GridPath India long-term (2020-2050) power system planning model data

This repository provides model data for GridPath-India, a capacity expansion model (CEM) implemented in the open-source GridPath platform. GridPath-India represents India’s electricity system with 34 load zones, interstate transmission, and hourly demand. Generation, storage, and transmission investments and operations are optimized across multiple planning periods from 2020 to 2050. This model uses two representative days per month (peak and median demand) at hourly temporal resolution to simulate long-term power system planning and operations.
The model data includes existing, planned, and candidate generation and storage projects, as well as more than 1,300 candidate wind and solar sites, and a compilation of state-level coal captive capacity. Plus, a predefined set of scenarios for transmission and project portfolios, operational characteristics, reliability requirements, and policy targets, efficient load-carrying capability for VRE projects, availability factors, and temporal stru...,
Capacity Factors

This dataset uses the MapRE framework to characterize candidate sites for variable renewable energy (VRE) development, focusing on solar PV and wind resources. Hourly capacity factor (CF) profiles are generated using a weather-to-VRE modeling approach that integrates MapRE with PySAM and PVWatts.
Solar CFs are derived from the National Solar Radiation Database (NSRDB), while wind CFs use ERA5 reanalysis data, unbiased with high-resolution wind speeds from the Global Wind Atlas. Wind CFs are derated to match historical generation reported by the Central Electricity Authority (CEA).
CF profiles are generated at hourly resolution (8,760 hours) and reflect technology-specific characteristics and availability factors. Multiple candidate sites per state and technology are included to support resource-aware and economically informed siting decisions.

India-Specific Technology Costs

Solar PV and wind cost projections combine multiple data sources and are adjusted using regi..., # GridPath India long-term (2020-2050) power system planning model data

Dataset DOI: [10.5061/dryad.dz08kpsbm](https://doi.org/10.5061/dryad.dz08kpsbm)

## Description of the data and file structure

This repository contains input data and software required to run GridPath-India power system planning models for India from 2020–2050. The data include hourly capacity factor time series (`capacity_factors.zip`) for solar and wind resources simulated for FY2018–19 using ERA5 (wind) and NREL NSRDB PSM v3 (solar), with wind speeds bias-corrected using the Global Wind Atlas. Technologies represented include offshore (`/offshore`) and onshore wind (existing `/wind_existing/`, adjusted `/wind_existingAdjusted`, and new `/wind_new`) and solar PV (fixed-tilt `/SolarPV_singleAxis`, single-axis tracking `/solarPV_singleAxis`, and rooftop `/solarPV_roofTop`). Technology-specific assumptions are applied for turbine type, hub height, PV configuration, and losses, and adjusted wind profiles are calibra...,