Automation Workflow for Critical Peak Pricing (CPP) Event Response

CPP events can impose charges 10-50x higher than standard rates. A custom automation workflow executes pre-programmed load shed strategies within minutes of a utility notification, avoiding these exorbitant per-kWh charges. The system quantifies savings per event, directly protecting operating margin and providing a clear, rapid ROI—often paying for the build within a single season of high-price events.

Without automation, responding to a CPP event requires a frantic, error-prone sequence: monitoring utility portals, manually calculating load impact, calling facility managers, and issuing control commands. This workflow automates the entire chain—from API-based event ingestion to direct control of IoT devices and building management systems (BMS)—freeing operations staff for higher-value tasks and eliminating the risk of human delay or error during critical windows.

The system doesn't just react; it learns. Post-event analysis agents automatically compare forecasted vs. actual load shed, asset performance, and cost avoidance. This data feeds back into strategy optimization models, refining which loads to shed and for how long during future events. This creates a continuous improvement loop, increasing the economic value extracted from existing flexible assets (HVAC, pumps, non-critical processes) without new capital expenditure.

Manual responses risk missing event notifications or shedding critical loads. The automated workflow embeds governance: it validates utility signals, checks for conflicting operations, and routes exceptions (e.g., a critical process that cannot be interrupted) for human approval. Every action is logged with full auditability for utility performance verification, protecting against penalties for non-compliance and providing defensible records.

For operators with multiple sites, manual CPP response does not scale. This architecture is designed for portfolio-wide control. A central orchestration layer (using frameworks like LangGraph or Temporal) can manage heterogeneous assets across geographies—ingesting local utility signals, executing site-specific strategies, and aggregating performance reporting. This turns a site-by-site operational headache into a centralized, leverageable capability.

A robust CPP automation workflow is the foundational control layer for participating in higher-value grid service markets like demand response (DR) or frequency regulation. The same asset telemetry, control APIs, and dispatch logic can be extended to bid into automated DR programs or virtual power plants (VPPs), creating new revenue streams. This builds operational leverage by monetizing existing load flexibility.

The workflow's trigger layer. It continuously monitors utility notification channels (APIs, emails, EDI) and grid telemetry for CPP event declarations. An ingestion agent validates the signal's authenticity, parses its parameters (start time, duration, price multiplier), and logs it for audit. Integration with systems like OSIsoft PI or Utility ADMS ensures signals are received with the latency and reliability required for effective response.

Upon event confirmation, a dispatch agent evaluates pre-programmed load-shed strategies against real-time site conditions. It ingests live meter data, IoT device states (HVAC, lighting, process loads), and battery state-of-charge from a BMS or SCADA system. Using a rules engine and light optimization, it selects the highest-confidence, least-disruptive strategy, often modeled in a digital twin first. This phase is where the operational upside is locked in, balancing charge avoidance against production or comfort impact.

The execution layer. Control agents translate the selected strategy into specific, timed commands for heterogeneous assets. This requires secure integration via MQTT, BACnet, or vendor APIs to systems like Siemens Desigo CC, JCI Metasys, or industrial PLCs. The orchestration logic sequences actions (e.g., adjust setpoints, dim lights, pause non-critical compressors) to avoid simultaneous load spikes that could trigger new peaks, and includes handshake protocols for command acknowledgment.

Critical for safety and trust. The workflow continuously monitors for exceptions: a critical load must stay online, a device fails to respond, or site occupancy overrides comfort rules. An exception-handling agent classifies the severity and routes alerts via Slack, Microsoft Teams, or a dedicated dashboard. For major strategy deviations or critical facility overrides (e.g., hospital, data center), the system requires manual approval via a one-click gate before proceeding, ensuring operational control is never fully ceded.

The accountability layer. During and after the event, verification agents collect meter and sub-meter data to calculate actual load reduction versus the baseline. This performance data is formatted into the report required by the utility or DR aggregator for settlement and to avoid penalties. Automated reporting closes the financial loop, providing auditable proof of performance and directly linking the automation to ROI calculation and future strategy tuning.

The learning loop. After settlement, an analysis agent runs, comparing predicted versus actual shed, device performance, and cost avoidance. Using this data, it can retrain forecasting models or suggest adjustments to strategy logic (e.g., 'Start battery discharge 10 minutes earlier'). This feedback is logged in a vector database (e.g., Pinecone, Weaviate) for retrieval-augmented strategy improvement, ensuring the system becomes more effective and economically optimized with each event.

The workflow's trigger layer ingests CPP event notifications via utility APIs (like OpenADR 2.0b), email parsers, or direct ISO/RTO market feeds. It validates event timing, pricing ($/kWh), and performance requirements before activating the response protocol. Integration with platforms like AutoGrid or CPower ensures signal fidelity, while agents monitor for last-minute cancellations or changes.

Pre-event, ML models forecast baseline load and site-specific demand using weather, occupancy, and historical data. An optimization agent evaluates pre-programmed shed strategies—like HVAC setback, lighting reduction, or process pauses—against the forecasted CPP price, calculating the net avoidance value. It selects the highest-value, lowest-risk strategy portfolio, respecting critical load exception lists (e.g., data center cooling, lab equipment).

Execution agents dispatch control signals via the appropriate protocol for each asset: BACnet/IP or Modbus TCP for Building Management Systems (BMS), OpenADR for DRAS devices, MQTT for IoT sensors, or direct PLC/DCS integrations for industrial loads. The system sequences actions to avoid simultaneous large load drops that could cause voltage issues, and it confirms device state changes via telemetry feedback loops.

A real-time monitoring dashboard flags any asset non-response or site-level load deviation from the expected shed curve. For critical facilities (hospitals, semiconductor fabs), the system requires engineer approval before executing major load shifts. Exception queues route anomalies to operations staff via Slack, PagerDuty, or SCADA alarms, with pre-scripted fallback actions to maintain safety and continuity.

Post-event, agents collect interval meter data (via AMI or sub-metering APIs) and calculate actual load reduction against the validated baseline. This performance report is formatted for automatic submission to the utility or DR aggregator for settlement, directly tying automation to revenue assurance. Data is logged for audit trails and fed back into the forecasting model for continuous strategy optimization.

For operators with multiple sites or mixed assets, the CPP workflow integrates with a Distributed Energy Resource Management System (DERMS) or energy management platform (e.g., Schneider Electric EcoStruxure, AutoGrid Flex). This allows coordinated CPP response across a portfolio, balancing site-specific strategies with broader grid service obligations (like frequency regulation) to maximize total portfolio value without conflict.