基于充电负荷随机特性的主动配电网多目标动态重构研究

庞松岭; 赵海龙; 张晨佳

庞松岭,赵海龙,张晨佳.基于充电负荷随机特性的主动配电网多目标动态重构研究[J].电测与仪表,2025,62(12):143-150.
Pang Songling,Zhao Hailong,Zhang Chenjia.Research on Multi-Objective Dynamic Reconfiguration of Active Distribution Network Based on The Random Characteristics of Charging Load[J].Electrical Measurement & Instrumentation,2025,62(12):143-150.

基于充电负荷随机特性的主动配电网多目标动态重构研究

Research on Multi-Objective Dynamic Reconfiguration of Active Distribution Network Based on The Random Characteristics of Charging Load

DOI：

中文关键词: 充电负荷随机特性主动配电网多目标动态重构重构时段开关动作次数偏移量

英文关键词:Random characteristics of charging load Active distribution network Multi objective dynamic reconstruction Reconstruction period Number of switch actions Offset

基金项目:中国南网电网有限责任公司科技项目（073000KK52220001）

作者中文名	作者英文名	单位
庞松岭	Pang Songling	海南电网有限责任公司电力科学研究院
赵海龙	Zhao Hailong	海南电网有限责任公司电力科学研究院
张晨佳	Zhang Chenjia	海南电网有限责任公司电力科学研究院

摘要点击次数: 8

中文摘要:

目前各类负荷加剧了主动配电网运行状况的复杂程度,导致配电网损耗较大。为促进能源的高效、可靠和可持续利用,确保配电网安全稳定运行,以充电负荷随机特性为依据,提出了主动配电网多目标动态重构方法。利用蒙特卡洛方法,模拟起始充电时间与续航量,推算出充电负荷曲线。划分曲线得到充电负荷基本功率,结合负荷波动分量,采用正态分布波动性模型,描述充电负荷随机特性。将网损和电压偏移量最小化、开关次数最少化及负荷均衡度最大化作为目标函数,构建多目标动态重构模型,设计约束条件。通过遗传算法对多目标优化算法进行求解,得到最终的多目标动态重构方案。测试数据表明,最大电压差值从0.068pu缩小至0.013pu,日均衡度从0.31提升至0.67,网损从7692kW降至5468kW,能够有效改善负荷均衡与网损问题,可行性显著。

英文摘要:

At present, various loads have exacerbated the complexity of the operation of active distribution networks, leading to significant losses in the distribution network. In order to promote the efficient, reliable, and sustainable utilization of energy, and ensure the safe and stable operation of the distribution network, an active multi-objective dynamic reconfiguration method for the distribution network is proposed based on the random characteristics of charging loads. The Monte Carlo method was used to simulate the initial charging time and range, and the charging load curve was calculated, divide the curve to obtain the basic power of the charging load, combined with load fluctuation component, the normal distribution fluctuation model is used to describe the random characteristics of the charging load. Minimizing network losses and voltage deviations, minimizing switching times, and maximizing load balance are used as objective functions to construct a multi-objective dynamic reconfiguration model with designed constraints. Solve the multi-objective optimization algorithm through genetic algorithm to obtain the final multi-objective dynamic reconstruction scheme. The test data shows that the maximum voltage difference has been reduced from 0.068pu to 0.013pu, the daily balance has been increased from 0.31 to 0.67, and the network loss has been reduced from 7692kW to 5468kW. This can effectively improve the load balance and network loss issues, with significant feasibility.

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