Abstract
Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.
Highlights
In recent years, the world has faced a urgent climate change problem and a depletion problem of fossil fuels
It has been shown that the accurate machine learning (ML)-based PV output power prediction schemes with energy storage system (ESS) management could significantly increase the market profit of PV power producers
We calculated the PV power producer’s profit considering the deviation penalties in case of ML-based prediction schemes with/without ESS in order to quantitatively estimate the effect of ESS for different prediction accuracies of two ML-based prediction schemes
Summary
The world has faced a urgent climate change problem and a depletion problem of fossil fuels. We characterize the ESS role in forthcoming energy markets and propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviation penalties from differences between day-ahead (DA) schedule and real-time (RT) supply in energy markets. We analyze the benefit from the accurate prediction scheme with ESS management as a case study on the participation of a utility-scale PV farm at a locational marginal price (LMP) market in the United States. The contributions of this paper can be summarized as follows: (1) We estimate the accuracy of two ML-based prediction schemes and characterize their error distributions in order to quantitatively estimate the effect of the prediction accuracy for PV power trading; (2) The capacity of ESSs, which can reduce cost associated with PV prediction errors for PV power trading, is estimated from different market parameters; (3) In case of ML-based prediction schemes with/without.
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