Dynamic Environmental Economic Dispatch Considering the Uncertainty and Correlation of Photovoltaic–Wind Joint Power

Yi Ru, Ying Wang (), Weijun Mao, Di Zheng and Wenqian Fang
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Yi Ru: College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Ying Wang: College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Weijun Mao: College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Di Zheng: College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
Wenqian Fang: College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China

Energies, 2024, vol. 17, issue 24, 1-18

Abstract: The traditional power grid planning lacks consideration of the uncertainty and correlation between wind and solar joint output in the same region, which poses challenges to the stable operation of the power system. Therefore, it is greatly important to consider the environmental and economic dispatch in light of the uncertainties and correlations associated with wind and solar energy. To tackle these issues, this paper introduces a dynamic environmental economic dispatch model that accounts for the uncertainties and correlations between wind and photovoltaic power based on their output characteristics. Initially, a probability model for photovoltaic–wind joint power is established using the Copula function. Subsequently, the Latin hypercube sampling method is employed alongside an improved K-means clustering technique to derive typical output scenarios. An adaptive multi-objective fireworks algorithm, featuring a differential selection strategy, is then utilized to enhance the environmental economic dispatch model. Finally, the IEEE 39 node system is used as an example to demonstrate the solution of the dynamic environmental and economic scheduling model. Simulation results reveal that the method for generating typical output scenarios presented in this paper effectively captures the uncertainties and correlations of photovoltaic–wind joint power. Furthermore, when compared to other optimization algorithms, the improved adaptive multi-objective fireworks algorithm proves to be more efficient in addressing the dynamic environmental economic dispatch challenges within the power system.

Keywords: dynamic economic environment scheduling; fireworks algorithm; multi-objective optimization algorithm; photovoltaic–wind joint power; uncertainty and correlation (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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