基于需求响应的家庭负荷优化调度算法研究

张丽; 刘青雷; 艾恒涛; 张涛; 张宏伟

doi:10.19912/j.0254-0096.tynxb.2023-0705

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (9) : 131-139. DOI: 10.19912/j.0254-0096.tynxb.2023-0705

基于需求响应的家庭负荷优化调度算法研究

张丽^1,2, 刘青雷¹, 艾恒涛¹, 张涛^1,3, 张宏伟⁴

作者信息 +

RESEARCH ON OPTIMAL DISPATCH ALGORITHM OF HOUSEHOLD LOAD BASED ON DEMAND RESPONSE

Zhang Li^1,2, Liu Qinglei¹, Ai Hengtao¹, Zhang Tao^1,3, Zhang Hongwei⁴

Author information +

文章历史 +

摘要

为提升电力需求侧负荷需求响应能力,降低家庭用户的用电成本和电网日负荷曲线峰谷差,提出一种基于改进多目标粒子群算法的家庭负荷优化调度策略。首先,分析家庭负荷的运行特性并对其分类建模,采用功率平衡和储能充放电功率为约束,建立以用电成本和负荷峰值平均功率比为目标的负荷优化调度模型;其次,以自适应惯性权重和均值欧氏距离等策略对多目标粒子群算法进行改进,并用改进算法对建立的模型进行优化求解;最后,用实际算例进行仿真,验证所提模型和算法的可行性与有效性,为用户参与需求响应提供了多种参考方案。

Abstract

To enhance the load demand response capability of the electricity demand side, and to reduce the electricity cost for household users and the peak-to-valley difference of the daily load curve, in the paper, an optimized household load scheduling strategy based on improved multi-objective particle swarm optimization is proposed. Firstly, the running characteristics of household load are analyzed and its classification model is built. Then, with power balance and energy storage charging and discharging power as constraints, a load optimization scheduling model aiming at power consumption cost and load peak-to-peak ratio is established. Secondly, adaptive inertial weights and mean Euclidean distance strategies are used to improve the multi-objective PSO, and the model is optimized and solved. Finally, the feasibility and effectiveness of the model and algorithm is verified by a simulation example, which provides a variety of reference schemes for users to participate in demand response.

导出引用

张丽, 刘青雷, 艾恒涛, 张涛, 张宏伟. 基于需求响应的家庭负荷优化调度算法研究[J]. 太阳能学报. 2024, 45(9): 131-139 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0705

Zhang Li, Liu Qinglei, Ai Hengtao, Zhang Tao, Zhang Hongwei. RESEARCH ON OPTIMAL DISPATCH ALGORITHM OF HOUSEHOLD LOAD BASED ON DEMAND RESPONSE[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 131-139 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0705

中图分类号： TM734

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