ENHANCE GRID RESILIENCY
JOBS TO BE DONE
Go beyond nameplate ratings and billing data to understand real load composition by feeder, substation, and transformer using appliance-level signals from AMI. Bidgely ties disaggregated load to each grid asset.
Score every premise for solar, storage, EV, and heat pump propensity so your long-range forecasts reflect what is actually coming, not what already appeared on an interconnection or rebate application.
20+ U.S. states now require NWA evaluation before approving capital upgrades. Bidgely identifies demand-side and DER-based solutions to capacity constraints, saving utilities up to $6M annually from deferred capital investment, before you commit to infrastructure spend.
Integrated Resource Planning (IRP) filings are under more scrutiny than ever, with PUCs rejecting infrastructure plans built on aggregate assumptions. Support interconnection analysis and NWA filings with premises-level load evidence that holds up to regulatory scrutiny, built from actual appliance inventory on the circuit, not regional averages.
USE CASES
Each use case uses the same appliance-level intelligence layer so your grid planning, operations, and regulatory teams operate from the same source of truth.
See what is actually stressing your grid assets, not just what your billing and grid systems suggest.
Stop forecasting with yesterday's data.
Find the demand-side flexibility before you approve the asset upgrade project.
AGENTIC AUTOMATION IN GRID RESILIENCY
Bidgely’s agentic layer handles the grid monitoring and planning workflows your team currently does manually, without adding headcount.
From deferred capital investment and transformer overload prevention. Planning cycles reduced from weeks to hours per constrained asset.
Monitors load at the substation, feeder, and transformer levels using appliance-level signals. Each alert is calibrated against that asset’s own baseline, not a territory average. Surfaces specific load drivers, such as EV growth, heat pump cold-start peaks, and HVAC degradation.
Runs continuous DER adoption projections by circuit, substation, and planning zone based on live propensity scores. Test multiple future scenarios, such as population growth, climate shifts, EV adoption, DER growth, DSM programs—starting from actual meter-level consumption, not generic load shapes.
Runs the entire NWA workflow autonomously, from constraint identification to scenario analysis to ranked recommendation. Produces defensible plans committees can act on without rebuilding from scratch. Generates appliance-level 8760 load curves before and after implementation. Delivers candidate packages including eligible customers, program options, and projected capacity relief.
GENAI IN GRID RESILIENCY
Bidgely’s GenAI layer gives grid planning, operations, and engineering teams a conversational interface to appliance-level data. Complete complex load research in minutes, not months.
A GenAI assistant for planning and engineering teams. Ask about feeder loading, DER concentration, interconnection capacity, and NWA potential to get appliance-level answers without custom queries.
Enables operations teams to ask about outage root cause, load anomalies, and asset performance using plain language. Surfaces appliance-level contributors to grid events so your team diagnoses faster and responds with precision.
Generates plain-language summaries of current-state DER load and hosting capacity for specific circuits and substations, supporting interconnection reviews and regulatory submissions with evidence grounded in AMI data.
PROVEN RESULTS
Detected 10,000+ EVs on its grid from AMI data alone (vs. hundreds previously known). Produced the first-ever 8760 EV load curve with 0.37 kW/EV coincident peak contribution. Mapped EV impacts at T&D and system levels. Now in CYME integration and scenario modeling for long-term grid planning.
Read Case Study
Heating identified as 41.45% of usage, 6.83 MWh/yr premium in electric-heating homes, 303.94 MWh forecast load growth. NWA design for remote diesel grid.
~1,500 EVs mapped with feeder-level visibility, continuous new use cases generated from AMI data without additional hardware.
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