Bi-Level Reactive Power Optimization for Wind Cluster Integrating the Power Grid Considering the Reactive Power Potential of Wind Farm

Xiping MA; Wenxi ZHEN; Chen LIANG; Xiaoyang DONG; Yaxin LI

doi:10.13648/j.cnki.issn1674-0629.2026.07.010

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South Power Sys Technol ›› 2026, Vol. 20 ›› Issue (7) : 101-110. DOI: 10.13648/j.cnki.issn1674-0629.2026.07.010

Bi-Level Reactive Power Optimization for Wind Cluster Integrating the Power Grid Considering the Reactive Power Potential of Wind Farm

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Abstract

With the integration of large-scale wind power clusters into the power system, wind farms are increasingly involved in reactive power regulation of the power grid. Based on this, an upper level optimization model is first established to minimize the active power loss and voltage deviation in the power system. Subsequently, the model is solved to reduce system network loss and achieve safe and energy-saving operation of the power grid. Additionally, a detailed analysis of the wind power cluster is conducted at a lower level, estimating its reactive power potential. Taking the reactive power potential of the wind farm as the objective function and the reactive power output of each wind turbine unit as the optimization variable, this study coordinates the reactive power output within the wind farm cluster and employs an improved whale optimization algorithm to solve this bi-level optimization model. Finally, the proposed optimization strategy and algorithm are verified through case studies to effectively reduce network losses and voltage deviation in the power system while maximizing utilization of wind power′s reactive power potential.

Key words

multi-objective reactive power optimization / bi-level optimization / reactive power potential analysis / improved whale algorithm

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Xiping MA , Wenxi ZHEN , Chen LIANG , et al . Bi-Level Reactive Power Optimization for Wind Cluster Integrating the Power Grid Considering the Reactive Power Potential of Wind Farm[J]. Southern Power System Technology. 2026, 20(7): 101-110 https://doi.org/10.13648/j.cnki.issn1674-0629.2026.07.010

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	丁涛，牟晨璐，别朝红，等. 能源互联网及其优化运行研究现状综述［J］. 中国电机工程学报， 2018， 38（15）： 4318 - 4328. DING Tao， MOU Chenlu， BIE Zhaohong， et al. Review of energy internet and its operation［J］. Proceedings of the CSEE， 2018， 38（15）： 4318 - 4328. Cited in this article [1]

[2]	周孝信，鲁宗相，刘应梅，等. 中国未来电网的发展模式和关键技术［J］. 中国电机工程学报， 2014， 34（29）： 4999 - 5008. ZHOU Xiaoxin， LU Zongxiang， LIU Yingmei， et al. Development models and key technologies of future grid in China［J］. Proceedings of the CSEE， 2014， 34（29）： 4999 - 5008. Cited in this article [1]

[3]	韩肖清，李廷钧，张东霞，等. 双碳目标下的新型电力系统规划新问题及关键技术［J］. 高电压技术， 2021， 47（9）： 3036 - 3046. HAN Xiaoqing， LI Tingjun， ZHANG Dongxia， et al. New issues and key technologies of new power system planning under double carbon goals［J］. High Voltage Engineering， 2021， 47（9）： 3036 - 3046. Cited in this article [1]

[4]	TANG Z， DAVID J H， LIU T， et al. Hierarchical voltage control of weak subtransmission networks with high penetration of wind power［J］. IEEE Transactions on Power Systems， 2018， 33（1）： 187 - 197. Cited in this article [1]

[5]	白建华，辛颂旭，刘俊，等. 中国实现高比例可再生能源发展路径研究［J］. 中国电机工程学报， 2015， 35（14）： 3699 - 3705. BAI Jianhua， XIN Songxu， LIU Jun， et al. Roadmap of realizing the high penetration renewable energy in China［J］. Proceedings of the CSEE， 2015， 35（14）： 3699 - 3705. Cited in this article [1]

[6]	尹青，杨洪耕，马晓阳. 含大规模风电场的电网概率无功优化调度［J］. 电网技术， 2017， 41（2）： 514 - 520. YIN Qing， YANG Honggeng， MA Xiaoyang. Probabilistic reactive power optimization of power grid with large-scale wind farms［J］. Power System Technology， 2017， 41（2）： 514 - 520. Cited in this article [1]

[7]	YIN S L， WU L， SONG W， et al. Multi-objective reactive power optimisation approach for the isolated grid of new energy clusters connected to VSC-HVDC［J］. The Journal of Engineering， 2017（13）： 1024 - 1028. Cited in this article [1]

[8]	吴成明，邢博洋，李世春. 基于麻雀搜索算法的微电网分层优化调度［J］. 南方电网技术， 2024， 18（2）： 115 - 123. WU Chengming， XING Boyang， LI Shichun. Hierarchical optimal dispatch of microgrid based on the sparrow search algorithm［J］. Southern Power System Technology， 2024， 18（2）： 115 - 123. Cited in this article [1]

