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...,

本仓库提供面向GridPath-India的模型数据，该模型是基于开源GridPath平台搭建的容量扩展模型（Capacity Expansion Model, CEM）。GridPath-India以34个负荷区、跨州输电网络和逐时用电需求刻画印度电力系统，并对2020至2050年多个规划周期内的发电、储能与输电投资及运行策略进行优化。该模型采用每月2个典型日（峰值与平值需求日）、逐时时间分辨率来模拟长期电力系统规划与运行。

该模型数据涵盖现有、规划中及候选发电与储能项目，同时包含超过1300个候选风电与光伏场址，以及印度各州自备燃煤装机的汇总数据。此外还包含预定义的输电与项目组合、运行特性、可靠性要求及政策目标场景集，可变可再生能源（Variable Renewable Energy, VRE）项目的有效载流能力、可用性因子及时序结构……，以及容量因子相关内容。

本数据集采用MapRE框架表征可变可再生能源（VRE）开发的候选场址，重点聚焦光伏与风电资源。通过将MapRE与PySAM、PVWatts集成的气象-可再生能源建模方法，生成逐时容量因子（Capacity Factor, CF）曲线。
光伏容量因子数据源自国家太阳辐射数据库（National Solar Radiation Database, NSRDB），风电容量因子则采用ERA5再分析数据，并结合全球风能图集（Global Wind Atlas）的高分辨率风速数据进行偏差校正。风电容量因子还经过降尺度处理，以匹配印度中央电力管理局（Central Electricity Authority, CEA）公布的历史发电数据。
上述容量因子曲线以逐时分辨率（共8760小时）生成，反映了技术特异性特征与可用性因子。每个州及技术类型均包含多个候选场址，可为资源导向且兼顾经济性的场址选择决策提供支撑。

印度专属技术成本
光伏与风电的成本预测整合了多源数据，并通过区域化调整……
# GridPath印度长期（2020-2050）电力系统规划模型数据
数据集DOI：[10.5061/dryad.dz08kpsbm](https://doi.org/10.5061/dryad.dz08kpsbm)

## 数据与文件结构说明
本仓库包含运行2020至2050年印度GridPath-India电力系统规划模型所需的输入数据与软件。数据包含针对2018-19财年模拟得到的光伏与风电资源逐时容量因子时间序列（`capacity_factors.zip`），其中风电数据基于ERA5再分析数据，光伏数据源自美国国家可再生能源实验室（NREL）NSRDB PSM v3数据库，且风电风速已通过全球风能图集完成偏差校正。涵盖的技术类型包括海上风电（`/offshore`）、陆上风电（现有陆上风电`/wind_existing/`、调整型现有陆上风电`/wind_existingAdjusted`、新增陆上风电`/wind_new`）以及光伏系统（固定倾角`/solarPV_fixedTilt`、单轴跟踪光伏`/solarPV_singleAxis`、屋顶光伏`/solarPV_roofTop`）。技术特异性假设涵盖机组类型、轮毂高度、光伏配置与损耗，调整后的风电曲线已完成校准……