Agentic System for Managing Electric Vehicle (EV) Charger Load and Customer Rates

This workflow automates the repetitive, data-intensive task of aligning customer charging costs with grid stability. For the utility, it eliminates manual analysis of charger telemetry and rate-plan performance, creating a continuous feedback loop for infrastructure planning and rate design. For customers, agents monitor usage, simulate billing scenarios, and recommend optimal rate switches, directly reducing their electricity costs while shifting load away from peak periods. The operational upside comes from improved asset utilization, reduced peak demand charges, and higher customer satisfaction with personalized, automated energy management.

Implementation integrates with the Customer Information System (CIS) for billing, Meter Data Management (MDMS) for usage, and distribution planning tools. The orchestrator, built on frameworks like LangGraph, routes data between specialized agents: one handles customer interactions via chat or portal, another models localized transformer load. Controls include approval gates for rate changes, confidence scoring on recommendations, and observability dashboards tracking adoption and grid impact. The architecture must handle data quality issues, exception routing for complex cases, and phased rollout to different customer segments.

This workflow automates the complex, manual process of advising customers on time-of-use rates and charging schedules. It eliminates repetitive call-center inquiries and spreadsheet-based analysis by deploying persistent agents that continuously monitor individual charger telemetry, weather, and real-time grid conditions. The operational upside comes from reduced customer service costs, increased adoption of grid-friendly rates, and the creation of a high-fidelity dataset for infrastructure planning and dynamic rate design, directly improving asset utilization and deferring capital expenditure.

Implementation integrates with the utility's Meter Data Management System (MDMS) and CIS via APIs, with agents built on frameworks like LangGraph for stateful orchestration. Each customer-facing recommendation passes through configurable business rules and approval gates before communication. The system includes observability for agent decisions, exception routing for edge cases to human advisors, and continuous model retraining using new charging patterns. This creates a closed-loop system where customer actions improve grid models, and refined models yield better customer savings.

Phase 1 establishes the core data pipeline and customer-facing agent. We integrate with the Meter Data Management System (MDMS) and Customer Information System (CIS) to ingest real-time charger usage. An AI agent analyzes this against time-of-use (TOU) rate schedules, providing personalized shift recommendations via the customer portal. This initial layer delivers immediate value by reducing customer bill shock and creating a foundation of trusted, actionable data without requiring grid-side control.

Phase 2 introduces the grid-side modeling agent and orchestration. This agent aggregates anonymized charging patterns to forecast localized transformer load, identifying infrastructure risk. The system can then initiate proactive, incentive-based demand-response events, communicating through the customer agent. Phase 3 integrates directly with SCADA and Distributed Energy Resource Management (DERM) platforms, allowing the orchestration engine to execute automated, pre-approved load-shift commands during critical peak events, monetizing grid stability and deferring capital expenditure.

This workflow automates the orchestration between customer-facing rate advisors and grid-analytic agents. It eliminates manual analysis of charging patterns and localized load impacts, directly reducing customer service inquiries and enabling more precise, data-driven rate design. The operational upside comes from shifting load to off-peak periods, lowering grid upgrade deferral costs, and improving customer satisfaction through personalized savings. Implementation requires integrating with the Customer Information System (CIS), Advanced Metering Infrastructure (AMI), and time-of-use rate engines.

Governance is enforced through approval gates for any rate change or infrastructure alert, with human-in-the-loop review for exceptions. A phased rollout starts with a pilot cohort, validating agent recommendations against historical data before full deployment. Controls include continuous monitoring of recommendation acceptance rates, model drift detection for load forecasts, and immutable audit logs for all agent decisions to ensure regulatory compliance and build operational trust before scaling.