Load-Frequency Control of Multi-Area Power System Based on the Improved Weighted Fruit Fly Optimization Algorithm

Nian Wang, Jing Zhang, Yu He, Min Liu, Ying Zhang, Chaokuan Chen, Yerui Gu and Yongheng Ren
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Nian Wang: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Yu He: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Min Liu: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Ying Zhang: Guizhou Electric Power Research Institute, Guiyang 550000, China
Chaokuan Chen: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Yerui Gu: School of Electrical engineering, Guizhou University, Guiyang 550025, China
Yongheng Ren: School of Electrical engineering, Guizhou University, Guiyang 550025, China

Energies, 2020, vol. 13, issue 2, 1-17

Abstract: With the development and application of large-scale renewable energy sources, the electric power grid is becoming huge and complicated; one of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints by using conventional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, we propose a co-evolutionary algorithm based on the improved weighted fruit fly optimization algorithm (IWFOA) to solve a multi-area frequency collaborative control model with non-linear constraints. Simulation results show that the control strategy can achieve system control targets, and fully utilize the various characteristics of each generator to balance the interests of different areas. Compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the co-evolutionary algorithm based on the IWFOA has a better suppression effect on the frequency deviation and tie-line power deviation caused by the disturbance and has a shorter adjustment time.

Keywords: differential games theory; a multi-area frequency collaborative control; non-linear constraints; co-evolutionary algorithm; improved weighted fruit fly optimization algorithm (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: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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