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Notes on this data set:

This data set provides inputs for a brownfield capacity expansion planning study considering the Electricity Reliability Corporation of Texas (ERCOT) system representing a planning year of 2036.

All inputs are generated by the open source PowerGenome data platform from public data sources. Some PowerGenome output columns are renamed or removed as redundant.

Existing generators are clustered based on heat rate and fixed O&M cost.

There are 3 regions (see accompanying map and time series data provided for various representative time periods -- 10 days, 4 weeks, 8 weeks, and 16 weeks (with hourly chronological resolution within each week) -- as well as a full year (52 weeks, 8,760 hours) time series. Each representative period represents 1 or more other days/weeks throughout the time series. The number of hours represented by each sample period is given by the Representative_Period_Weight input column in the Inputs_data.csv file. Representative weeks and weights are determined via K-means clustering to minimize error in wind, solar, hydropower and demand time series within each cluster as per Mallapragada et al. (2018), Impact of model resolution on scenario outcomes for electricity sector system expansion, Energy 163 (DOI: https://doi.org/10.1016/j.energy.2018.08.015) and always include the peak demand week.

Demand profiles are based on historical 2012 profile and increased by a fixed percentage for all hours based on U.S. EIA Annual Energy Outlook 2020 projected demand growth for Texas through 2036.

Renewables data profiles are from an open source data set available via PowerGenome under open source license from Vibrant Clean Energy, with 13-km continental United States resolution and are for weather year 2012.

Technology costs are from NREL Annual Technology Baseline 2019 edition and represent the average of costs for the period 2021-2036.

Transmission transfer limits from EPA IPM model.

Data is provided without warranty for accuracy.

Note this is the same data set as provided for Notebook 07 and the columns are described in more detail there. A set of code for reading these inputs into Julia is also available there.