SchedPulse

Near-real-time plant schedule feedback from executed trades, helping energy operators rapidly update physical dispatch plans to meet delivery commitments under ramp constraints and late market changes.

The Problem

“Executed energy trades are not reflected in plant dispatch plans fast enough”

Organizations face these key challenges:

1

Executed trades arrive faster than operators can manually assess plant feasibility

2

Ramp constraints and unit limits make simple quantity matching unsafe

3

Late market changes force repeated schedule revisions

4

Trade systems, scheduling tools, and plant control data are often disconnected

Impact When Solved

Reduce time from trade execution to dispatch-plan update from minutes to secondsImprove on-time delivery compliance under ramp and unit constraintsLower imbalance penalties and manual correction effortIncrease operator throughput during volatile market periods

The Shift

Before AI~85% Manual

Human Does

  • •Monitor executed trades and reconcile position changes against current plant schedules
  • •Assess ramp limits, unit operating constraints, and delivery commitments for feasible dispatch changes
  • •Revise dispatch plans in scheduling or EMS tools as market conditions change
  • •Coordinate schedule adjustments with control room staff and confirm implementation

Automation

  • •Send basic trade or schedule notifications from predefined rules
  • •Flag simple threshold breaches for capacity or timing
  • •Provide limited data aggregation from disconnected operational sources
With AI~75% Automated

Human Does

  • •Approve or reject recommended dispatch-plan updates for nonstandard or higher-risk situations
  • •Review constraint conflicts and decide how to handle infeasible trade impacts
  • •Set operating priorities when delivery commitments, plant limits, and market changes compete

AI Handles

  • •Continuously interpret executed trade events and map them to required schedule changes
  • •Evaluate dispatch feasibility against ramp constraints, unit limits, and current plant status
  • •Generate and prioritize recommended schedule updates with highlighted risks and conflicts
  • •Publish or route approved schedule changes and monitor plant response in near real time

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence87%
ArchetypeRecommend & Decide
Shape6-step converge
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 shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

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 handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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