How can utilities see what is actually driving load on specific feeders, substations, or transformers?

Utilities can trace load on a specific feeder, substation, or transformer back to its root cause by rolling up appliance-level AMI data from individual homes to the asset level. Bidgely's Grid Asset Capacity Analysis aggregates meter data across all premises served by an asset, breaking down total load by end use, including EV charging, heat pumps, solar, batteries, and more, so planning and operations teams can see exactly what's driving peak demand at that location. With actual load drivers in view, utilities can assess capacity more accurately, prioritize upgrades based on real need, and flag which constraints call for deeper analysis. Bidgely’s intelligence layer is backed by 90%+ DER detection accuracy at the premises level. Utilities have also used AMI data to identify more than 10,000 EVs across their service territories.

How can utilities evaluate non-wires alternatives before committing to a traditional upgrade?

Utilities can evaluate non-wires alternatives before committing to a traditional upgrade by screening a local constraint for flexible demand-side and DER-based options before infrastructure spend is locked in. Bidgely's Non-Wires Alternatives workflow identifies the load driving the constraint, ranks customers by flexibility value per transformer, and quantifies NWA potential using appliance-level load-shift potential modeled from AMI data. With this, planning teams can screen alternatives earlier, compare customer-side options against a conventional upgrade, and build a more defensible case for deferral when the load and flexibility profile support it. This approach reduces NWA analysis from weeks to hours per asset, and has saved utilities up to $6M annually from deferred capital investment.

How can utilities improve long-term load forecasts when EVs, solar, and heat pumps are changing the load curve?

Utilities can improve long-term load forecasts in a DER-heavy grid by replacing static load snapshots with hourly views that capture how EVs, solar, storage, and heat pumps change demand across the year. Bidgely’s Long-Term DER Forecasting and Planning workflow starts from actual meter-level consumption, scores premises for likely DER adoption, and produces granular 8760 forecasts that reflect daily, weekly, and seasonal variability. Utilities can then enhance these forecasts with their own planning inputs, such as program targets or local growth assumptions, to bring utility-specific context into scenario analysis.

How can utilities use Bidgely's DER-aware forecasts with planning tools like CYME and Synergi?

Bidgely’s DER-aware forecasts fit into planning workflows by feeding granular load and DER-adoption outputs into the tools utilities already use for grid analysis, not by replacing those environments end to end. Long-Term DER Forecasting and Planning produces 8760 views of daily, weekly, and seasonal variability from premise-scored adoption and actual meter-level consumption. Planning teams can bring customer-level DER behavior and premise-scored adoption into established study workflows, then continue scenario modeling and power-flow analysis inside the systems they already trust for engineering decisions.

How have utilities used Bidgely to improve grid resiliency?

Bidgely's grid resiliency approach applies appliance-level intelligence to real utility workflows, supporting measurable outcomes in EV visibility, DER-aware forecasting, and non-wires alternative evaluation. Hydro One identified more than 10,000 EVs from AMI data alone, compared with only hundreds previously known, and Bidgely's approach has cut NWA analysis time from weeks to hours per asset. More details can be found within the following resources: the Hydro One NWA story, the AI-Powered Load Forecasting for DER Integration video, and the Modernizing DER Programs webinar.

Der Grid Planning

Grid outcomes you can actually own.

Know the true capacity of every asset on your system.

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.

Plan for the DERs you cannot see yet.

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.

Find Non-Wires Alternatives (NWAs) before you break ground.

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.

Defend your filings with data.

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.

Transformer Loading Monitoring Agent

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.

DER Forecasting and Scenario Agent

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.

Non-Wires Alternatives Recommendation Agent

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.