Industrial overcapacity can enable seasonal flexibility in electricity use: Supplementary Data
收藏资源简介:
This repository contains raw data related to the paper "Industrial overcapacity can enable seasonal flexibility in electricity use." 'aluminum_demand_all_scenarios.json' is a JSON file containing the aluminum demand projections for all planning horizons and scenarios. 'costs_YYYY.csv' provides year-specific technology cost parameters, including investment and operational costs for energy technologies through 2060. 'China_current_capacity.csv' and related technology-specific files in 'existing_infrastructure/' contain the installed capacity data for power generation and heating infrastructure by province. 'edges.txt' defines the transmission network topology and connectivity used for network preparation. 'heat_demand_profile_{scenario}{year}.h5' and associated files in 'heating/' provide hourly district heating demand profiles, heat pump performance data (COP), and solar thermal generation profiles. 'daily_hydro_inflow_per_dam_1979_2016_GWh.pickle' and related hydro data files store multi-decadal daily inflow, reservoir capacity, and nominal power potential for hydroelectric systems. 'load{year}_weatheryears_1979_2016_TWh.h5' contains the hourly electricity demand profiles for 31 provinces, capturing weather-dependent variability over a 37-year historical period. 'CHN_adm1.shp' provides the geographic shapefiles for Chinese administrative boundaries used to define model regions. 'al_smelter_p_max.csv' and 'al_production_ratio.csv' contain the maximum power capacity and technical production parameters specifically for the aluminum smelting industry. 'population.h5' contains gridded population data used for spatial demand allocation. 'regions_onshore.geojson' and 'regions_offshore.geojson' define the geographic regions and availability constraints for renewable energy resource deployment. Other compressed or HDF5 files contain the high-resolution renewable generation profiles and climate reanalysis data used to drive the energy system optimization.



