DOI: 10.5061/dryad.dz08kpsbm
title: GridPath India long-term (2020-2050) power system planning model data

description: 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 structures. Additionally, the repository provides project-level
 wind and solar capacity factors for multiple technology configurations.
description: <strong>Capacity
 Factors</strong> 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. <strong>India-Specific Technology Costs</strong> Solar PV and wind cost projections combine multiple data sources and are adjusted using region-specific factors. Single-axis tracking PV costs are based on 2023 ATB estimates, adjusted using IRENA 2020 auction prices and 2022 ITC data. Fixed-tilt PV costs are derived from U.S. utility-scale cost relationships and adapted for Indian conditions. For rooftop PV, incentivized costs are excluded; the ITC mid estimate is treated as the lower bound and the high estimate as the upper bound. Onshore wind costs adopt 2020 ATB values initially and follow original 2023 ATB projections from 2030 onward due to inconsistencies across datasets. Offshore wind costs combine 2023 ATB and 2022 ITC estimates, with future trends adjusted using local onshore wind prices in the absence of Indian auction data. Cost projections for pumped storage hydropower (PSH), batteries, and hydrogen storage integrate multiple sources and regional considerations. PSH costs reflect 2023 ATB, 2022 ITC, and Indian auction data, with power and energy components scaled using U.S. project data. Battery costs combine 2024 ITC Power Storage and 2022 ATB data, adjusted using international auctions and tenders. Hydrogen costs assume proton exchange membrane electrolysis, storage in tanks or salt caverns, and electricity generation via fuel cells, which are assumed to become viable by 2030–2040. Cost estimates draw from 2024 ITC Green Fuels, NREL, and international studies; salt cavern costs rely on international data due to limited domestic evidence. <strong>GridPath</strong> GridPath is an open-source power system planning platform designed to support long-term capacity expansion, production cost simulation, and resource adequacy analysis. It formulates the electricity system as a linear optimization problem that minimizes total system cost subject to demand, operational, reliability, transmission, and policy constraints. The model represents generation, storage, and transmission assets with technology-specific operational characteristics and allows decisions to be optimized across multiple investment periods.  By integrating investment and operational decisions within a single framework, GridPath allows consistent evaluation of trade-offs between capacity expansion, system operations, and decarbonization pathways under alternative technology, demand, and policy scenarios.
description: # 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 calibrated to
 match reported FY2018–19 generation at the state level. The repository
 also provides technology cost trajectories (`technology_cost.zip`) for
 bettery (`Battery.csv`), pumped storage hydro (`Hydro_Pumped.csv`),
 hydrogen (`Hydrogen.csv`), thermal generators (`Thermal.csv`), solar PV
 (`SolarPV.csv`), and wind (`Wind.csv`) from 2020–2050, along with complete
 GridPath CSV input files (`gridpath_csv_files.zip`). Two alternative sets
 of hourly load demand projections are included: (i)
 `india_2020-2050-24_ICED`, which linearly scales FY2018–19 demand profiles
 to future years, and (ii) `india_2020-2050-24_PIERv2`, which uses
 bottom-up, weather-synchronized FY2018–19 demand profiles from PIERv2
 projected forward to 2050. ### Files and variables ####
 **`capacity_factors.zip`** ##### **`/offshore`** * **time:** date in
 format mm/dd/yyyy HH:MM for the value **capacity_factor** column. *
 **capacity_factor:** ratio of power output with respect to installed
 capacity. * **project:** project name (XX_Y), where XX
 is **load_zone_abr** and Y is the zone letter in the **load_zone**
 (alphabetic order). * **capacity_mw:** maximum available capacity in the
 zone in MW. * **load_zone_abr:** Indian states common two-letter short
 forms. Offshore wind capacity factors are simulated using **ERA5 hourly
 wind speeds**, bias-corrected so that annual mean wind speeds match the
 **Global Wind Atlas (GWA)** at each site. Generation is calculated using
 **135m hub-height, 7MW offshore turbines (Vestas 164)**. Simulations are
 performed for **2019** at hourly resolution. ##### **`/wind_existing`** *
