Combined Heat and Power (CHP) and Thermal Storage Integration Automation

This workflow automates the dispatch of capital-intensive energy assets based on real-time greenhouse heat demand and electricity market prices. It eliminates the operational bottleneck of manual, suboptimal setpoint decisions, directly converting forecast accuracy into margin by reducing energy purchase costs and maximizing export revenue. The architecture integrates forecasting agents, CHP controller APIs, and the greenhouse climate computer into a single, closed-loop control system governed by economic logic.

Implementation requires building a multi-agent system that ingests external price feeds and internal sensor telemetry, runs continuous optimization against physical and financial constraints, and executes via secure integrations with industrial controllers. Critical controls include human approval gates for major operational mode changes, comprehensive observability dashboards for asset performance, and fail-safe logic to default to base climate setpoints during system faults.

This workflow automates the critical operational bottleneck of manually balancing on-site power generation with greenhouse heat demand and volatile electricity export prices. It directly converts forecast data into dispatch decisions, capturing 15-25% higher asset utilization and reducing energy costs by optimizing against real-time pricing. Savings stem from avoiding grid import during peaks, maximizing export revenue, and utilizing otherwise-wasted thermal energy, turning a fixed-cost asset into a dynamic profit center integrated with the farm's financial model.

Implementation integrates forecasting agents with the CHP unit's controller API (e.g., via Modbus TCP/RTU or OPC UA) and the thermal storage system's PLC. The orchestrator, built on frameworks like LangGraph, executes setpoint adjustments in the greenhouse climate computer (e.g., Priva, Hoogendoorn) to utilize stored heat. Critical controls include human-in-the-loop approval gates for major mode changes, continuous observability via a central historian, and exception routing for equipment faults or forecast inaccuracies to ensure system resilience and operational trust.

This workflow automates the high-frequency economic decision of when to run the CHP unit for power export versus direct heat use, and when to charge or discharge the thermal storage tank. It eliminates manual setpoint adjustments and spreadsheet-based forecasting, directly targeting the 20-30% of energy costs tied to suboptimal dispatch. Savings come from arbitraging time-of-use electricity rates, reducing peak demand charges, and maximizing the utilization of expensive thermal storage capacity. The system ingests real-time data from greenhouse climate computers, CHP controller APIs, utility price feeds, and weather forecasts.

Implementation integrates with existing SCADA, Building Management Systems (BMS), and CHP controllers like 2G or Tedom via their APIs. A core orchestrator, built with LangGraph or a custom agent framework, sequences forecasting, optimization, and command execution. Critical controls include approval gates for major mode changes, continuous monitoring of asset health telemetry, and a human review queue for optimization exceptions or predicted maintenance flags. Rollout requires phased testing against a digital twin of the energy system before live control, ensuring economic logic performs under real grid and climate volatility.

Successful implementation requires a governance layer that enforces financial and operational guardrails. The orchestration logic must operate within pre-approved price thresholds, greenhouse climate safety bands, and CHP manufacturer operating limits. A human-in-the-loop approval gate is mandatory for any action exceeding these parameters or for initiating a full system shutdown. This ensures the automation delivers economic upside—maximizing electricity export revenue and reducing natural gas purchases—without introducing unacceptable operational risk to the crop or capital assets.

Rollout should follow a phased, test-in-production approach. Phase 1 deploys the forecasting agents and decision logic in a shadow mode, logging recommended actions without executing them, to validate model accuracy against historical data. Phase 2 introduces control for the thermal storage system only, as its misoperation poses lower risk. The final phase enables full, closed-loop control of the CHP unit, with continuous monitoring of key performance indicators like heat utilization rate and marginal profit per operating hour. This staged deployment de-risks the integration with critical energy and climate systems.

This workflow automates the high-frequency operational decisions required to maximize the economic return on capital-intensive CHP and thermal storage assets. It replaces manual setpoint adjustments and spreadsheet-based forecasting, directly targeting the bottleneck of suboptimal asset utilization. The business value comes from increased electricity revenue through price arbitrage, reduced natural gas consumption by prioritizing stored heat, and lower overall energy costs per kilogram of produce, directly protecting operating margin in energy-intensive controlled environments.

Implementation requires a multi-agent architecture, typically built on LangGraph or a custom orchestrator, that ingests live data from greenhouse BMS, weather APIs, and electricity market feeds. The forecasting agent predicts heat demand and prices, while a dispatch agent executes optimized setpoints via direct integration with CHP controller APIs (e.g., BACnet, Modbus) and thermal storage PLCs. Critical controls include human-in-the-loop approval gates for major operational mode changes, continuous performance monitoring against baselines, and automated exception routing to facility engineers when system boundaries are breached.