Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length

Xiaoming Liu, Liang Wang, Yongji Cao (), Ruicong Ma, Yao Wang, Changgang Li, Rui Liu and Shihao Zou
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Xiaoming Liu: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China
Liang Wang: State Grid Shandong Electric Power Company, Jinan 250001, China
Yongji Cao: Academy of Intelligent Innovation, Shandong University, Jinan 250101, China
Ruicong Ma: Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China
Yao Wang: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China
Changgang Li: Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China
Rui Liu: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250061, China
Shihao Zou: Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Jinan 250061, China

Energies, 2023, vol. 16, issue 7, 1-16

Abstract: Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios. A case study is conducted to verify the effectiveness of the proposed approach. The calculated time autocorrelation, average offset rate, and climbing similarity are 0.0515, 0.0396, and 0.9035, respectively, which shows the superior performance of the proposed approach.

Keywords: ARIMA model; copula function; genetic algorithm; renewable energy; scenario generation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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