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Behind-the-Meter Flexibility Optimization

Optimizes on-site load, storage, and generation schedules using tariffs and forecasts to reduce bills and peak demand.

The Problem

“AI Behind-The-Meter Optimization for Cost, Peak, and Resilience Management”

Organizations face these key challenges:

1

Demand charges and time-of-use tariffs are difficult to optimize manually

2

Forecast uncertainty for load, solar, weather, and prices degrades static schedules

3

Flexible assets have many operational constraints such as comfort, process windows, and battery degradation

4

Operators lack a unified optimization layer across BMS, EMS, DERMS, and edge devices

5

Emergency scenarios are too numerous and complex to simulate manually at sufficient depth

6

Distributed batteries are often underutilized because coordination is operationally complex

7

Legacy systems expose inconsistent telemetry and control interfaces

8

Regulatory, safety, and reliability requirements limit acceptable automation behavior

Impact When Solved

Reduce electricity bills through tariff-aware scheduling of loads, storage, and generationLower peak demand charges by shifting or curtailing flexible consumptionIncrease solar self-consumption and reduce grid importsImprove outage resilience with optimized battery reserve policiesEnable virtual power plant participation using coordinated distributed batteriesSupport emergency response planning with AI-assisted scenario simulation and decision supportScale optimization across thousands of devices and sites with consistent control logic

The Shift

Before AI~85% Manual

Human Does

  • •Review interval usage, solar output, and tariff schedules to identify cost drivers.
  • •Set static operating schedules for batteries, EV charging, and flexible loads.
  • •Coordinate manually across facility systems to reduce peaks and maintain comfort.
  • •Adjust plans during unusual weather, occupancy changes, or grid events.

Automation

  • •No AI-driven forecasting or optimization is used in the legacy workflow.
  • •Basic alarms or rule triggers flag obvious threshold breaches.
  • •Simple reports summarize historical consumption and peak demand patterns.
With AI~75% Automated

Human Does

  • •Approve operating objectives, comfort limits, charging priorities, and resiliency policies.
  • •Review recommended dispatch plans and authorize exceptions for business-critical conditions.
  • •Handle edge cases such as outages, maintenance constraints, or conflicting site priorities.

AI Handles

  • •Forecast site load, solar generation, EV demand, and tariff exposure at high time resolution.
  • •Continuously optimize battery, load, and charging schedules to reduce energy spend and peaks.
  • •Coordinate multi-asset dispatch across on-site generation, storage, and flexible loads.
  • •Monitor real-time conditions and automatically adjust schedules as forecasts or grid conditions change.

Operating Intelligence

How Behind-the-Meter Flexibility Optimization runs once it is live

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence95%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Behind-the-Meter Flexibility Optimization implementations:

AI scenario simulationOther
4 mentions
plant operational dataOther
4 mentions
response recommendation engineOther
4 mentions
intelligent system architectureOther
3 mentions
building/load profile dataOther
2 mentions
+2 more technologies(sign up to see all)

Key Players

Companies actively working on Behind-the-Meter Flexibility Optimization solutions:

Westinghouse

Real-World Use Cases

AI emergency scenario simulation for nuclear plant response planning

AI acts like a fast training simulator for a nuclear plant, trying thousands of emergency situations and recommending the safest response plan for each one.

simulation and decision optimizationproposed/deployed early enterprise use case with named vendor adoption signal but limited operational detail in the source.
10.0

EV and battery co-optimization for site energy autonomy

AI helps a building decide when to charge or use batteries and electric vehicles so it can rely more on its own energy and less on the grid.

prescriptive optimizationproposed applied optimization workflow documented as a concrete chapter-level application in a 2025 energy ai book, but source does not confirm commercial deployment.
10.0

AI-driven predictive maintenance and fault prevention for smart grids

Sensors watch the grid all the time, and AI spots signs that equipment may fail soon so crews or automation can act before the lights go out.

anomaly detection and failure predictiondeployed capability; the source presents predictive maintenance and fault flagging as core smart-grid functions.
10.0

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