On-Site Solar and Storage Dispatch Based on Real-Time Energy Pricing

This workflow automates the economic optimization of distributed energy resources (DERs), turning solar arrays and battery storage from passive assets into active revenue generators. It eliminates the manual analysis and suboptimal dispatch decisions that leave 10-20% of potential savings or revenue on the table. By continuously ingesting real-time pricing from wholesale markets (e.g., CAISO, PJM) and utility tariffs, the system makes split-second decisions to maximize self-consumption, arbitrage price spreads, and participate in demand response, directly improving the ROI of your capital investment.

Implementation requires robust integration with solar inverters (SolarEdge, Enphase), battery management systems (Tesla Powerwall, Fluence), and building management systems via Modbus or proprietary APIs. The core orchestration, built on frameworks like LangGraph, executes a decision engine that factors in battery state of charge, forecasted solar yield, and building load profiles. Critical controls include pre-configured trading limits, automated performance reporting, and manual override gates to ensure operational safety and regulatory compliance within utility interconnection agreements.

This workflow automates the financial arbitrage and operational control of on-site solar PV and battery storage. It eliminates the manual, suboptimal decisions of when to consume, store, or sell energy, directly converting real-time price signals and load forecasts into dispatch commands. The operational upside comes from slashing peak demand charges, increasing renewable self-consumption by 20-40%, and creating a new revenue stream through automated participation in wholesale energy or demand response markets.

Implementation requires integrating with SolarEdge or Enphase inverters, battery BMSs like Tesla Powerwall, and price feeds from utilities or platforms like OhmConnect. The orchestrator, built with LangGraph or a custom state machine, executes a control loop every 5-15 minutes. Critical controls include governor limits on battery cycles, fallback to rule-based mode on API failure, and human-in-the-loop approval gates for high-stakes grid export events. Observability is built into the action router, logging every decision's rationale, price context, and outcome for audit and continuous optimization.

The operational upside comes from converting a static solar asset into a dynamic financial instrument. By automating dispatch decisions—store, consume, or sell—based on real-time locational marginal pricing (LMP) and demand forecasts, you directly reduce peak demand charges, increase renewable self-consumption, and create a new revenue stream from wholesale market participation. This shifts energy management from a passive, scheduled operation to an active, margin-optimizing one, with savings and revenue tied directly to price volatility and grid conditions.

Implementation follows a phased, risk-managed approach. Phase 1 establishes data ingestion from price feeds (e.g., Day-Ahead Market via API), weather services, and the building management system (BMS) for load. Phase 2 deploys the core orchestration logic, likely built with LangGraph, to run optimization models that respect battery cycle life and inverter limits. Phase 3 integrates control via vendor APIs (e.g., SolarEdge, Tesla Powerwall) or a Distributed Energy Resource Management System (DERMS), introducing human-in-the-loop approval gates for wholesale market sales. Each phase is governed by observability dashboards tracking ROI, cycle counts, and exception rates.

This workflow automates the core financial arbitrage of distributed energy resources. It eliminates the manual, reactive monitoring of utility price feeds and building SCADA systems, replacing it with a continuous, algorithmic decision loop. The operational upside comes from capturing high-price export windows, avoiding peak consumption charges, and deferring grid infrastructure upgrades. Savings are direct, measurable, and tied to volatile market conditions that human operators cannot track at scale. Implementation requires integrations with DERMS platforms, inverter APIs (e.g., SolarEdge, Enphase), battery management systems, and wholesale market data feeds like PJM or CAISO.

Governance is non-negotiable. The orchestration layer must include approval gates for strategy changes, real-time dashboards for portfolio managers, and automated rollback procedures if price signals become anomalous. Exception routing handles communication failures with inverters, BMS state-of-health alerts, and manual operator overrides. Rollout is sequenced by asset, starting with non-critical loads, using a digital twin for simulation before live dispatch. The architecture typically combines a time-series database, a rules engine for business logic (e.g., peak shaving thresholds), and LangGraph for managing the multi-step agentic workflow between forecasting, optimization, and command execution.