Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution

Derong Lv, Guojiang Xiong (), Xiaofan Fu, Yang Wu, Sheng Xu and Hao Chen ()
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Derong Lv: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Guojiang Xiong: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xiaofan Fu: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yang Wu: Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China
Sheng Xu: Guizhou Electric Power Grid Dispatching and Control Center, Guiyang 550002, China
Hao Chen: Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System, Quanzhou 362216, China

Energies, 2022, vol. 15, issue 24, 1-21

Abstract: Optimal power flow is one of the fundamental optimal operation problems for power systems. With the increasing scale of solar energy integrated into power systems, the uncertainty of solar power brings intractable challenges to the power system operation. The multi-objective optimal power flow (MOOPF) considering the solar energy becomes a hotspot issue. In this study, a MOOPF model considering the uncertainty of solar power is proposed. Both scenarios of overestimation and underestimation of solar power are modeled and penalized in the form of operating cost. In order to solve this multi-objective optimization model effectively, this study proposes a clustering-based multi-objective differential evolution (CMODE) which is based on the main features: (1) extending DE into multi-objective algorithm, (2) introducing the feasible solution priority technique to deal with different constraints, and (3) combining the feasible solution priority technique and the merged hierarchical clustering method to determine the optimal Pareto frontier. The simulation outcomes on two cases based on the IEEE 57-bus system verify the reliability and superiority of CMODE over other peer methods in addressing the MOOPF.

Keywords: optimal power flow; uncertainty; differential evolution; hierarchical clustering; Pareto frontier (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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