Automations
This pillar focuses on local energy market workflows that decide when distributed assets should consume, store, sell, or shift energy based on grid conditions and price signals. The content should show how custom microgrid automation improves asset economics, reduces peak-load pressure, and coordinates pricing, settlement, and dispatch decisions.
This foundational page details the custom orchestration of distributed energy resources (DERs) to autonomously trade in local markets and respond to grid signals. It explains the architecture for integrating price feeds, asset telemetry, and control systems to maximize revenue and reduce peak demand costs. Implementation covers multi-agent decision logic, real-time settlement, and the governance required for reliable, auditable operations.
This workflow automates the identification and execution of energy trades across different regional markets or balancing authorities to capture price differentials. It details the agentic architecture for ingesting disparate market data, managing transmission rights, and coordinating settlements, delivering significant margin improvement. The page covers the risk controls, latency requirements, and integration with existing trading and scheduling systems.
This page outlines a custom system for automatically qualifying distributed assets, forecasting regulation signals, and submitting optimized bids into frequency regulation markets. It connects the business case of high-value grid service revenue to the technical architecture for low-latency telemetry, performance scoring, and automated settlement. Implementation details include integration with DERMS, SCADA, and ISO market interfaces.
This workflow automates the economic dispatch of a mixed portfolio including solar, storage, generators, and flexible loads against volatile market prices. It explains the optimization logic that balances degradation costs, fuel prices, and contractual obligations to maximize portfolio NPV. The architecture combines forecasting models, solver engines, and direct asset control via APIs or PLCs, with human override gates for major deviations.
This page details a real-time decision system that autonomously decides whether to curtail excess solar generation or charge on-site batteries based on price, state-of-charge, and forecasted demand. It quantifies the value of avoiding negative pricing and reducing demand charges. The implementation covers the integration of inverter controls, battery management systems, and market data feeds within a rule-based and ML-augmented agent.
This workflow automates the pre-emptive shifting of high-power industrial processes (e.g., compressors, furnaces) to avoid peak demand charges, which can constitute 30-50% of an industrial bill. It details the architecture for integrating process control systems (PLC/DCS), load forecasting, and real-time meter data to trigger safe, profitable load adjustments. The page covers exception handling, operator notifications, and ROI calculation.
This page explains a custom orchestration layer that schedules EV fleet charging based on energy costs, vehicle availability, and grid needs, including discharging for vehicle-to-grid (V2G) revenue. It details the multi-agent system that negotiates between fleet management software, charging hardware APIs, and ISO signals. Implementation covers the safeguards for battery health, driver constraints, and the settlement of grid service payments.
This workflow automates the daily charge/discharge cycles of commercial or industrial battery systems to capitalize on time-of-use rate differentials and wholesale price spikes. It breaks down the forecasting, bidding, and automated control logic required to capture value without manual intervention. The architecture integrates with energy management systems (EMS), market APIs, and the battery's own control system, including performance degradation modeling.
This page details a production-grade system that automatically receives DR event signals from utilities or aggregators, validates them, and executes pre-defined load shed strategies across a portfolio of sites. It covers the reduction in manual coordination labor and the improvement in performance penalties through reliable, automated execution. The architecture includes event ingestion, strategy selection, asset control, and verification reporting back to the DR program operator.
This workflow automates the dynamic adjustment of HVAC setpoints across a building portfolio in response to real-time prices, occupancy, and weather forecasts to reduce energy cost without compromising comfort. It details the integration with building management systems (BMS/BAS), occupancy sensors, and price feeds. The page covers the ML models for thermal inertia prediction, the control logic, and the tenant override protocols.
This page outlines a custom implementation of transactive energy, where distributed assets automatically bid their willingness to consume or generate based on internal value, creating a local market-clearing price. It explains the agent-based architecture for peer-to-peer negotiation or coordination through a central transactive coordinator. Implementation covers the communication protocols, settlement mechanics, and integration with existing DER assets.
This workflow automates the detection and response to utility-critical peak pricing events, triggering pre-programmed load reduction strategies to avoid exorbitant per-kWh charges. It details the system for monitoring utility notifications, forecasting event likelihood, and executing shed strategies across IoT devices and control systems. The page covers the exception routing for critical loads and the post-event analysis for strategy optimization.
This page details a back-office automation system that ingests raw meter data, validates it for anomalies and gaps, and calculates settlements for market transactions or customer billing. It eliminates manual data wrangling and reduces revenue leakage from settlement errors. The architecture combines data ingestion pipelines, validation rules engines, and integration with billing or market settlement systems, with human review queues for exceptions.
