Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer

Ragab El-Sehiemy, Abdallah Elsayed, Abdullah Shaheen, Ehab Elattar and Ahmed Ginidi
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Ragab El-Sehiemy: Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Abdallah Elsayed: Department of Electrical Engineering, Faculty of Engineering, Damietta University, Damietta 34517, Egypt
Abdullah Shaheen: Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt
Ehab Elattar: Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Ahmed Ginidi: Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt

Sustainability, 2021, vol. 13, issue 21, 1-24

Abstract: Typically, the main control on alternating current (AC) power systems is performed by the scheduling of rotary machines of synchronous generators and static machines of on-load tap changer (OLTC) transformers and volt-ampere reactive (VAR) sources. Large machines of synchronous generators can be managed by utilizing terminal voltage control when synchronized in parallel to the power system. These machines are typically terminal voltage regulated. In addition, substation on-load tap changer (OLTC) transformers improve system voltage management by controlling variable turn ratios that are adjusted in different levels known as taps along either the primary or secondary winding. Moreover, volt-ampere reactive (VAR) sources of static VAR compensators (SVCs), which are automated impedance devices connected to the AC power network, are designed for voltage regulation and system stabilization. In this paper, scheduling of these machines is coordinated for optimal power system operation (OPSO) using a recent algorithm of social network search optimizer (SNSO). The OPSO is performed by achieving many optimization targets of cost of fuel, power losses, and polluting emissions. The SNS is a recent optimizer that is inspired from users in social networks throughout the different moods of users such as imitation, conversation, disputation, and innovation mood. The SNSO is developed for handling the OPSO problem and applied on an IEEE standardized 57-bus power system and real Egyptian power system of the West Delta area. The developed SNSO is used in various assessments and quantitative analyses with various contemporary techniques. The simulated findings prove the developed SNSO’s solution accuracy and resilience when compared to other relevant techniques in the literature.

Keywords: optimal power flow; social network search algorithm; electrical power grids; power losses; fuel costs; emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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