 **time:** date in format mm/dd/yyyy HH:MM for
 the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. Existing onshore wind capacity factors are
 simulated using **ERA5 hourly wind speeds** adjusted to match **GWA annual
 mean wind speeds**. Site-specific turbine classes (low, mid, or high wind)
 are used based on local resource conditions. Simulated generation reflects
 physical turbine performance but does not include operational derates
 beyond standard availability factors and losses. Simulations are performed
 for **2019** at hourly resolution. ##### **`/wind_existingAdjusted`** *
 **time:** date in format mm/dd/yyyy HH:MM for
 the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. This dataset applies **post-simulation
 adjustments** to wind_existing capacity factors so that **FY2018–19
 generation aggregated at the state level matches reported wind
 generation**. These adjustments account for operational constraints,
 curtailment, and performance factors not captured in the physical wind
 model. ##### **`/wind_new`** * **time:** date in format mm/dd/yyyy HH:MM
 for the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. New onshore wind capacity factors are simulated
 using **ERA5 wind data bias-corrected with GWA**, assuming **newer turbine
 models with 120m hub height and 2MW capacity**. Turbine selection is
 site-specific (low, mid, or high wind class), reflecting improved
 performance relative to existing installations. Simulations are performed
 for **2019** at hourly resolution. ##### **`/solarPV_fixedTilt`** *
 **time:** date in format mm/dd/yyyy HH:MM for
 the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. Fixed-tilt solar PV capacity factors are simulated
 using **NREL NSRDB PSM v3** irradiance and temperature data at 4x4 km
 spatial resolution. Generation is calculated assuming **standard
 monocrystalline modules** with regionally appropriate tilt and standard
 utility-scale loss assumptions. Simulations are performed at hourly
 resolution for **FY2018–19**. ##### **`/solarPV_singleAxis`** *
 **time:** date in format mm/dd/yyyy HH:MM for
 the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. Single-axis tracking solar PV capacity factors are
 simulated using **NSRDB PSM v3** data, incorporating a solar **tracking
 mount** to maximize plane-of-array irradiance incidence. Systems assume
 **high-efficiency monocrystalline modules** and standard tracking system
 losses. Simulations are performed at hourly resolution for **FY2018–19**.
 ##### **`/solarPV_roofTop`** * **time:** date in format mm/dd/yyyy HH:MM
 for the value **capacity_factor** column. * **capacity_factor:** ratio of
 power output with respect to installed capacity. * **project:** project
 name (XX_Y), where XX is **load_zone_abr** and Y is the zone letter in the
 **load_zone** (alphabetic order). * **capacity_mw:** maximum available
 capacity in the zone in MW. * **load_zone_abr:** Indian states common
 two-letter short forms. Rooftop solar PV capacity factors are simulated
 using **NSRDB PSM v3**, assuming **standard monocrystalline modules** with
 **higher losses due to soiling and installation constraints** relative to
 utility-scale PV. These capacity factors represent distributed rooftop
 systems rather than optimized ground-mounted plants. The profile for each
 state corresponds to the most populated city. Simulations are performed at
 hourly resolution for **FY2018–19**. #### **`technology_cost.zip`** #####
 **`Battery.csv`** * **Power cost (USD/kW):** Low, mid, and high
 trajectories (2020–2050) * **Energy cost (USD/kWh):** Low, mid, and high
 trajectories (2020–2050) * **Power O&M (USD/kW-yr):** Low, mid, and
 high trajectories (2020–2050) * **Energy O&M (USD/kWh-yr):** Low, mid,
 and high trajectories (2020–2050) ##### **`Hydro_Pumped.csv`** * **Power
 cost (USD/kW):** Low, mid, and high constant trajectories * **Energy cost
 (USD/kWh):** Low, mid, and high constant trajectories * **O&M cost
 (USD/kW-yr):** Low, mid, and high constant trajectories #####
 **`Hydrogen.csv`** * **Capacity costs:** * **Power (USD/kW):**
 Electrolyzer (proton exchange membrane) input combined with fuel cell
 output; low, mid, and high trajectories (2020–2050) * **Energy
 (USD/kWh):** Tank or cavern; low, mid, and high trajectories (2020–2050) *
 **O&M (USD/kW-yr):** Low, mid, and high trajectories (2020–2050) #####
 **`Thermal.csv`** * **Capital cost (USD/kW)** and **O&M (USD/kW-yr)**
 by technology: * Nuclear: mid constant trajectory * CT and CCGT: mid
