skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: The cost of day-ahead solar forecasting errors in the United States

Journal Article · · Solar Energy

As solar energy contributes an increasing share of total electricity generation, solar forecasting errors become important relative to overall load uncertainty and can add costs to electricity systems. We investigated the costs of day-ahead solar forecast errors across 667 existing solar power plants in the United States (years 2012 through 2019). Our analysis was based on hourly real-time and day-ahead nodal prices. We analyzed two types of solar forecasts: persistence forecasts, a simple approach to forecasting, and a numerical weather prediction forecast, the North American Mesoscale Model (NAM), an improvement over persistence forecasts based on public data and modelling software. We modeled hourly energy forecasts using meteorological forecasts and plant specific characteristics. Hourly plant generation was modeled and debiased with multiple sources of generation records. NAM forecast errors had relatively low costs on average, at no more than $$\$$1$$/MWh in all years except 2016, when costs rose to $$\$$1.5$/MWh. Even after these error costs, the value of solar was marginally higher when simulating solar participation in day-ahead markets versus participation only in real-time markets. On average, the premium for participating in the day-ahead market, based on NAM forecasts, ranged from -0.5 to 5.2 $/MWh across years. Average error costs were higher in regions with higher solar penetration (i.e., California and New England) compared to regions with low solar penetration. Furthermore, California and New England had similar error costs despite higher solar penetration in California, indicating that error costs to date have been only loosely correlated with solar penetration levels.

Research Organization:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
ac02-05ch11231
OSTI ID:
1836615
Alternate ID(s):
OSTI ID: 1840957
Journal Information:
Solar Energy, Journal Name: Solar Energy Vol. 231 Journal Issue: C; ISSN 0038-092X
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (17)

Benefits of solar forecasting for energy imbalance markets journal February 2016
The potential of forecasting in reducing the LCOE in PV plants under ramp-rate restrictions journal December 2019
Integrating solar PV (photovoltaics) in utility system operations: Analytical framework and Arizona case study journal June 2015
Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets journal May 2016
Hybrid machine learning forecasting of solar radiation values journal February 2016
The value of day-ahead solar power forecasting improvement journal May 2016
Economic Implications of Enhanced Forecast Accuracy: The Case of Photovoltaic Feed-In Forecasts journal January 2015
Solar Power Forecasting: A Review journal July 2016
The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany journal September 2018
Solar photovoltaic generation forecasting methods: A review journal January 2018
Solar and wind grid system value in the United States: The effect of transmission congestion, generation profiles, and curtailment journal July 2021
Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning journal October 2010
Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate journal August 2015
Economic merits of a state-of-the-art concentrating solar power forecasting system for participation in the Spanish electricity market journal July 2013
The value of day-ahead forecasting for photovoltaics in the Spanish electricity market journal December 2017
The National Solar Radiation Data Base (NSRDB) journal June 2018
Forecast value considering energy pricing in California journal July 2014