Overview¶
Multi-step analysis wizards accessible from the Workflows menu. Each wizard provides step-by-step guidance, progress tracking, configurable parameters, and map-based visualization where applicable.
The resource assessment wizards (Solar PV, Wind, OTEC, Rooftop Solar) and the EV & V2G wizard are organized in two phases:
- Phase A: Resource or fleet assessment — defines the study domain, configures the technology baseline, runs the analysis, and presents results.
- Phase B: Advanced analysis and grid integration — detailed characterization, financial analysis, scenario comparison, and generation of configuration parameters for the optimization model.
The Demand Distribution and Grid Auto-Mapping wizards operate in a single phase.
Resource computations are powered by standalone Python libraries that can also be used independently for scripting and batch analysis:
| Library | Scope | PyPI |
|---|---|---|
| solarex | Ground-mounted solar PV resource assessment and MCDA | pip install solarex |
| windrex | Wind resource assessment and wake modeling | pip install windrex |
| rooftex | Rooftop solar potential and building footprint analysis | pip install rooftex |
| evrex | EV fleet adoption, charging demand, and V2G analysis | pip install evrex |
| otex | Ocean thermal energy conversion (OTEC) assessment | pip install otex |
Workflow Catalog¶
| Workflow | Steps | Description | Library |
|---|---|---|---|
| Solar PV Assessment | 9 (5+4) | Ground-mounted PV potential via Multi-Criteria Decision Analysis (MCDA), GHI characterization, financial analysis, array layout optimization, and hourly availability profile generation | solarex |
| Wind Assessment | 9 (5+4) | Wind resource assessment with Weibull fitting, MCDA site ranking, financial analysis, Jensen/Park wake modeling, and hourly availability profile generation | windrex |
| OTEC Assessment | 11 (4+7) | Ocean Thermal Energy Conversion site assessment with thermodynamic cycle modeling, cold water pipe sizing, Monte Carlo uncertainty quantification, and Sobol sensitivity analysis | otex |
| Rooftop Solar | 9 (5+4) | Distributed rooftop PV potential from building footprints, adoption curve modeling (logistic, Gompertz, Bass, agent-based), and scenario-based grid integration | rooftex |
| Demand Distribution | 5 | Spatial distribution of nodal demand among busbars using building footprint classification and density-based or centroid-based clustering | — |
| Grid Auto-Mapping | 5 | Automated power grid construction from open geospatial databases (OpenStreetMap, WRI, GEM, GridFinder) with node placement via clustering and fuel routing | — |
| EV & V2G Assessment | 9 (5+4) | Electric vehicle fleet adoption modeling, charging demand scenario generation, vehicle-to-grid potential assessment, battery degradation analysis, and grid impact evaluation | evrex |
| Financial Analysis | 8 (4+4) | Post-optimization financial assessment: NPV decomposition, IRR/MIRR, LCOE/VALCOE, pro-forma cash flows, sensitivity analysis, and Monte Carlo risk simulation | — |