RectifHyd—monthly generation estimates for 1,500 hydroelectric power plants in the United States

Cameron Bracken^1*, Nathalie Voisin¹, and Kristian Nelson¹

¹ Pacific Northwest National Laboratory

* Contacts: cameron.bracken@pnnl.gov nathalie.voisin@pnnl.gov

Abstract

The U.S. Energy Information Administration conducts an annual survey (form EIA-923) to collect, among other variables, annual and monthly net generation for more than ten thousand U.S. power plants. Only ~10% of the ~1,500 hydroelectric plants included in this regular data release come with observations of monthly net generation; the remaining 90% of plants are represented with monthly net generation that is imputed using a statistical method. The imputation method neglects local hydrology and reservoir operations, rendering the monthly generation data unsuitable for several research applications. Here we present an alternative approach to disaggregate each plant’s reported annual generation using historical reservoir releases (~180 plants) or, for the majority of cases where reservoir releases are unavailable, river discharge recorded downstream of each dam (~1180 plants). We evaluate the approach using 130 plants for which monthly generation observations are available, showing median Kling Gupta Efficiency (KGE) of 0.74, 90% CI [-0.07, 0.93] across plants with observed reservoir release, and KGE of 0.51 [-0.28, 0.79] for cases imputed using a downstream flow gage. The new dataset—named RectifHyd—provides a timely alternative to EIA-923 for U.S. scale, plant-level, hydropower net generation for analyzing within-year hydropower generation behavior and constraining sub-annual hydropower availability in power grid operation and expansion models.

Journal reference

Turner, S.W.D., Voisin, N., and Nelson, K. (202X). RectifHyd—monthly generation estimates for 1,500 hydroelectric power plants in the United States. (In prep.)

Data reference

Turner, S., Voisin, N., Nelson, K., & Bracken, C. (2023). RectifHyd (1.2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8422075

Data download

In R:

readr::read_csv("https://zenodo.org/record/10011017/files/RectifHyd_v1.2.1.csv")

In Python:

import pandas as pd
pd.read_csv("https://zenodo.org/record/10011017/files/RectifHyd_v1.2.1.csv")

Reproduce RectifHyd

1. Clone this repo to get all scripts.
2. Download each of the following datasets and place in the data directory.

• ResOpsUS: Steyaert, J., Condon, L., Turner, S. & Voisin, N. (2021). ResOpsUS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5367383
• EIA-923: U.S. Energy Information Administration (2023). EIA-923/906/920. [Data set]. EIA. https://www.eia.gov/electricity/data/eia923/ (all spreadsheets 2001 - 2022)
• HILARRI: Hansen, C.H., and P.G. Matson. 2021. Hydropower Infrastructure - LAkes, Reservoirs, and Rivers (HILARRI). DOI: 10.21951/HILARRI/1781642
• Existing Hydropower Assets: Megan M. Johnson, Shih-Chieh Kao, and Rocio Uria-Martinez. 2023. Existing Hydropower Assets, 2023. HydroSource. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. https://doi.org/10.21951/EHA_FY2023/1972057
• NWIS: U.S. Geological Survey, 2016, National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), accessed [September, 2023] via R package [http://waterdata.usgs.gov/nwis/].

1. Run the following R scripts in the main directory to re-create this experiment:

Script Name	Description
1. Process EIA spreadsheets.R	Combine each of the last 20 years’ EIA-923 data releases, extract hydropower net generation, output clean data table as .csv
2a Disaggregate using ResOpsUS.R	Prepare data to disaggregate annual hydropower using reservoir releases from ResOpsUS
2b Disaggregate using USGS.R	Prepare data to disaggregate annual hydropower using downstream flows
3. Combine and compute new monthly generation.R	Combine flow fraction tables and perform disaggregation of annual to monthly flow
4a. Create final file.R	Clean up and prepare the final RectifHyd file

Enjoy!

ST | NV | KN | CB