This workflow automates the end-to-end management of Renewable Energy Credits, from generation-based creation through tracking registry transactions to final retirement for compliance or reporting. It removes the manual, error-prone spreadsheet processes common in sustainability teams. The system integrates with meter data, registry APIs (like M-RETS or NAR), and internal ESG reporting platforms, ensuring audit-ready traceability.
This page explains an automated system that calculates expected payments for grid services (e.g., demand response, frequency regulation) based on performance telemetry, cross-references them with settlement statements from ISOs, and flags discrepancies for review. It directly addresses revenue assurance for asset operators. The architecture involves ingesting complex market rules, telemetry streams, and settlement files, then using agents to reconcile and report.
This workflow automates the real-time monitoring of grid constraints (like transformer or line loading) and dispatches distributed energy resources to provide relief, avoiding costly grid upgrades. It details the integration of distribution system state estimation (DSSE), DER telemetry, and control systems. The page covers the optimization logic, safety interlocks, and coordination with utility distribution operators for a non-wires alternative solution.
This page outlines a custom system that uses distributed solar inverters and batteries to autonomously regulate voltage and reactive power on the distribution grid, improving efficiency and hosting capacity. It explains the control architecture that integrates with utility VVO systems or operates independently, responding to real-time sensor data. Implementation covers communication standards (IEEE 2030.5), device control, and performance reporting.
This workflow automates the detection of a grid outage, the seamless disconnection from the main grid, and the transition to islanded microgrid operation using local generation and storage. It is critical for resilience-focused sites like hospitals or campuses. The page details the high-speed sensing logic, the sequencing of breaker controls and generator starts, and the stabilization of island frequency/voltage, all with human-in-the-loop confirmation for major events.
This page details a custom workflow that ingests data from smart meters, PMUs, and DERs to maintain a real-time model of the distribution grid's state (voltage, loading). This model then automatically triggers other optimization and control actions. It addresses the 'grid visibility' problem for utilities and microgrid operators. Implementation covers data fusion, model calibration, and downstream integration with DERMS or ADMS platforms.
This workflow automates the generation of highly accurate, site-specific solar power forecasts by fusing satellite imagery, sky cameras, and on-site sensor data. It directly improves trading and dispatch decisions for solar asset owners. The page explains the ML pipeline for data ingestion, model inference, and integration into energy management systems, including uncertainty quantification and automated model retraining.
This system automates the coordination of distributed storage and flexible loads to absorb excess renewable generation locally, minimizing costly curtailment commands from the grid operator. It details the agentic negotiation between asset controllers to find the most economic storage or consumption option. The architecture integrates curtailment signals, asset state, and market data to protect renewable asset revenue.
This page outlines a custom control system that dynamically optimizes the dispatch of a co-located hybrid power plant, deciding moment-to-moment how much solar, wind, and storage to use or sell based on market conditions and physical constraints. It maximizes plant revenue over simple, rule-based operation. The architecture combines forecasting, a multi-period optimization solver, and direct control of plant subsystems.
This workflow automates the selection of the most cost-effective retail electricity tariff for a commercial or industrial customer, analyzing historical load profiles against available utility rate plans. It eliminates manual analysis and can trigger automated switching processes. The system integrates with meter data, utility rate databases, and billing systems, providing ongoing monitoring and re-evaluation as load patterns or rates change.
This page details a custom platform that enables automated P2P energy trading within a community microgrid or virtual power plant. It covers the agent architecture where prosumer agents autonomously set prices, match orders, and settle transactions on a distributed ledger or centralized platform. Implementation focuses on the market clearing logic, settlement integration, and user interface for monitoring and overrides.
This workflow automates the onboarding, commissioning, and ongoing performance management of distributed assets into a Virtual Power Plant portfolio. It reduces the manual labor of contract management, device registration, and telemetry verification. The system integrates with customer CRM, device management platforms, and market participation systems, using agents to handle routine tasks and flag exceptions for human review.
This page details a highly specialized workflow that automates energy cost management for semiconductor fabrication plants, coordinating ultra-pure water systems, HVAC, and process tool load without impacting yield or cleanroom standards. It explains the integration with fab MES and facility systems, using predictive models to shape load against real-time prices. The architecture includes stringent safety gates and engineer approval for any action affecting production.
This workflow automates the scheduling and power modulation of electric arc furnaces—massive, intermittent loads—to avoid demand charges and capitalize on low-price periods. It details the integration with production planning systems and furnace controllers to find feasible load shifts that don't disrupt throughput. The page covers the substantial cost savings potential and the technical architecture for real-time control and exception handling.
