SAGE: a smart agent for grid reinforcement.
A QMerse workflow around the tools planners already use, DIgSILENT PowerFactory and Python. It decides what to study, reads the results, screens compliance and drafts the report. It doesn't replace the solver; it takes away the week of manual work around it.
- No.
- 01
- Type
- Multi-agent workflow
- Tools
- PowerFactory, Python
- Agents
- A coordinator and five specialists
- Sign-off
- An engineer at every gate
- Built on
- QMerse
- Status
- Demonstrator
One request, five agents, one engineer.
Watch a reinforcement study run from request to report, or pick an agent to see what it takes in and hands on.
Agentic core
Plans the study, splits it into tasks, hands them to the agents in the right order and sends every output to the engineer before anything is trusted. It organises the work; the engineer signs it off.
Takes in
- Engineer's request
- Study objective
- Available agents
Hands on
- Task plan
- Agent calls
- Run ready for review
Illustrative run. The figures are examples.
The work around the solver, automated.
- Automates
Study set-up and runs
Builds the scenarios and runs the batch, with no manual model wrangling.
- Automates
Results triage
Finds, ranks and explains the violations that actually matter.
- Automates
Compliance and reporting
Screens results against your criteria and drafts the report.
- You stay in control
Engineer in the loop
Your engineers review and approve every step before anything is trusted.
You stay in control
This is one example of what QMerse can do. Tell us the manual process that slows your team down, and we build the workflow that removes it.