Study on optimization of economic dispatching of electric power system based on Hybrid Intelligent Algorithms (PSO and AFSA)

Guanghui Yuan and Weixin Yang

Energy, 2019, vol. 183, issue C, 926-935

Abstract: In order to optimize the economic dispatching of the electric power system, this paper has proposed a new Hybrid Intelligent Algorithm based on the Particle Swarm Optimization (PSO) and Artificial Fish Swarm Algorithm (AFSA). Basing on a comprehensive dispatching optimization model with the goal of minimizing coal consumption, pollution emission and purchasing cost, we have utilized this Hybrid Intelligent Algorithms to solve the integrated weighted dispatching optimization model for five units and ten units respectively, considering the node flow balance of the power system, as well as the system's active power balance, positive and negative reserve constraints, transmission capacity constraints, unit output constraints, node voltage constraints, unit output power rise rate constraints, unit minimum runtime and downtime constraints, etc. The calculation results in the case study of five units are that coal consumption is 10,074.17 hundred-yuan, carbon emission is 11,280.75 kg, and electric power cost is 12,827.54 hundred-yuan. The calculation results in the case study of ten units, given a population size of 200, are that coal consumption is 31,305.45 hundred-yuan, carbon emission is 13,982.06 kg, and electric power cost is 16,754.79 hundred-yuan; while given a population size of 30, coal consumption is 29,221.16 hundred-yuan, carbon emission is 10,921.21 kg, and electric power cost is 16,521.56 hundred-yuan. Moreover, we have obtained the above results with an improvement of 35.56% in calculation efficiency.

Keywords: PSO; AFSA; Hybrid intelligent algorithm; Electrical power system; Dispatching and optimization (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:183:y:2019:i:c:p:926-935

DOI: 10.1016/j.energy.2019.07.008

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