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AI Energy Optimization Platform

Provides a cloud-based AI layer to ingest real-time operational data and continuously optimize energy use across assets and sites.

The Problem

“Continuously optimize electrified fleet charging and energy market participation in real time”

Organizations face these key challenges:

1

Vehicle readiness targets conflict with lowest-cost charging windows

2

Real-time tariff, market, and grid conditions change faster than human operators can respond

3

Depot power limits, transformer constraints, and charger availability create complex scheduling bottlenecks

4

Vehicle arrival times, route changes, and state-of-charge data are noisy or incomplete

5

Renewable generation and site load forecasts are uncertain

6

Market participation rules and telemetry requirements are operationally complex

7

Separate systems for telematics, chargers, EMS, and market bidding prevent coordinated decisions

8

Static rules cause overcharging during peaks and undercharging before departures

Impact When Solved

Reduce fleet charging energy cost by 10% to 25% through dynamic scheduling against tariffs and market pricesIncrease vehicle readiness and on-time departure performance with constraint-aware charging plansLower site peak demand charges by 15% to 35% using coordinated charger, battery, and load controlCreate new revenue streams from demand response and ancillary market participation where regulations allowImprove use of on-site solar and battery assets to reduce grid imports and carbon intensityDelay or avoid distribution upgrade costs by operating within transformer and feeder limits

The Shift

Before AI~85% Manual

Human Does

  • •Review SCADA, weather, market, and load reports to estimate near-term supply and demand
  • •Adjust dispatch, storage, and procurement plans using rules, spreadsheets, and operator judgment
  • •Coordinate generation, storage, and DER actions across separate workflows and control views
  • •Run manual scenario checks for outages, price swings, and peak demand periods

Automation

  • •Provide basic historical reporting from operational data
  • •Surface threshold alarms and standard control room alerts
  • •Generate fixed-interval forecasts using simple statistical methods
  • •Calculate routine performance and settlement summaries
With AI~75% Automated

Human Does

  • •Set operating objectives, risk limits, emissions priorities, and reliability guardrails
  • •Approve dispatch, bidding, or load-shifting actions above defined financial or operational thresholds
  • •Review AI recommendations during abnormal grid, market, or asset conditions

AI Handles

  • •Continuously ingest cross-asset operational, weather, maintenance, and market data
  • •Generate probabilistic forecasts for load, renewable output, prices, and asset availability
  • •Optimize dispatch, storage cycling, and flexibility actions under cost, emissions, and grid constraints
  • •Monitor real-time conditions, detect forecast drift or asset anomalies, and re-rank actions as conditions change

Operating Intelligence

How AI Energy Optimization Platform 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

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