources, electric vehicles, and advanced meter infrastructure, AI is stepping in to optimize grid operations.
AI Leverages Data to Optimize Grids
The electricity grid is essentially a massive, interconnected data network with thousands of sensors and control points. By leveraging grid sensor data and advanced analytics, AI can discover patterns, model complex scenarios, and enable predictive insights for grid operators.
Rather than relying on time-consuming manual scenario analyses, utilities can use AI to immediately determine optimal topology configurations, forecast demand based on weather data, reroute power based on real-time line failures, and much more. For example, Transmission and Distribution Engineering solutions from vendors such as Hitachi ABB use reinforcement learning algorithms to ingest sensor telemetry and suggest actions to stabilize frequency and optimize voltage levels across the grid.
The result is enhanced reliability and resilience at lower operational costs – a grid that maximizes sustainability while minimizing power interruptions. An estimated 80% of all grid outages originate in distribution networks, costing the US upwards of $150 billion (about $460 per person in the US) annually.
By optimizing voltage levels and rerouting power in real-time around equipment failures or storm damage, AI can systematically reduce outage durations. In fact, UK Power Networks estimates a 10-20% reduction in customer minutes lost due to AI-enabled distribution automation and self-healing capabilities.
Lowering Equipment Failures through Predictive Maintenance
In addition to real-time automation of grid operations, utilities are applying AI to predict problems before they even occur. By analyzing sensor and event data across millions of data points, machine learning algorithms can detect anomalous patterns predictive of future failures.
Whether an aging transformer, faulty capacitor bank, or damaged overhead line, AI helps engineers stay one step ahead by flagging assets in need of repair or proactive maintenance. Utilities like Jacksonville Electric Authority (JEA) claim upwards of a 30 percent reduction in distribution equipment failures through the implementation of predictive analytics platforms.
Transmission and distribution networks contain tens of thousands of capital-intensive and mission-critical assets. As these assets degrade over decades of use, identifying the highest risk equipment for proactive maintenance becomes an exercise in finding the infamous “needle in a haystack.”
AI sifts through mountains of historical maintenance data, real-time telemetry, and operational events to accurately assess asset health and failure probability – no haystack required. Engineers receive role-based recommendations on optimum maintenance strategies to minimize costs and extend asset lifetime.
Renewable Integration and Microgrid Management
The global transition to renewable energy introduces a layer of complexity surrounding grid planning and operations. Compared to traditional generation, the output of wind and solar power can fluctuate wildly minute-to-minute.
Without careful coordination of such variable resources, grid instability arises. Here too, AI demonstrates enormous potential to integrate higher concentrations of renewables while maintaining frequency and voltage parameters within acceptable levels.
By aggregating weather forecasts, historians of renewable generation, load demands, and real-time grid conditions, AI algorithms can predict fluctuations in renewable output days or weeks in advance. Grid operators leverage these forecasts to stage other fast-ramping resources like hydroelectric dams and natural gas “peaker” plants to compensate for drops in renewable output.
Running simulations using massive sets of grid data, AI models can also determine optimal locations for renewable assets from a transmission infrastructure perspective. Placement decisions balance voltage support, congestion management, and other critical grid services provided by conventional generation.
At the distribution level, AI facilitates integration and management of distributed energy resources (DERs) like residential solar arrays, community battery storage, and electrical vehicle charging infrastructure. By coordinating clusters of such assets into edge microgrids, AI can dynamically balance local generation and demand.
When the larger grid suffers disruptions from extreme weather or other system contingencies, these AI-orchestrated microgrids can disconnect and continue serving critical customer loads autonomously. Examples include SP Group’s intelligent microgrids across Singapore and Opus One Solutions’ transactive grid platform for networked DERs.
Engineering Design and Simulation
Beyond real-time grid management and predictive analytics, AI also unlocks superior engineering design and simulation capabilities. Algorithmic design optimization helps engineers quickly evaluate millions of design permutations to arrive at the optimal transmission tower prototype or substation layout.
By ingesting scores of historical designs, performance data, regulations, and costs models, AI learns to generate design alternatives that maximize objectives like storm resilience and minimization of right-of-way usage while adhering to constraints.
Reinforcement learning (RL), a type of machine learning based on simulated trial-and-error interactions, is particularly impactful for design applications. For instance, RL can run multiple simulated environments on the cloud to test out new substation configurations under various contingency scenarios faster and more accurately than any human engineer.
The learnings improve the agent’s decision-making algorithms over time. AutoGrid and Uptake demonstrate how RL applied to design and simulation creates higher performing grid infrastructure at lower costs.
As AI capabilities continue advancing, the technology will permeate virtually every domain of transmission and distribution engineering. Cloud computing power and progress in areas like computer vision and natural language processing will drive more intelligent analytics and simulation packages.
Rather than just optimizing operations and infrastructure today, AI will soon enable grids to self-adapt to tomorrow’s challenges like electrification, decentralization, and cybersecurity concerns. Truly autonomous grid operations are on the horizon.
However, industry collaboration remains vital to unlocking AI’s full potential. Utilities and technology vendors must co-develop solutions with consistent data standards, security protocols, and governance frameworks so technologies interoperate smoothly.
They must also nurture an engineering culture focused on digital skill-building while being sensitive towards concerns about AI eroding jobs. Workforce development programs in data science and machine learning will be critical for utilities. With care and strategic vision, the rise of AI in transmission and distribution engineering promises to deliver smarter, cleaner, and more reliable electricity infrastructure for years to come.