Energy & Utilities · Energy Efficiency & Demand Response

Demand Response Event Dispatch & Customer Coordination

Enhances→Stable

Available Now

Production-ready. Commercial solutions exist and organizations are actively deploying.

Trajectories describe the observable direction of human effort — not a prediction about specific roles, headcount, or individual careers.

What You Do Today

Trigger DR events manually when system operators call for load reduction. Dispatch curtailment signals to enrolled customers and DERs. Manage customer fatigue and opt-out rates by limiting event frequency. Settle baseline and performance calculations after each event.

AI Technologies

Reinforcement Learning (Optimal DR Event Timing and Duration)ML Optimization (Curtailment Portfolio Selection)ML Forecasting (Baseline Load and Curtailment Estimation)IoT Analytics (Smart Thermostat and EV Charger Coordination)Predictive Analytics (Customer Fatigue and Opt-Out Modeling)

Roles Involved

Who works on this

Demand Response ManagerEnergy Efficiency ManagerUtility PlannerData Analyst

Manager/SupervisorIndividual Contributor

How It Works

AI dispatches DR events by predicting optimal curtailment portfolios across customer classes and DER (Distributed Energy Resource) types, maximizing load reduction while minimizing fatigue and opt-out rates.

What Changes

DR dispatch moves from manual event triggers to AI-optimized portfolio dispatch. Curtailment accuracy improves because AI predicts each customer's actual reduction capability rather than using average estimates.

What Stays the Same

Customer relationship management when DR events cause complaints. The call center still handles the angry customer whose smart thermostat was adjusted during a heat wave. Program rules still need regulatory approval.

Cross-Industry Concepts

Resource Optimization Real-Time Decision Making Personalization

Evidence & Sources

•AutoGrid DERMS platform
•Virtual Peaker demand flexibility
•FERC Order 2222 DER aggregation

Sources listed are directional references, not formal citations. Verify against primary sources before using in business cases or presentations.

Last reviewed: March 2026

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for demand response event dispatch & customer coordination, document your current state in energy efficiency & demand response.

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Map your current process: Document how demand response event dispatch & customer coordination works today — who does what, how long each step takes, and where the bottlenecks are. Use your SCADA/EMS data to establish a factual baseline.

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Identify the judgment calls: Customer relationship management when DR events cause complaints. The call center still handles the angry customer whose smart thermostat was adjusted during a heat wave. Program rules still need regulatory approval. — these are the boundaries AI won't cross. Know them before you start.

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Check your data readiness: AI tools for energy efficiency & demand response need clean, accessible data. Check whether your SCADA/EMS has the historical data, integrations, and quality to support Reinforcement Learning (Optimal DR Event Timing and Duration) tools.

Without a baseline, you can't tell whether AI actually improved demand response event dispatch & customer coordination or just changed who does it.

Define Your Measures

What to track and how to calculate it

system reliability (SAIDI/SAIFI)

How to calculate

Measure system reliability (SAIDI/SAIFI) for demand response event dispatch & customer coordination before and after AI adoption. Pull from your SCADA/EMS.

Why it matters

This is the most direct indicator of whether AI is adding value to energy efficiency & demand response.

generation efficiency

How to calculate

Track generation efficiency using the same methodology you use today. Don't change how you measure just because you changed how you work.

Why it matters

Speed without quality is just faster mistakes. Measure both together.

When to check: Check after 30 days of consistent use, then quarterly.

The commitment: Give new tools at least 30 days before judging. The first week is always awkward.

What NOT to measure: Don't measure AI adoption rate as a goal. Measure outcomes. If the tool helps with demand response event dispatch & customer coordination, people will use it.