EN计及风电场无功支撑性能的多目标优化调度策略
Multi-objective Optimal Dispatch Strategy Considering Reactive Power Support Performance of Wind Farm
- 动力工程学报 2025年45卷第1期 页码：87-95
- 作者机构：
1. 华北电力大学 控制与计算机工程学院,北京,102206
2. 国网甘肃省电力公司电力科学研究院,甘肃,兰州,730070
- 作者简介：
[ "杨健(1995—),男,河南许昌人,博士研究生,研究方向为新能源发电技术" ]
[ "任国瑞(通信作者),男,讲师,硕士生导师,博士,E-mail:guorui@ncepu.edu.cn" ]
- 基金信息：国家自然科学基金资助项目(52107091)
DOI：10.19805/j.cnki.jcspe.2025.230439
中图分类号： TM614;TM73-
网络首发：2025-01-15，
纸质出版：2025
- 稿件说明：
移动端阅览
杨健,王玮,周强,付炳喆,任国瑞. 计及风电场无功支撑性能的多目标优化调度策略动力工程学报, 2025, 45(1): 87-95 https://doi.
org/10.19805/j.cnki.jcspe.2025.230439
杨健,王玮,周强,付炳喆,任国瑞. 计及风电场无功支撑性能的多目标优化调度策略动力工程学报, 2025, 45(1): 87-95 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230439.
org/10.19805/j.cnki.jcspe.2025.230439 DOI：
摘要
为解决高比例新能源接入背景下无功补偿不充分的问题
提出了一种计及风电场无功支撑性能的多目标优化调度策略。从电力系统运行的电压稳定性、无功裕度安全性和功率损耗经济性方面分析了风电场无功支撑性能
构建了考虑多指标满意度区间的二次函数组
建立了系统多区间动态优化模型;同时
针对无功优化调度问题的非线性、多约束等特征
提出了自适应混沌差分磷虾群算法(A-CDKH);最后
通过修改的IEEE30节点模型和某实际风电场模型上的仿真结果证明了所提策略的优势性及有效性。结果表明:相比于多目标模糊优化模型
采用多目标动态优化模型所求得的电压偏差指标最高可达到32.99%的优化程度;在电压偏差指标上
A-CDKH相比于其他算法最多能优化75.94%。
Abstract
In order to solve the problem of inadequate reactive power compensation under the background of high proportion of new energy resources access
a multi-objective optimal dispatch strategy considering reactive power support performance of wind farm was proposed. The reactive power support performance of wind farm was analyzed from the aspects of voltage stability
reactive power margin safety and power loss economy of the power system operation. A quadratic function group considering multi-objective satisfaction interval was constructed
and a multi-interval dynamic optimization model of the system was established. The adaptive chaotic differential krill herd algorithm (A-CDKH) was proposed to address the nonlinear and multi-constraint characteristics of the reactive power optimal dispatch problem. Finally
the advantages and effectiveness of the proposed strategy were proved through the simulation results of the modified IEEE30-node model and an actual wind farm model. Results show that
compared with the multi-objective fuzzy optimization model
the maximum voltage deviation index obtained by the multi-objective dynamic optimization model can reach 32.99% optimization degree. The A-CDKH can optimize 75.94% compared with other algorithms in the voltage deviation index.
关键词
Keywords
references
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