Automations
This pillar focuses on utility workflows that analyze distributed meter and grid telemetry to rebalance load, isolate faults, and restore service paths autonomously. Content should show how a custom self-healing network architecture improves reliability, lowers outage impact, and combines forecasting, controls, and utility operations in one production-grade system.
This foundational page details the architecture for a custom, multi-agent workflow that continuously analyzes grid telemetry, predicts overloads and faults, and orchestrates autonomous switching and load transfer to maintain stability. It explains how integrating SCADA, DERMS, and OMS systems with agentic logic reduces outage minutes, improves SAIDI/SAIFI metrics, and creates a production-grade self-healing network for utilities.
This page outlines a custom workflow where specialized agents coordinate to pinpoint faults using smart meter pings and PMU data, isolate damaged sections via automated switches, and restore power to unaffected customers by reconfiguring the network. The architecture focuses on reducing manual dispatch and restoration times, directly lowering customer interruption costs and improving reliability indices for distribution operators.
This page covers the automation of real-time load balancing across substations by analyzing feeder loading, transformer ratings, and network topology. The workflow triggers autonomous switching orders to transfer load before thermal limits are breached, preventing equipment damage and costly outages. Implementation integrates with EMS and switching management systems for auditable, operator-approved execution.
This page explains a closed-loop automation workflow that continuously adjusts capacitor banks, voltage regulators, and inverter-based resources to maintain optimal voltage profiles and reduce reactive power losses. By automating this traditionally manual setpoint adjustment, utilities achieve significant energy savings, defer capital upgrades, and improve power quality without increasing operator workload.
This page details a workflow that fuses LiDAR, satellite imagery, and historical outage data to predict vegetation encroachment risk on power lines. The system automatically generates and prioritizes trimming work orders, schedules crews, and can even trigger pre-emptive switching during high-wind events, reducing vegetation-caused outages and supporting wildfire mitigation programs.
This page describes an agentic workflow for campus, military, or critical facility microgrids that detects grid disturbances and autonomously executes a sequenced islanding process. It coordinates disconnection, stabilizes local frequency using DERs and storage, and manages internal load-shedding, ensuring continuous power for essential services and reducing reliance on manual operator intervention during crises.
This page outlines a custom orchestration layer for depots or fleets that schedules EV charging based on real-time grid congestion, dynamic tariffs, and fleet operational needs. The workflow prevents local transformer overloads, minimizes demand charges, and can provide grid services through V2G, turning a potential grid stressor into a manageable asset for both fleet operators and utilities.
This page covers the end-to-end automation of DR programs, from predicting grid shortfalls and selecting optimal customer segments to dispatching curtailment signals, verifying compliance via meter data, and generating settlement reports. The workflow eliminates manual call-downs and data reconciliation, improving program participation, accuracy, and cost-effectiveness for grid operators.
This page details a workflow where AI agents determine the most profitable and grid-supportive dispatch for utility-scale or aggregated batteries. It ingests price signals, renewable forecasts, and grid constraints to autonomously execute bids for energy arbitrage, frequency regulation, or congestion relief, maximizing asset ROI while providing critical grid flexibility.
This page explains a workflow that processes weather sensor data (wind, temperature, solar irradiance) to calculate real-time ampacity of transmission and distribution lines. The system automatically adjusts line loading limits in the EMS/SCADA, safely unlocking hidden grid capacity, deferring rebuilds, and integrating more renewable generation without compromising safety margins.
This page focuses on the automation of a high-risk, manual process: creating and executing switching orders. The workflow uses a digital twin of the network to simulate and validate safe switching sequences, then generates the formal orders and—with operator approval—executes them via remote-controlled switches, drastically reducing reconfiguration time and human error.
This page details an asset management workflow that analyzes DGA (Dissolved Gas Analysis), loading, and temperature data to predict transformer failures weeks or months in advance. It automatically generates maintenance alerts, prioritizes replacements in capital planning systems, and can even recommend load transfers to protect at-risk assets, preventing catastrophic failures and expensive emergency repairs.
This page outlines a workflow for safely and effectively leveraging customer-owned solar and batteries during grid outages. The system identifies available DERs within an isolated section, establishes communication, coordinates their synchronization to form a stable microgrid, and manages load within their capacity, extending resilience without utility-owned infrastructure.
This page covers a workflow that moves beyond substation-level forecasts to predict load at the feeder or transformer level using smart meter data, weather, and event calendars. These granular forecasts automatically trigger pre-emptive load shaping actions (like adjusting CVR setpoints or initiating targeted DR), preventing localized overloads and improving planning accuracy.
This page explains a workflow for transmission or distribution operators that detects emerging congestion through PMU and market data. It automatically evaluates and executes a portfolio of relief actions—including re-dispatching generation, adjusting transformer taps, or triggering DR—to alleviate constraints before they cause price spikes or reliability violations, optimizing grid economics.
