基于数据驱动的高比例新能源配电网无功优化算法

doi:10.3969/j.issn.2097-0706.2023.11.002

综合智慧能源 ›› 2023, Vol. 45 ›› Issue (11): 10-19.doi: 10.3969/j.issn.2097-0706.2023.11.002

基于数据驱动的高比例新能源配电网无功优化算法

林泓宏¹(), 余涛¹(), 张桂源²^,^*(), 张孝顺³()

1.华南理工大学电力学院，广州 510640
2.南方电网产业投资集团有限责任公司，广州 510630
3.东北大学佛山研究生创新学院，广东佛山 528311

收稿日期:2023-05-09 修回日期:2023-06-14 出版日期:2023-11-25
通讯作者: * 张桂源（1998），男，工程师，从事人工智能与电气应用方面的研究，1037174861@qq.com。
作者简介:林泓宏（1997），男，在读硕士研究生，从事人工智能与电气应用方面的研究，384502053@qq.com；
余涛（1974），男，教授，博士，从事电力系统运行与控制、人工智能技术方法研究，taoyu1@scut.edu.cn；
张孝顺（1990），男，副教授，博士，从事人工智能与电气应用方面的研究，zhangxiaoshun@mail.neu.edu.cn。
基金资助:
中央高校基本科研业务资助(N2229001)

Data-driven reactive power optimization algorithm for the distribution network with high proportion of renewable energy

LIN Honghong¹(), YU Tao¹(), ZHANG Guiyuan²^,^*(), ZHANG Xiaoshun³()

1. College of Electric Power，South China University of Technology，Guangzhou 510640，China
2. China Southern Power Grid Industry Investment Group Company Limited，Guangzhou 510630，China
3. Foshan Graduate School of Innovation，Northeastern University， Foshan 528311，China

Received:2023-05-09 Revised:2023-06-14 Published:2023-11-25
Supported by:
Fundamental Research Fund for the Central Universities(N2229001)

摘要/Abstract

摘要：

随着大量分布式新能源接入配电网，其电能损耗和节点电压偏差增大等问题愈发严重，仅依赖安装无功补偿设备、改变传统发电机机端电压等手段已无法满足高比例新能源配电网的高调节性能需求。为此，研究深度挖掘不同种类新能源对于配电网的无功调控潜力，详细分析了目标函数以及约束条件的设置，规划了基于数据驱动的无功调控算法流程，实现了合理调节配电网的节点电压与降低配电线路电能损耗的目标。首先，构建了高比例新能源参与配电网无功优化的数学模型，并应用多种智能算法求解。随后，采用多种算法得到的无功调控策略作为深度学习长短期记忆（LSTM）神经网络的训练数据，通过训练的网络在此类模型下可以预测得到优质的、高效率的无功调控策略。最后，利用IEEE 14与IEEE 33节点算例验证了所提算法的有效性和优化性能。

关键词: 高比例新能源, 配电网, 深度学习, 无功优化, 帕累托前沿

Abstract:

With the integration of massive distributed new energy into distribution networks， power loss and voltage deviation at nodes are becoming increasingly severe. The high requirements on high-proportion renewable energy distribution networks' adjustability can not be satisfied by traditional measures merely，such as installation of reactive power compensation devices， adjusting the voltage at the generator side. Therefore，the reactive power regulation capacities of different types of new energy generators in distribution networks are study deeply. The study analyses the objective function and constraint settings，and outlines a data-driven reactive power regulation algorithm. The algorithm can regulate node voltage and reduce power loss of the distribution network at the same time. Firstly，a mathematical model of reactive power optimization for the high-proportion new energy distribution network is constructed， and solved by various intelligent algorithms. Subsequently，the reactive power regulation solution sets obtained from the algorithms are used as training data for the deep learning long-short-term memory（LSTM）network. The trained network can predict reactive power regulation strategies efficiently based on the model above. Finally，the effectiveness and optimization performance of the proposed algorithm is validated by in an IEEE 14-bus system and an IEEE 33-bus system.

Key words: high-penetration new energy, distribution network, deep learning, reactive power optimization, Pareto front

中图分类号:

TK01

林泓宏, 余涛, 张桂源, 张孝顺. 基于数据驱动的高比例新能源配电网无功优化算法[J]. 综合智慧能源, 2023, 45(11): 10-19.

LIN Honghong, YU Tao, ZHANG Guiyuan, ZHANG Xiaoshun. Data-driven reactive power optimization algorithm for the distribution network with high proportion of renewable energy[J]. Integrated Intelligent Energy, 2023, 45(11): 10-19.

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新能源	接入新能源序号	风速/（m⋅s^-1）	辐照度/(W⋅m^-2)	有功功率/kW	无功功率范围/(kV·A)
光伏电站	1		775.43	54.28	[-25.35,25.35]
光伏电站	2		677.71	47.44	[-36.73,36.73]
风电场	1	10.18		239.30	[-110.39,133.64]
电动汽车充放电站	1			70.80	[-70.62,70.62]

新能源	接入新能源序号	风速/（m⋅s^-1）	辐照度/(W⋅m^-2)	有功功率/kW	无功功率范围/(kV·A)
光伏电站	1		589.57	41.27	[-43.55,43.55]
	2		578.71	40.51	[-44.26,44.26]
	3		535.71	37.50	[-46.84,46.84]
	4		515.00	36.05	[-47.96,47.96]
风电场	1	11.70		290.10	[-76.50,111.07]
	2	11.48		282.74	[-81.41,114.34]
	3	11.36		278.64	[-84.15,116.16]
电动汽车充放电站	1			70.00	[-71.41,71.41]
	2			69.08	[-72.30,72.30]
	3			63.12	[-77.56,77.56]

基于数据驱动的高比例新能源配电网无功优化算法

Data-driven reactive power optimization algorithm for the distribution network with high proportion of renewable energy

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