Applied Energy

These data describe how the energy usage of a large, diverse residential sector would change if all space heating was electrified. Using the actual weather data from 2016 for 17 locations in Texas, thousands of building models representative of the building stock in the residential sector of the Texas electric grid were simulated using the open-source dynamic energy modeling tool, EnergyPlus. Four total scenarios are examined in this study: a base scenario representative of current building stock and three electrification scenarios. In each electrification scenario, building models with fossil fuel heating sources had their heating units replaced with reversible heat pumps (i.e., heat pumps that provide both heating and cooling). The three electrification scenarios reflected the efficiency of the reversible heat pump being installed: standard efficiency, high efficiency, and ultra-high efficiency. Data reflect a residential system peak shifting from summer to winter and reduced summer consumption due to efficiency improvements. 1. Energy Consumption Data ~/hourly_energy_consumption: Hourly energy usage data for each building model was collected from EnergyPlus and multiplied by a scaling factor sized to reflect the actual dimensions of the Texas grid's residential sector. In the case of this study, that scaling factor is 230. The scaled hourly energy usage was summed over all modeled dwellings to give the Texas grid's residential sector hourly energy consumption. Each scenario's hourly consumption data are in a separate csv file in this directory, noted by the filename. 2. Daily Peak Demand ~/daily_peak_demand: The maximum hour of electricity consumption (kWh) for each day is divided by the change in time (one hour) to create an absolute peak hourly demand value (kW) for the day. These values for maximum hourly demand on each day are referred as daily peak demand values in the associated journal article. Each scenario's daily peak demand data are in a separate .csv file in this directory, noted by the filename. 3. Building Stock Details ~/building_stock_details The .csv file "base_scenario_building_stock_data.csv" includes housing information (e.g., insulation details, setpoint data, geometry data) about every building modeled in this study. It also includes annual energy and end-use consumption values for each building. Note: data from approximately 38,000 dwellings of the total 41,000 were used in our study, because some of the locations covered areas not served by the Texas electric grid. The remaining .csv files contain energy consumption data for the buildings that had heating units replaced by heat pumps. The files are organized by electrification scenario.

The supplemental information provides supporting figures comparing the virtual water trade of food and electricity in Europe. Additionally, we provide a description of the contents in File S1, which contains data necessary for reproducing the figures in the manuscript, "The Changing Virtual Water Trade Network of the European Electric Grid" by Christopher M. Chini and Ashlynn S. Stillwell.

1) This supplementary file is the simulation program for the Applied Energy submission "Core Temperature Modelling and Monitoring of Lithium-ion Battery in the Presence of Sensor Bias"; 2) The simulation file can run well under the Environment of MATLAB R2018b 3）The system identifcation of nonliear model is in "MOGA_coefficient_with_radiator.m" 3) By executing the following commands, >> UKF_temp() >> EUKF_temp_bias() One can directly get the results shown in Fig. 8. For other figures in Section 3, the codes should be accordingly modifed. 4) MATLAB is a registered trademark is a trademark of The MathWorks, Inc.

Data set of hourly QCLCD for nine low wind sites in Florida is extracted from the NOAA data set cited below. National Oceanic Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) formerly National Climatological Data Center (NCDC), “QCLCD ASCII Files,” Data Set. [Online]. Available: https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/quality-controlled-local-climatological-data-qclcd. [Accessed: 21-Aug-2019]

Electronic supplementary material accompanying the article: Fighting carbon leakage through Consumption-Based carbon emissions policies: empirical analysis based on the World Trade Model with Bilateral Trades Matteo V Rocco°*, Nicolò Golinucci°*, Stefano M. Ronco^, Emanuela Colombo°* ° Department of Energy, Politecnico di Milano, Via Lambruschini 4, 21056, Milan, Italy * Fondazione Eni Enrico Mattei (FEEM), Corso Magenta 63, 21056, Milan, Italy ^ Università degli Studi di Torino, Via Giuseppe Verdi 8, 10124, Turin, Italy Corresponding Author’s e-mail address: matteovincenzo.rocco@polimi.it

Assessing energy and economic impacts of large-scale policy shocks based on Input-Output analysis: Application to Brexit

Discharge capacity fading data from cycle aging test at different temperatures (LFP and NMC batteries)

This data contains a weather file for SAM, and hourly demand for Kyoto City for 2018.

Irish TIMES input assumptions including technology capital costs and bioenergy potentials. Model outputs for scenario ensembles used to derive MACCs.

Gams Code for the 24-bus test system Case 1 related to the following research article: Dual variable decomposition to discriminate the cost imposed by inflexible units in electricity markets