Multi-Agent Automation for University Campus Microgrid Orchestration

This workflow automates the real-time economic dispatch of a campus microgrid, coordinating academic buildings, dormitories, central plants, and on-site renewables. It eliminates manual load forecasting and reactive adjustments by integrating with campus BMS, SCADA, and financial systems. The operational upside comes from slashing peak demand charges, capturing wholesale price arbitrage, and reducing carbon emissions through intelligent storage and generation scheduling, directly impacting the university's operating budget and sustainability targets.

Implementation requires deploying containerized agents that subscribe to live telemetry and publish optimized setpoints via APIs or direct PLC control. Each agent respects domain-specific constraints: academic schedules protect research lab uptime, dorm agents prioritize comfort, and storage logic models degradation costs. The orchestrator uses a solver to maximize net economic value, with all decisions logged for audit and performance review. Rollout is sequenced by building cluster, with governance ensuring facilities management retains override authority for any grid stability or safety concern.

A university campus microgrid presents a complex orchestration challenge, balancing energy between academic buildings, dormitories, research labs, and central plants against volatile real-time prices and carbon signals. The operational bottleneck is the manual, reactive coordination between facility managers, energy buyers, and building operators. This custom automation workflow directly attacks peak demand charges, reduces overall procurement costs by 15-25%, and leverages on-site generation and storage to provide financial and resilience ROI. The architecture integrates with the campus BMS, SCADA, and financial systems to create a closed-loop control system.

Implementation centers on a LangGraph-based orchestrator that defines agent roles, conversation flows, and approval gates. The Building Load Agent negotiates with the Orchestrator to execute pre-approved load-shifting strategies in non-critical zones. The Storage and Generator agents optimize dispatch using a solver that models degradation costs and fuel prices. Controls are mandatory: all major setpoint changes or generator commands route through a human-in-the-loop dashboard for confirmation, with automatic rollback if telemetry deviates from expected ranges. Observability is built into the agent framework, logging every decision rationale for audit and continuous optimization against actual meter data.

This orchestration workflow automates the continuous economic dispatch of a mixed asset portfolio—solar, CHP, battery storage, and flexible building loads—against volatile real-time energy prices and campus demand. It eliminates the manual, reactive coordination between facilities, finance, and sustainability teams, directly reducing peak demand charges and wholesale energy costs by 15-25%. The system integrates with campus BMS, SCADA for generator control, and financial systems for settlement, using predictive models to respect academic schedules, research lab priorities, and resilience requirements without compromising operations.

Implementation requires deploying containerized agent logic—likely using LangGraph or a custom Python orchestration layer—on a platform with sub-minute latency to campus control systems. Each agent (Load, Storage, Generator) manages its domain's constraints and communicates bids to a central optimizer. Critical controls include human-in-the-loop gates for major mode changes (e.g., islanding), real-time observability dashboards, and automated exception routing to facility engineers. Rollout is sequenced by building cluster, with performance validated against a digital twin of campus thermal and electrical networks before live dispatch.

Initial deployment focuses on a single, non-critical asset cluster—like a dormitory thermal storage system—to validate forecasting and control logic without risking academic or research operations. Agents operate in a shadow mode, recommending actions logged against a digital twin, while human operators retain direct control via the campus BMS or SCADA. This sandbox phase builds confidence in data quality from IoT meters and inverter APIs, and establishes baseline performance metrics for cost and carbon savings before agents assume any direct load-shaping authority.

Upon validated performance, control expands to direct agent execution for pre-approved actions, such as time-of-use shifting for HVAC plants, enforced by automated approval gates for any deviation beyond set bounds. A central orchestrator, built on frameworks like LangGraph, manages agent conflicts and logs all decisions for audit. The final phase enables full multi-asset orchestration with continuous observability dashboards tracking financial exposure, carbon impact, and system health, ensuring campus engineers can override any agent action instantly via integrated SCADA alarms.