[9]	YANG M， LIU Y， GUO L， et al. Hierarchical distributed chance-constrained voltage control for HV and MV DNs based on nonlinearity-adaptive data-driven method［J］. IEEE Transactions on Power Systems， 2025， 40（1）： 806 - 819. Cited in this article [1]

[10]	LOPEZ J， GUBIA E， OLEA E， et al. Ride through of wind turbines with doubly fed induction generator under symmetrical voltage dips［J］. IEEE Transactions on Industrial Electronics， 2009， 56（10）： 4246 - 4254. Cited in this article [1]

[11]	ZOU X， ZHU D， HU J， et al. Mechanism analysis of the required rotor current and voltage for DFIG-based WTs to ride-through severe symmetrical grid faults［J］. IEEE Transactions on Power Electronics， 2018，33（9）： 7300 - 7304. Cited in this article [1]

[12]	SHEN Y， ZU W， LIANG L， et al. Comprehensive evaluation of the reactive power and voltage control capability of a wind farm based on a combined weighting method［J］. Power System Protection and Control， 2020， 48（14）： 18 - 24. Cited in this article [1]

[13]	LI Y， XU Z， ZHANG J， et al. Variable droop voltage control for wind farm［J］. IEEE Transactions on Sustainable Energy， 2018， 9（1）： 491 - 493. Cited in this article [1]

[14]	XU Y， DONG Z， ZHANG R， et al. Multi-timescale coordinated voltage/var control of high renewable-penetrated distribution systems［J］. IEEE Transactions on Power Systems， 2017， 32（6）： 4398 - 4408. Cited in this article [1]

[15]

李振坤，汪璇璇，时珊珊，等. 考虑微网间功率交互的配电网双层优化调度［J］. 南方电网技术， 2022， 16（9）： 107 - 118.

Zhenkun

， WANG

Xuanxuan

， SHI

Shanshan

， et al. Bi-level optimal dispatch of distribution network considering power interaction among microgrids［J］. Southern Power System Technology， 2022，16（9）： 107 - 118.

Cited in this article [1]

[16]	APPEN J V， STETZ T， BRAUN M， et al. Local voltage control strategies for PV storage systems in distribution grids［J］. IEEE Transactions on Smart Grid， 2014， 5（2）： 1002 - 1009. Cited in this article [1]

[17]	AGALGAONKAR Y P， PAL B C， JABR R A. Distribution voltage control considering the impact of PV generation on tap changers and autonomous regulators［J］. IEEE Transactions on Power Systems， 2014， 29（1）： 182 - 192. Cited in this article [1]

[18]	REN X， WANG H， WANG Z， et al. Reactive voltage control of wind farm based on tabu algorithm［J］. Frontiers in Energy Research， 2022（10）： 902623.1 - 902623.10. Cited in this article [1]

[19]	陆彬，高山，李德胜. 基于发电机运行实际的电力系统日前动态无功优化研究［J］. 发电技术， 2021， 42（1）： 122 - 130. LU Bin， GAO Shan， LI Desheng. Research on day ahead dynamic reactive power optimization based on generator operation［J］. Power Generation Technology， 2021， 42（1）： 122 - 130. Cited in this article [1]

[20]

虞伟，黄浩，金晨星，等. 基于多头自注意力特征变换和CNN-LSTM的超短期风电功率预测［J］. 南方电网技术，2026， 20（5）： 71 - 80.

Wei

， HUANG

Hao

， JIN

Chenxing

，et al.Ultra-short-term wind power forecasting based on multi-head self-attention feature transformation and CNN-LSTM［J］.Southern Power System Technology，2026，20（5）：71 - 80.

Cited in this article [1]

[21]	彭穗，李峰，王彦峰，等. 考虑极热极寒日的高比例风电电力系统备用容量规划［J］. 广东电力， 2025， 38（10）： 1 - 13. PENG Sui， LI Feng， WANG Yanfeng， et al.Reserve capacity planning for high-proportion wind power systems considering extremely hot and cold days［J］.Guangdong Electric Power， 2025， 38（10）： 1 - 13.

[22]

张柏林，李希德，魏博，等. 基于改进的场景分类和去粗粒化MCMC 的风电出力模拟方法［J］. 电测与仪表， 2024， 61（7）： 41 - 49.

ZHANG

Bolin

， LI

Xide

， WEI

， et al.Wind power output simulation method based on improved scene classification algorithm and coarse-grained MCMC［J］. Electrical Measurement & Instrumentation， 2024，61（7）： 41 - 49.

Cited in this article [1]