<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In this paper, starting from the PS dispatching model, the adaptive Corsi variance is introduced to get rid of the local optimum using particle swarm majorization procedure, and the adaptive Corsi variance multiple swarm coevolutionary procedure is constructed through coevolutionary strategy and information sharing strategy. The MCPSO-ACPM procedure is used to optimize the PS scheduling operation model, and experiments are conducted on both load and unit for the optimized scheduling model. From the load majorization results, the peak-to-valley variance is concentrated from 176.02KW to 110.51KW compared with the original load, and the peak-to-valley ratio is reduced by 0.718, which saves customers 98.63 yuan in electricity purchase cost. From the scheduling majorization prediction, the PS output power prediction value of 1 min during the day is closest to the actual measured value of output power, and its prediction deviation is about 2.67%. This shows that the use of a multi-objective majorization procedure can realize the optimal dispatch of PS and achieve the reduction of economic cost.</p>
</abstract>

Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In this paper, starting from the PS dispatching model, the adaptive Corsi variance is introduced to get rid of the local optimum using particle swarm majorization procedure, and the adaptive Corsi variance multiple swarm coevolutionary procedure is constructed through coevolutionary strategy and information sharing strategy. The MCPSO-ACPM procedure is used to optimize the PS scheduling operation model, and experiments are conducted on both load and unit for the optimized scheduling model. From the load majorization results, the peak-to-valley variance is concentrated from 176.02KW to 110.51KW compared with the original load, and the peak-to-valley ratio is reduced by 0.718, which saves customers 98.63 yuan in electricity purchase cost. From the scheduling majorization prediction, the PS output power prediction value of 1 min during the day is closest to the actual measured value of output power, and its prediction deviation is about 2.67%. This shows that the use of a multi-objective majorization procedure can realize the optimal dispatch of PS and achieve the reduction of economic cost.

Analysis of power system scheduling operation mode based on multi-objective optimization algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In...

Analysis of power system scheduling operation mode based on multi-objective optimization algorithm

Publié en ligne: 30 sept. 2023

Pages: -

Reçu: 06 janv. 2023

Accepté: 10 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00406

Mots clés
Power system scheduling, Adaptive Corsi variance, Co-evolutionary strategy, MCPSO-ACPM procedure, Optimal scheduling model.

© 2023 Bo Bao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of power system scheduling operation mode based on multi-objective optimization algorithm

Publié en ligne: 30 sept. 2023

Pages: -

Reçu: 06 janv. 2023

Accepté: 10 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00406

Mots clésPower system scheduling, Adaptive Corsi variance, Co-evolutionary strategy, MCPSO-ACPM procedure, Optimal scheduling model.

© 2023 Bo Bao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Power system scheduling, Adaptive Corsi variance, Co-evolutionary strategy, MCPSO-ACPM procedure, Optimal scheduling model.