Automation Workflow for Solar PV Curtailment vs. Storage Charge Decisions

This workflow automates a high-stakes, high-frequency decision loop for solar asset operators: when local generation exceeds demand, should you curtail (wasting potential revenue) or charge a battery (incurring efficiency losses and future discharge costs)? Manual oversight is impossible at scale. The automation ROI comes from capturing price arbitrage, avoiding negative pricing penalties, and reducing long-term demand charges by strategically shaping load. Implementation requires integrating inverter APIs, battery management systems (BMS), and market data feeds into a deterministic rule engine augmented with ML for price and load forecasting.

The solution architecture is a closed-loop control system deployed on edge servers or cloud VMs, using frameworks like LangGraph for stateful orchestration. It polls telemetry from SCADA or a DERMS, evaluates against live pricing (e.g., from CAISO or ERCOT APIs) and the battery's state-of-charge, then executes via Modbus or IEEE 2030.5 commands. Critical controls include hard safety limits on SOC, operator approval gates for major strategy shifts, and comprehensive observability dashboards logging every decision's rationale for performance auditing and settlement verification.

This workflow automates a critical financial decision: when grid prices turn negative or demand charges loom, should you waste solar energy via curtailment or absorb it into storage? Manual decisions are slow and suboptimal. A custom multi-agent system integrates live price feeds from ISOs, inverter telemetry, battery state-of-charge, and load forecasts. It executes the highest-value action in seconds, capturing arbitrage, avoiding curtailment penalties, and reducing peak demand. The business case is direct: improved NPV for solar-plus-storage assets through automated, granular response to volatile market and grid signals.

Implementation connects forecasting models, a rules-based and ML-augmented decision engine, and direct asset control via APIs like IEEE 2030.5 or Modbus. The Orchestrator Agent manages the workflow, ingesting data from market APIs (e.g., PJM, CAISO) and site SCADA. The Decision Agent evaluates state-of-charge constraints, degradation costs, and price forecasts against a value function. Commands are routed through a safety interlock layer before execution. Observability is built in: all decisions, telemetry, and exceptions are logged for performance analysis and audit, with a human-in-the-loop console for major deviations or strategy overrides.

Phase 1 establishes the core decision engine, integrating with inverter APIs, battery management systems (BMS), and market price feeds via a low-latency orchestrator. This initial build focuses on rule-based logic using state-of-charge, real-time pricing, and a simple demand forecast. The goal is to validate the data pipeline and control signals in a sandbox environment, proving the fundamental economic logic of avoiding negative pricing and peak demand penalties before live dispatch.

Phase 2 enhances the system with ML-augmented forecasting for solar output and facility demand, improving decision accuracy. Concurrently, we implement critical governance controls: human review queues for major deviations, approval gates for strategy overrides, and a full observability layer logging every decision's rationale, price inputs, and asset state. This phase ensures operational safety and auditability before connecting to live assets. The final phase executes a staged rollout, scaling the validated architecture across the portfolio.

This automation workflow directly protects asset economics by autonomously deciding whether to curtail solar PV or charge batteries based on real-time price signals, state-of-charge, and forecasted demand. The operational upside comes from avoiding negative wholesale pricing, reducing peak demand charges, and maximizing the utilization of on-site storage. Implementation requires integration with inverter APIs, battery management systems (BMS), and market data feeds from ISOs or aggregators, orchestrated by a rule-based and ML-augmented decision agent that runs on low-latency telemetry.

Rollout requires sequenced deployment with explicit approval gates for each control action. A human-in-the-loop review queue is mandatory for anomalous conditions, such as a sudden price spike or a BMS fault. The architecture must include comprehensive logging to an immutable audit trail for regulatory compliance (e.g., FERC, NERC) and performance settlement. Observability dashboards should display decision rationale, system state, and financial impact, enabling operators to monitor and tune the agent's logic without compromising system stability during phased implementation.