 constant trajectory * Supercritical coal: low and high constant
 trajectories ##### **`SolarPV.csv`** * **Capital cost (USD/kW):** *
 Single-axis tracking: low, mid, and high trajectories (2020–2050) *
 Fixed-tilt: low, mid, and high trajectories (2020–2050) * Rooftop PV: low,
 mid, and high trajectories (2020–2050) * **O&M (USD/kW-yr):** *
 Single-axis, fixed-tilt, and rooftop PV: mid trajectory (2020–2050) #####
 **`Wind.csv`** * **Capital cost (USD/kW):** * Onshore and offshore wind:
 low, mid, and high trajectories (2020–2050) * **O&M (USD/kW-yr):** *
 Onshore and offshore wind: mid trajectory (2020–2050) ####
 **`gridpath_csv_files.zip`** #### **`/india_2020-2050-24_ICED`** This
 folder contains GridPath input files (`gridpath_input`) in which **hourly
 load demand profiles for 2020–2050 are derived by linearly scaling
 FY2018–19 demand profiles**. The temporal shape of demand is preserved
 from FY19, while annual demand levels are scaled to match projected growth
 in PIERv2.  #### **`/india_2020-2050-24_PIERv2`** This folder contains
 GridPath input files (`gridpath_input`) that use bottom-up hourly load
 demand profiles from PIERv2. These profiles are weather-synchronized with
 FY2018–19 and projected forward to 2020–2050. The `gridpath_input`
 directory contains all model inputs required to reproduce the GridPath
 simulations used in this study. Inputs are organized by model component
 following the native GridPath input structure. Each top-level folder
 corresponds to a specific class of model assumptions: * **`fuels/`**: fuel
 prices and fuel availability assumptions * **`markets/`**: market
 structure and market-related constraints * **`policy/`**: policy
 constraints (e.g., emissions targets, renewable requirements) *
 **`project/`**: generation, storage, and transmission project definitions
 * **`reliability/`**: resource adequacy and reliability constraints *
 **`reserves/`**: operating reserve requirements * **`solver/`**:
 optimization solver settings * **`system_load/`**: hourly electricity
 demand profiles (ICED or PIERv2) * **`temporal/`**: timepoint, period, and
 chronology definitions * **`transmission/`**: inter-zonal transmission
 constraints * **`tuning/`**: numerical and performance tuning parameters
 Scenario selection and model configuration are controlled through the
 `scenarios*.csv` files. These files define which inputs from the above
 folders are activated in each model run and specify the different
 combinations of demand assumptions, cost trajectories, and policy settings
 analyzed. Some folders are empty. However, all folders are included
 intentionally to document the experimental design and to allow exact
 reproduction of the simulations in GridPath. ### ### GridPath-India ####
 Pre-Setup Instructions Before running the model, download the
 `gridpath_input_csvs.zip` folder from Dryad repository and save it to your
 desktop. The path should be
 `~/Desktop/gridpath-india/gridpath_input_csvs`. #### Setting up Conda
 ##### Install Anaconda Anaconda is a package manager and environment
 manager for Python/R. It simplifies managing libraries and creating
 isolated environments. Follow the installation instructions based on your
 OS: * [macOS](https://docs.anaconda.com/anaconda/install/mac-os/) *
 [Windows](https://docs.anaconda.com/anaconda/install/windows/) *
 [Linux](https://docs.anaconda.com/anaconda/install/linux/) #### Terminal
 Access Use the terminal to interact with Conda: * **macOS**: Open Terminal
 via Spotlight or under Applications &gt; Utilities. * **Linux**: Open
 Terminal from the applications menu or by pressing `Ctrl + Alt + T`. *
 **Windows**: Open "Anaconda Prompt" from the Start menu. ####
 Creating a Conda Environment In the terminal, create a new environment
 named `` with Python version 3.9 for GridPath: ```bash conda
 create -n python=3.9 ``` Conda offers various tools to
 manage environments, including activating, deactivating, listing, and
 removing them as needed. For detailed information on managing Conda
 environments, refer to this guide. For example, you can view all existing
 environments in Conda by running: ```bash conda env list ``` #### Setting
 up model requirements in terminal ##### Activating the Conda environment
 To activate the environment by run the following in the terminal: ```bash
 conda activate ``` Once activated, the environment name will
 appear at the beginning of the terminal prompt in parenthesis - any
 subsequent package installations will take place within this environment.
