基于改进二进制粒子群算法的家庭负荷优化调度策略

doi:10.11930/j.issn.1004-9649.202207079

中国电力 ›› 2023, Vol. 56 ›› Issue (5): 118-128.DOI: 10.11930/j.issn.1004-9649.202207079

基于改进二进制粒子群算法的家庭负荷优化调度策略

张丽¹, 刘青雷¹, 张宏伟²

1. 河南理工大学电气工程与自动化学院, 河南焦作 454003;
2. 国网山西省电力公司临汾供电公司, 山西临汾 041000

收稿日期:2022-07-27 修回日期:2022-12-08 出版日期:2023-05-28 发布日期:2023-05-27
作者简介:张丽(1982-),女,通信作者,博士,副教授,从事电网需求侧响应、智能用电信息技术研究,E-mail:dqzhangli@hpu.edu.cn;刘青雷(1996-),男,硕士研究生,从事电力系统优化与控制研究,E-mail:lql199@home.hpu.edu.cn;张宏伟(1990-),男,工程师,从事电力调度与信息处理研究,E-mail:18835747666@163.com
基金资助:
国家自然科学基金资助项目（基于稳定裕度补偿的大型光伏并网系统谐波谐振抑制策略研究，U1804143）；河南省科技攻关项目（大容量光伏并网系统的暂态稳定性分析及其关键控制技术研究，202102210295）；河南省高校基本科研业务费专项（NSFRF210424）；河南理工大学青年骨干教师资助项目（2019XQG-17)。

Home Load Optimization Scheduling Strategy Based on Improved Binary Particle Swarm Optimization Algorithm

ZHANG Li¹, LIU Qinglei¹, ZHANG Hongwei²

1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China;
2. Linfen Power Supply Company, State Grid Shanxi Electric Power Company, Linfen 041000, China

Received:2022-07-27 Revised:2022-12-08 Online:2023-05-28 Published:2023-05-27
Supported by:
This work is supported by National Natural Science Foundation of China (Study on Harmonic Resonance Suppression Strategy of Large Photovoltaic Grid-connected System Based on Stability Margin Compensation, No.U1804143), Science & Technology Project of Henan Province (Transient Stability Analysis and Key Control Technology Research of Large Capacity Photovoltaic Grid-connected System, No.202102210295), Fundamental Research Funds for Universities of Henan Province (No.NSFRF210424) and Funding Project for Young Backbone Teachers of Henan University of Technology (No.2019XQG-17).

摘要/Abstract

摘要： 为降低家庭用电成本以及提高户用光伏发电的就地消纳率, 提出了一种基于实时控制储能充放电行为的家庭负荷调度策略。首先，对家庭负荷分类并建立以电费最低、电力碳排放量最小及舒适度最大为目标的调度模型；其次，提出以实时光伏出力和峰谷分时电价为依据，通过控制储能充放电实现家庭负荷用电需求的调度策略；最后，利用场景分析法和分等级多策略学习的二进制粒子群改进算法（HLSBPSO）对模型进行仿真求解。结果表明，所提策略和算法可使用户电费降低49.2%，舒适度提高67.9%，可为户用光伏发电的安全经济运行提供新的理论支持。

关键词: 需求响应, 二进制粒子群算法, 碳排放, 柔性负荷, 家庭能量管理系统

Abstract: In order to reduce the cost of household electricity consumption and improve the local consumption rate of residential photovoltaic power generation, a home load scheduling strategy is proposed based on real-time control of energy storage charging and discharging behavior. Firstly, the household loads are classified and a scheduling model is established with the objectives of lowest electricity cost, smallest carbon emission and largest comfort; secondly, based on the real-time photovoltaic output and peak-valley time-of-use electricity price, a scheduling strategy is proposed to meet the household load electricity demand through controlling the charging and discharging of energy storage; finally, the proposed model is simulated and solved using the scenario analysis method and hierarchical multi-strategy learning improved binary particle swarm optimization algorithm (HLSBPSO). The results show that the proposed strategy and algorithm can reduce the user's electricity bill by 49.2% and increase the comfort by 67.9%, which can provide a new theoretical support for the safe and economical operation of household photovoltaic power generation.

Key words: demand response, binary particle swarm algorithm, carbon emission, flexible load, home energy management system

张丽, 刘青雷, 张宏伟. 基于改进二进制粒子群算法的家庭负荷优化调度策略[J]. 中国电力, 2023, 56(5): 118-128.

ZHANG Li, LIU Qinglei, ZHANG Hongwei. Home Load Optimization Scheduling Strategy Based on Improved Binary Particle Swarm Optimization Algorithm[J]. Electric Power, 2023, 56(5): 118-128.

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基于改进二进制粒子群算法的家庭负荷优化调度策略

Home Load Optimization Scheduling Strategy Based on Improved Binary Particle Swarm Optimization Algorithm

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基于改进二进制粒子群算法的家庭负荷优化调度策略

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