This page details the sensitive automation of Public Safety Power Shutoff events. The workflow integrates hyper-local weather (wind, humidity), fire risk indices, and asset health to recommend circuit segments for de-energization. It then orchestrates customer notifications, executes controlled shutdowns, and plans restoration sequences, balancing wildfire mitigation with customer impact.
This page outlines a workflow for restoring power to a blacked-out grid segment without relying on traditional large-scale generation. It identifies and sequences available grid-forming inverters from solar-plus-storage systems, coordinates their startup to establish a stable voltage and frequency island, and then gradually re-energizes the network, enhancing grid resilience against widespread blackouts.
This page covers the automation of a tedious, study-heavy process: evaluating if the grid can host new solar or storage projects. The workflow pulls real-time grid models, simulates the impact of proposed DERs under multiple scenarios, and generates approval/rejection reports with required upgrade recommendations, slashing interconnection study timelines from months to days.
This page details a workflow that implements real-time, location-specific pricing signals. It calculates dynamic tariffs based on local grid congestion and losses, pushes them to advanced meters and customer systems, and automates the settlement of transactive energy transactions between the utility and prosumers, creating a market-driven mechanism for grid optimization.
This page explains a high-speed automation workflow designed to arrest cascading failures on the transmission grid. By analyzing PMU data in real-time, the system predicts propagation paths and automatically executes remedial action schemes (RAS) such as generation tripping or load shedding to isolate the disturbance, preventing large-scale regional blackouts.
This page outlines a workflow that continuously monitors voltage and current waveforms from PQ meters, automatically classifies disturbances (sags, swells, harmonics), identifies the root-cause asset or event, and generates compliance reports or maintenance tickets. This replaces manual engineer review, speeding up response to customer complaints and improving system power quality.
This page details a workflow that applies anomaly detection algorithms to massive streams of smart meter data to identify patterns indicative of energy theft or meter tampering. The system automatically flags high-probability cases, routes them to investigators with supporting evidence, and updates risk scores, improving revenue recovery and reducing manual data sleuthing.
This page covers the automation of post-outage operations: correlating SCADA alarms, meter last-gasp signals, and weather data to determine the most likely cause (e.g., tree, animal, equipment). It then triggers personalized customer notifications via SMS/email with accurate cause and ETR, reducing call center volume and improving customer satisfaction during service incidents.
This page explains a workflow that continuously refines outage restoration times. It ingests crew dispatch status, repair complexity assessments from field images, and parts availability to calculate and update ETRs in the OMS and customer portals automatically. This provides customers with reliable information and manages expectations without constant manual dispatcher input.
This page details a workflow that automates the running of massive N-1 and N-1-1 contingency studies using the latest grid model and real-time operating conditions. It identifies new vulnerabilities, recommends preventive actions (like switching or re-dispatch), and generates compliance reports, ensuring the grid remains reliable under potential equipment failures without daily manual analysis.
This page outlines a preparedness workflow that simulates hurricane, ice storm, or wildfire impacts on the grid using forecast models and digital twins. It predicts likely damage locations and scale, then automatically generates work orders, pre-positions crews and materials, and suggests network pre-configuration to expedite restoration, turning reactive storm response into a proactive operation.
This page describes a workflow for critical facilities like data centers that manages the interaction with the utility grid. It monitors grid stability, predicts outages, and autonomously orchestrates the transition to on-site generation (UPS, generators, batteries) and back, ensuring seamless uptime, optimizing energy costs, and providing grid support services when possible.
This page covers a workflow for large industrial consumers to automate their participation in grid flexibility markets. It continuously evaluates process constraints, energy prices, and grid signals to autonomously shed or shift non-critical loads, providing demand response or frequency regulation without disrupting core operations, creating a new revenue stream.
This page details a self-contained automation workflow for campus microgrids that balances on-site generation (solar, CHP), storage, and load in both grid-connected and islanded modes. It autonomously optimizes for cost or carbon, executes islanding during utility outages, and manages internal energy transactions, ensuring energy security and operational efficiency for the facility.
This page focuses on control room operator support, automating the triage of hundreds of SCADA alarms to highlight the most critical events. It then simulates potential switching actions in a digital twin and recommends the optimal, safe sequence to resolve the issue, reducing operator cognitive load and speeding up decision-making during emergencies.
This page explains a workflow that synchronizes field crew logistics with grid operations. After an automated system isolates a fault and creates a switching plan for restoration, it automatically dispatches the nearest available crew with the correct work order and holds switching commands until the crew confirms they are clear, ensuring both efficiency and absolute crew safety.
This page details a workflow for extreme heat events that predicts transformer and cable overheating risks using load and temperature forecasts. It automatically triggers proactive cooling measures (like activating transformer cooling systems), initiates targeted conservation voltage reduction, and rebalances load to protect assets from thermal degradation and prevent heat-induced outages.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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