Our structured approach to validating and implementing AI solutions for grid optimization — on your own grid model and measurement data, ending in a report with detailed evaluation metrics and a quantified savings potential.
Define the project boundaries and objectives: selection of grid section, controllable grid elements, planning horizon and target KPIs.
Gather and validate all necessary data: grid topology, assets and parameters, verification data for calibration, historical load and generation profiles.
Build, test and validate the AI solution: set up and verify the digital twin, create and test a baseline, create and test the AI solution — delivered with a report including detailed evaluation metrics.
Plan for production deployment: a prototypical assistance system, discussion of results, a clear picture of the savings potential and an implementation roadmap towards operationalization.
A grid model in a common format (CIM/CGMES, NEPLAN export, pandapower, PSS/E), measurement history for the study area, a named engineer as technical counterpart, and roughly two hours of your team's time per week. The full checklist is published — data readiness can be assessed before the first call.
The data requirements checklist →A validated digital twin of the study area (yours to keep, in open formats), the module's results on your real scenarios, a quantified business case with written assumptions, and a concrete proposal for the three phases after the PoC — pilot, integration, operation — with prices attached. If the numbers don't justify continuing, the business case says that too.
The three most common failure modes of grid-AI projects, and how we address each one.
If your operators don't believe the recommendations, the numbers don't matter. Mitigation: they're in the working sessions from week five, the reasoning ships with every recommendation, and back-testing happens on their grid's history — not on a benchmark.
Grid models drift from reality; measurements have gaps. Mitigation: the published checklist sets expectations before kickoff, week one is honest about what the data supports, and if the honest answer is "start with state estimation," we say it in week one — not week twelve.
A result nobody in your house can interrogate is a dependency, not a capability. Mitigation: working sessions are teaching sessions, the twin and its formats are open, and the documentation is written for your engineers — not for ours.
A validated digital twin of the study area (yours to keep, in open formats), the module's results on your real scenarios, a quantified business case with written assumptions.
Let's discuss how we can validate and implement AI solutions for your grid optimization needs. Check the data requirements, then contact our team.
