基于改进多目标粒子群算法的家庭用电时段优化

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

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (1): 70-78.doi: 10.16182/j.issn1004731x.joss.20-0681

基于改进多目标粒子群算法的家庭用电时段优化

闫秀英^1,2, 党苗苗²

1.西安建筑科技大学建筑设备科学与工程学院,陕西西安710055;
2.西安建筑科技大学信息控制与工程学院,陕西西安 710055

收稿日期:2020-09-09 修回日期:2021-02-17 出版日期:2022-01-18 发布日期:2022-01-14
作者简介:闫秀英(1980-)，女，博士，副教授，研究方向为建筑智能化。E-mail：55746411@qq.com
基金资助:
国家十三五科技支撑项目子课题(2016YFC0700208-03); 陕西省低能耗建筑节能创新示范工程项目研究(2017ZDXM-GY-025)

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

Yan Xiuying^1,2, Dang Miaomiao²

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;

Received:2020-09-09 Revised:2021-02-17 Online:2022-01-18 Published:2022-01-14

摘要/Abstract

摘要： 为解决家庭用电负荷的调度优化问题,综合考虑用电成本、满意度以及用户侧波动程度3个目标进行优化。提出改进自适应权重多目标粒子群算法(improved adaptive weighted multi-objective particle swarm optimization, IAW-MOPSO)求解模型,通过对粒子的适应度值分段更新惯性权重,均衡了粒子群算法的局部改良能力和全局搜索能力,在保证得到全局最优解的同时完成对家用电器的优化调度。结果表明：该优化策略降低了29%的电费,保障了高峰时期用电的稳定性,用户满意度明显增加,验证了所提模型的有效性以及算法的优越性。

关键词: 家庭能源管理, 用电调度, 多目标优化, 自适应权重, 满意度

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.

Key words: home energy management, electricity dispatch, multi-objective optimization, adaptive weight, satisfaction

中图分类号:

TP391.9

闫秀英, 党苗苗. 基于改进多目标粒子群算法的家庭用电时段优化[J]. 系统仿真学报, 2022, 34(1): 70-78.

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.

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基于改进多目标粒子群算法的家庭用电时段优化

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

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