Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

Le Chi Kien, Thanh Long Duong, Phan Van-Duc and Thang Trung Nguyen
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Le Chi Kien: Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, Vietnam
Thanh Long Duong: Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
Phan Van-Duc: Faculty of Automobile Technology, Van Lang University, Ho Chi Minh City 700000, Vietnam
Thang Trung Nguyen: Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam

Sustainability, 2020, vol. 12, issue 3, 1-35

Abstract: In the paper, a proposed particle swarm optimization (PPSO) is implemented for dealing with an economic load dispatch (ELD) problem considering the competitive electric market. The main task of the problem is to determine optimal power generation and optimal reserve generation of available thermal generation units so that total profit of all the units is maximized. In addition, constraints, such as generation limit and reserve limit of each unit, power demand and reserve demand, must be exactly satisfied. PPSO is an improved version of conventional particle swarm optimization (PSO) by combining pseudo gradient method, constriction factor and a newly proposed position update method. On the other hand, in order to support PPSO to reach good results for the considered problem, a new constraint handling method (NCHM) is also proposed for determining maximum reserve generation and correcting reserve generation. Three test systems with 3, 10 and 20 units are employed to evaluate the real performance of PPSO. In addition to the comparisons with previous methods, salp swarm optimization (SSA), modified differential evolution (MDE) and eight other PSO methods are also implemented for comparisons. Through the result comparisons, two main contributions of the study are as follows: (1) NCHM is very effective for PSO methods to reach a high success rate and higher solution quality, (2) PPSO is more effective than other methods. Consequently, NCHM and PPSO are the useful combination for the considered problem.

Keywords: competitive electricity market; maximum total profit; particle swarm optimization; total fuel cost; total revenue (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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