SPIDER accurately forecasts DER adoption and demand impacts to facilitate distribution planning. Custom-tailored for your unique needs, SPIDER integrates all DERs into a single spatial and temporal forecast model that explicitly addresses uncertainty and risk.
SPIDER™ (Spatial Penetration and Integration of Distributed Energy Resources) permits rapid, accurate, granular DER adoption & impact forecasts to facilitate both distribution and system-level planning – with explicit integration of uncertainty & risk.
Proven, vetted methodology developed by a team with 15 years of experience modeling DER adoption for electric utilities, commissions, NREL and the U.S. D.O.E
Flexible spatial resolution that balances computational needs with data availability and inherent uncertainty, avoiding the trap of "false precision"
Spatial, temporal forecasts at the feeder, substation, or ZIP Code level, as desired
Auto-calibrates in minutes with updated data, ensuring that forecasts are never stale
Model is custom-tailored to meet your evolving needs
Prevents being shackled to a one-size-fits-all “solution”
Seamlessly integrates with legacy data, maximizing flexibility
Optional development of customized DER-tracking database (from application through approval)
Transparent & Visual
Unparalleled transparency through a visual, icon-based platform (a stark contrast with most "black box" models)
Visually compare historical adoption with simulated back-casts, adding confidence to the forecast and improving buy-in
Easily inspect every model input, output, and intermediate variable in both chart and table format
Quickly test multiple scenarios and run what-if analyses
integrated risk analysis
Integrates any combination of solar PV, battery storage, electric vehicles, energy efficiency, and demand response into a single forecast
Model structure and calibration techniques inherently account for DER interactions and correlations
Addresses uncertainty and planning risk using Monte Carlo simulation
DER economics and demand impacts respond to any electric rate structure (TOU, demand charges, etc.)
Mixed-integer programming permits simulating hourly or sub-hourly dispatch optimization (e.g., for storage systems)
Responds to historical (for calibration) and forecast changes to electric rates, system costs, incentives, tax credits, etc.
Multiple delivery options
Model usage is dictated by your unique needs. Options include:
Desktop Application - Maximum Control
Software as a Service (SaaS) - Secure Web Interface
Forecasting as a Service (FaaS) - Regularly Updated