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An application of Differential Evolution technique on unit commitment problem using Priority List approach

Manisha Govardhan, Ranjit Roy

Open publisher page 5 citations

Abstract

This paper solves Unit commitment (UC) problem using Priority List (PL) method based on maximum power rating of the generating unit while satisfying all the constraints over a period of time. Two novel optimization techniques, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) are implemented to deal with UC problem and to attain minimum operating cost by proper scheduling of the generating units. The results are compared by considering 10 and 20 units test system over a 24 hour scheduling period. It is found that the results achieved by applying Differential evolution (DE) algorithm are better compared to Particle Swarm Optimization.

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What this paper is about

This paper solves Unit commitment (UC) problem using Priority List (PL) method based on maximum power rating of the generating unit while satisfying all the constraints over a period of time. Two novel optimization techniques, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) are implemented to deal with UC problem and to attain minimum operating cost by proper scheduling of the generating units. The results are compared by considering 10 and 20 units test system over a 24 hour scheduling period. It is found that the results achieved by applying Differential evolution (DE) algorithm are better compared to Particle Swarm Optimization.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper solves Unit commitment (UC) problem using Priority List (PL) method based on maximum power rating of the generating unit while satisfying all the constraints over a period of time. Two novel optimization techniques, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) are implemented to deal with UC problem and to attain minimum operating cost by proper scheduling of the generating units. The results are compared by considering 10 and 20 units test system over a 24 hour scheduling period. It is found that the results achieved by applying Differential evolution (DE) algorithm are better compared to Particle Swarm Optimization.

Key concepts: Differential evolution, Particle swarm optimization, Power system simulation, Mathematical optimization, Scheduling (production processes), Computer science, Economic dispatch, Differential (mechanical device)

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