 ##### Installing open-source solver GridPath needs an open-source solver
 software (cbc) to solve the optimization problem. To install cbc: **For
 Mac users:** run the following command to set up the cbc solver: ```bash
 conda install -c conda-forge pyomo coincbc ``` **For Windows PC users:**
 1. Windows PC users need to copy the cbc.exe file to the Library/bin
 folder of your Anaconda environment. The cbc.exe file is located under the
 ‘solver’ in the primary model folder (state_model). 2. In the Anaconda
 Prompt, run the following to get the list of conda environments in the
 system: ```bash conda env list ``` 1. Copy the path next to the
 environment where is installed. It would look something like
 this: C:\ProgramData\anaconda3\envs 2. Open File Explorer and
 paste the path you copied into the address bar. Navigate to the Library
 folder, then to the bin folder. 3. Copy the cbc.exe file from the location
 state_level\solver\cbc.exe and paste it into ~\Library\bin\ (the path
 navigated in step 5). #### Git repositories A Git repository (repo) is a
 version-controlled storage space for project files, tracking changes over
 time and allowing users to collaborate without overwriting each
 other's work. Repos can be hosted locally or on remote platforms like
 GitHub, facilitating efficient code management and collaboration. For more
 information, go to [https://github.com/](https://github.com/) **For Mac
 users:** Git is installed by default in the OS and no extra steps are
 required. **For Windows users:** visit the [Git for
 Windows](https://git-scm.com/download/win) website and download the latest
 version. After downloading, run the installer and follow the prompts.
 ##### Clone gridpath repo GridPath is a versatile grid-analytics platform
 that seamlessly integrates several power-system planning approaches –
 including production-cost, capacity-expansion, asset-valuation, and
 reliability modeling – within the same software ecosystem. More
 information can be found here:
 [https://gridpath.readthedocs.io/en/latest/index.html](https://gridpath.readthedocs.io/en/latest/index.html) **For Mac users:** ```bash cd ~/Desktop/gridpath-india git clone https://github.com/blue-marble/gridpath.git --branch v0.16.1 cd ~/Desktop/gridpath-india/gridpath pip install . ``` **For Windows users:** ```bash cd *\Desktop\gridpath-india git clone https://github.com/blue-marble/gridpath.git --branch v0.16.1 cd gridpath pip install . ``` ### Access information #### State-Level Model Tutorial For demo state-level models capable of running in your local machine, please follow the instructions on this repository: [https://github.com/cetlab-ucsb/gridpath-workshop-ucsb](https://github.com/cetlab-ucsb/gridpath-workshop-ucsb)  #### GridPath Documentation GridPath is a versatile grid-analytics platform that seamlessly integrates several power-system planning approaches – including production-cost, capacity-expansion, asset-valuation, and reliability modeling – within the same software ecosystem.  More information on Gridpath can be found here:[https://gridpath.readthedocs.io/en/latest/introduction.html](https://gridpath.readthedocs.io/en/latest/introduction.html)  #### Perspectives on Indian Energy based on Rumi (PIER)  PIER is an Indian energy systems model for India built on the Rumi modelling platform that estimates demand and cost-optimal supply options to meet the demand. Version 2.0 of PIER currently features energy demand estimation for five different sectors: residential, transport, industry, agriculture and "others" up to 2040-41 For more information on PIER v2, visit: [https://energy.prayaspune.org/our-work/research-report/pier-detailed-demand-side-energy-modelling-of-residential-transport-industry-sectors-for-india-from-fy2023-24-to-fy2040-41](https://energy.prayaspune.org/our-work/research-report/pier-detailed-demand-side-energy-modelling-of-residential-transport-industry-sectors-for-india-from-fy2023-24-to-fy2040-41)   #### Multi-criteria Analysis for Planning Renewable Energy (MapRE)  The Multi-criteria Analysis for Planning Renewable Energy (MapRE) seeks to provide a framework for the systematic identification and valuation of areas for renewable energy development–focusing mainly on solar and wind technologies–for developing countries. For more information, visit [https://mapre.es.ucsb.edu/](https://mapre.es.ucsb.edu/)