Abstract
With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.
Highlights
Published: 13 September 2021In recent years, the penetration rate of renewable energy such as solar energy has increased [1]
PV power generation is affected by the environment, resulting in power fluctuations lasting for several seconds to several minutes [2], in which the maximum instantaneous power fluctuation rate can reach 75%/s [3], causing grid voltage and frequency flicker, which will reduce power quality and power supply reliability [4,5]
In Sun Y., Tang X., Sun X., et al [10], an improved low-pass filtering algorithm (ILFA) is proposed to optimize the power distribution of the battery and the supercapacitor, and it combines with the fuzzy control (FC) to smooth the power fluctuations based on the SC priority control strategy
Summary
The penetration rate of renewable energy such as solar energy has increased [1]. In Sun Y., Tang X., Sun X., et al [10], an improved low-pass filtering algorithm (ILFA) is proposed to optimize the power distribution of the battery and the supercapacitor, and it combines with the fuzzy control (FC) to smooth the power fluctuations based on the SC priority control strategy. The low-pass filtering and SOC are combined to smooth the output power fluctuations, but after data acquisition and calculation, its RMSE is up to 4.73%, and the accuracy of power smoothing is reduced. References [14,15,16,17,18] smooth the power fluctuations within a short period based on power prediction, but the accuracy of such methods is on the low side.
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