Economics of physics-based solar forecasting in power system day-ahead scheduling

Wenting Wang, Yufeng Guo, Dazhi Yang, Zili Zhang, Jan Kleissl, Dennis van der Meer, Guoming Yang, Tao Hong, Bai Liu, Nantian Huang and Martin János Mayer

Renewable and Sustainable Energy Reviews, 2024, vol. 199, issue C

Abstract: A high-quality solar power forecasting system that strictly adheres to grid regulations is valuable for system operators to formulate strategies for power system scheduling. Some grid operators, therefore, enact penalty schemes for the forecasts submitted by photovoltaic (PV) plant owners, as a means to fortify truthful and high-quality forecast submissions. From the perspectives of both plant owners and grid operators, this study inquires into the quality-to-value mapping of solar forecasts in the context of power system day-ahead scheduling. A physics-based solar power forecasting method is presented, which consists of two steps. Firstly, ensemble numerical weather prediction (NWP) is summarized into point forecasts. Then irradiance is converted to power via a physical model chain. The results reveal that the two-step physics-based forecasting method has an advantage over a winning method in Global Energy Forecasting Competition 2014 in terms of several accuracy measures. Subsequently, the economics of solar forecasting is quantified through performing day-ahead scheduling on a modified IEEE 30-bus system with PV and battery storage. It is demonstrated that, by respecting the statistical theory on consistency and elicitability when extracting point forecasts from NWP ensembles, both power system operators and PV plant owners can benefit profoundly in terms of cost savings. The former sees fewer needs for reserves, while the latter is less penalized. The data and Python code used to produce the results are also provided to enhance the reproducibility of this work.

Keywords: Solar power forecasting; Model chain; Consistency; Elicitability; Day-ahead scheduling; Reproducibility (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114448

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