This page outlines a custom orchestration system for a university campus microgrid, automatically balancing energy between academic buildings, dormitories, central plants, and on-site generation to minimize cost and carbon footprint. It details the multi-agent architecture that respects academic schedules, research lab priorities, and resilience needs. Implementation covers integration with campus BMS, SCADA, and financial management systems.
This workflow automates the management of hospital backup generators and storage to ensure critical load resilience while also allowing participation in grid demand response programs when safe to do so. It details the complex prioritization logic, testing schedules, and seamless transition protocols. The architecture integrates with hospital electrical systems, generator controllers, and utility signals, with clinical engineering oversight gates.
This page details a utility-facing workflow that automates the procurement and dispatch of distributed energy resources as a non-wires alternative to traditional grid upgrades. It covers the system for identifying grid constraints, running auctions for DER services, contracting, and then automatically dispatching those resources when needed. This reduces utility CapEx and accelerates solution deployment.
This page explains the custom implementation of a DERMS core, where multiple agents orchestrate thousands of distributed assets for grid services like voltage support and peak shaving. It moves beyond vendor black-box solutions to a tailored architecture. The workflow details agent roles for registration, forecasting, optimization, control, and settlement, integrated with utility ADMS and OMS systems.
This workflow automates the analysis of granular load data to model the impact of proposed utility rate changes, providing data-driven support for rate case filings or interventions. It eliminates months of manual data slicing and scenario modeling. The system ingests AMI data, applies proposed rate structures, and generates detailed impact reports by customer class, improving the efficiency and strength of regulatory engagements.
This page details a custom analytics pipeline that processes high-volume AMI data to automatically detect anomalies (like theft or meter faults), segment customers, and extract insights for grid planning and program targeting. It turns raw meter data into operational intelligence without manual analysis. The architecture covers data ingestion, feature engineering, ML model inference, and alert routing to relevant utility departments.
This workflow automates the monitoring of the grid's real-time carbon intensity and shifts flexible loads or dispatches storage to minimize operational carbon footprint. It supports corporate sustainability goals and can interact with carbon markets. The system integrates carbon tracking APIs, asset controls, and logs emissions savings for ESG reporting, providing a measurable automation ROI beyond energy cost.
This page outlines an automated system that collects meter data, matches it with hourly grid emission factors, and calculates Scope 2 emissions for reporting under frameworks like GHG Protocol. It eliminates the error-prone, quarterly manual process. The architecture integrates with ERP sustainability modules, ingests data from utilities and ISOs, and generates audit-ready reports, significantly reducing compliance overhead.
This workflow automates the scheduling of flexible computing workloads (like data center batch jobs or rendering) to run when grid carbon intensity is lowest. It details the integration with job schedulers (e.g., Kubernetes), carbon intensity APIs, and site generation forecasts. The page quantifies the carbon reduction and potential cost savings, covering the architecture for job queue management and runtime constraints.
This page details an automated risk management workflow that continuously calculates the Value-at-Risk for a portfolio of physical and financial energy positions based on live market data and volatility models. It provides traders and portfolio managers with real-time risk visibility instead of end-of-day reports. The system integrates market feeds, position data, and risk models, flagging breaches for immediate review.
This workflow automates the collection, validation, formatting, and submission of data required for mandatory reports to regulators like FERC, NERC, or state PUCs. It drastically reduces the labor and error risk associated with manual compliance. The architecture pulls data from operational and market systems, applies business rules, and generates submission-ready files, with a review and approval layer for legal sign-off.
This page explains a custom surveillance system that uses agents to monitor trading and operational data in real-time for patterns indicative of market manipulation. It helps energy traders and utilities comply with regulations and avoid massive fines. The architecture involves ingesting trade blotters, market data, and physical operations logs, then applying rule-based and anomaly detection models to flag suspicious activity for compliance officers.
This workflow automates the execution of energy trades, from receiving a signal or instruction to routing the order to the optimal exchange or counterparty and confirming fill. It reduces latency and manual errors for trading desks. The page details the integration with internal trading platforms, market APIs, and risk checks, including fail-safes and human intervention triggers for large or unusual orders.
This page details a back-office automation system that ingests trade confirmations from multiple sources (emails, APIs, ETRM systems), reconciles them, and updates a central position book in real-time. It eliminates manual data entry and reduces position errors. The architecture uses LLMs and rules to parse unstructured confirmations, matches them to expected trades, and flags discrepancies for trader review.
This workflow automates the monitoring of operational technology (OT) network traffic and device behavior in microgrids and energy assets to detect and respond to cyber threats. It addresses the critical need for security in grid-edge systems. The system integrates with firewalls, historians, and device logs, using anomaly detection models to trigger alerts and automated containment actions (like segmenting networks) with SOC oversight.
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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