AI Agentic Workflow for Battery State-of-Charge (SoC) Optimization

The core financial driver. By ingesting real-time and day-ahead price forecasts from ISOs (CAISO, PJM, ERCOT), the workflow's optimization agent executes charge/discharge cycles to capture spread between low and high price periods. This automates a high-frequency trading decision that is impossible to manage manually at scale, directly converting price volatility into merchant revenue.

Automation prevents value-destroying operational patterns. A dedicated degradation modeling agent evaluates the long-term cost of each cycle based on depth-of-discharge, temperature, and C-rate. The orchestrator integrates this cost into the real-time dispatch objective function, avoiding cycles that offer marginal revenue but high degradation cost. This extends usable battery life and improves total cost of ownership (TCO).

The workflow replaces constant human oversight of SCADA screens, price feeds, and spreadsheet models. Orchestration agents autonomously handle data ingestion from BMS, weather APIs, and market feeds, execute optimized dispatch signals, and log all actions. This frees skilled operators for exception management and strategic analysis, reducing direct labor costs and operational risk from human error.

Automation unlocks participation in high-value, fast-response markets. The workflow includes a market eligibility agent that continuously assesses the battery's available capacity and response capability against ISO requirements for frequency regulation or spinning reserves. When profitable, it automatically submits qualified bids and responds to AGC signals, creating a diversified, higher-margin revenue stream.

Automated guardrails and audit trails reduce financial and regulatory exposure. Control agents enforce safe operating envelopes (voltage, temperature), while a compliance agent tracks and logs all market submissions and dispatches for regulatory audit. Exception events are automatically routed to human operators via ticketing systems like ServiceNow, ensuring oversight without constant monitoring.

The architecture is designed for multi-asset control. A fleet orchestrator agent can coordinate dispatch across a portfolio of geographically dispersed batteries, optimizing for portfolio-level constraints (e.g., aggregate interconnection limits) and diversifying market participation. This turns a site-by-site operational model into a centralized, high-leverage control center, dramatically improving margins at scale.

This agent is responsible for the continuous, fault-tolerant collection of real-time and day-ahead price feeds (e.g., from CAISO, PJM APIs), grid operator dispatch signals, and ancillary service market postings. It validates data quality, handles API failures with retry logic, and normalizes disparate data formats into a unified event stream for downstream agents. Without this robust ingestion layer, the entire optimization system operates on stale or incomplete information.

This agent consumes weather forecasts, historical price patterns, and scheduled transmission outages to generate probabilistic scenarios for energy prices and local grid congestion over the next 24-72 hours. It runs ensemble models and outputs a range of likely futures, which are packaged for the optimization engine. This moves decision-making from reactive to predictive, identifying arbitrage windows before they occur.

The core decision agent. It ingests price scenarios and the battery's current State-of-Health (SoH) from the BMS. Using a reinforcement learning or model predictive control (MPC) policy, it solves for the charge/discharge schedule that maximizes expected revenue minus the long-term degradation cost of each cycle. This agent outputs a high-confidence dispatch signal, balancing immediate profit with asset preservation.

This low-level control agent translates optimal dispatch signals into specific setpoints for the Battery Management System (BMS), respecting real-time thermal, voltage, and current limits. It acts as a final safety gate, overriding economically optimal commands that would push the battery outside its safe operating envelope. It also streams telemetry (temperature, cell voltages) back into the observability layer.

This orchestration agent monitors the entire workflow for anomalies: a failed price feed, a sudden BMS alarm, or an optimization result with unusually high risk. It classifies exceptions and routes them to the appropriate human operator queue in tools like ServiceNow or PagerDuty, with full context, while placing the system in a safe, holding state. It ensures automation doesn't mean abdication of oversight.

This agent builds the business case for the workflow. It continuously tracks key metrics: revenue captured per cycle, degradation cost incurred, forecast accuracy, and system uptime. It generates daily attribution reports, showing how much value was created by the automated decisions versus a baseline strategy. This data is critical for financial validation, model retraining triggers, and stakeholder reporting.

The workflow's control loop requires real-time read/write access to the BMS API for state-of-charge, temperature, voltage, and health metrics. A dedicated ingestion agent polls this data, while a command agent issues validated charge/discharge setpoints. Integration must handle latency, authentication (e.g., OPC UA, Modbus TCP), and fail-safe logic to prevent commands during fault states. This direct hardware integration is non-negotiable for closed-loop control.

Revenue depends on ingesting high-frequency price feeds (e.g., day-ahead, real-time LMPs from CAISO, PJM) and ancillary service signals. A market data agent manages authentication, polling, and normalization across disparate ISO APIs, feeding a unified stream to the optimization engine. The architecture must include robust error handling, data validation, and reconciliation logic to ensure trading decisions are based on reliable, timely data.

For portfolio management and financial controls, the workflow must push settlement data to Energy Trading and Risk Management (ETRM) systems and log dispatch actions to the ERP. A settlement agent formats and submits trade tickets. Maintenance triggers from degradation models are routed as work orders to the Computerized Maintenance Management System (CMMS). These integrations ensure financial traceability and align automated actions with broader business operations.

Optimization requires forecasts for energy prices, local renewable generation (if co-located), and grid demand. A data fusion agent orchestrates pipelines from weather APIs (e.g., NOAA), satellite nowcasting services, and internal generation forecasts. This agent handles data quality checks, missing value imputation, and timestamp alignment, creating a clean feature set for the reinforcement learning model. Without reliable forecasts, optimization degrades to reactive control.

Every decision, command, and market data point must be logged with a full audit trail. A governance agent streams telemetry to a time-series database (e.g., InfluxDB) and logs rationale to a structured log store. This layer integrates with monitoring dashboards (e.g., Grafana) and alerting systems (e.g., PagerDuty) for performance drift or system failures. It is critical for operational oversight, regulatory compliance, and model retraining.

While the workflow runs autonomously, certain high-stakes actions (e.g., exceeding cycle limits, entering a new market) require human approval. An orchestration agent (built with LangGraph or similar) routes these decisions to a configured approval gate in a platform like ServiceNow or via a dedicated dashboard. The architecture must support synchronous holds and provide context-rich alerts to operators, blending automation with necessary oversight.