Optimization of Household Electricity Consumption Period Based on Improved Multi-objective Particle Swarm Optimization

Authors

Xiuying Yan, 1. School of Construction Equipment Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;;2. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;;
Miaomiao Dang, 2. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;;

Abstract

Abstract: Aiming at the household power load scheduling optimization, three objectives of the cost of electricity, satisfaction and user-side fluctuation degree are taken into comprehensive account. An improved adaptive weight multi-objective particle swarm optimization (IAW-MOPSO) algorithm is proposed to realize the scheduling optimization of household power load. The local improvement ability and global search ability of particle swarm optimization are balanced by updating the inertia weight of particle fitness value. The simulation results of five groups show that the proposed optimization strategy reduces the electricity charge by 29%, ensures the stability of electricity consumption in the peak period, and obviously increases the user satisfaction, which verifies the validity of the proposed model and the superiority of the algorithm.

Recommended Citation

Yan, Xiuying and Dang, Miaomiao (2022) "Optimization of Household Electricity Consumption Period Based on Improved Multi-objective Particle Swarm Optimization," Journal of System Simulation: Vol. 34: Iss. 1, Article 8.
DOI: 10.16182/j.issn1004731x.joss.20-0681
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss1/8

First Page

70

Revised Date

2021-02-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0681

Last Page

78

CLC

TP391.9

Recommended Citation

Yan Xiuying, Dang Miaomiao. Optimization of Household Electricity Consumption Period Based on Improved Multi-objective Particle Swarm Optimization[J]. Journal of System Simulation, 2022, 34(1): 70-78.

DOI

10.16182/j.issn1004731x.joss.20-0681