An Improved Algorithm for Optimal Load Shedding in Power Systems

Raja Masood Larik, Mohd Wazir Mustafa, Muhammad Naveed Aman, Touqeer Ahmed Jumani, Suhaib Sajid and Manoj Kumar Panjwani
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Raja Masood Larik: School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
Mohd Wazir Mustafa: School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
Muhammad Naveed Aman: Department of Computer Science, National University of Singapore, Singapore 119077, Singapore
Touqeer Ahmed Jumani: School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
Suhaib Sajid: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Manoj Kumar Panjwani: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2018, vol. 11, issue 7, 1-16

Abstract: A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

Keywords: under voltage loadshedding; power systems; blackouts; voltage collapse; genetic algorithms (GA); particle swarmoptimization (PSO) (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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