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. However, 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.
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An open-access version of this article published in Solar Energy can be downloaded here.