<trans-title>Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

Zhongfu WANG

doi:10.16516/j.gedi.issn2095-8676.2021.04.013

SOUTHERN ENERGY CONSTRUCTION > 2021 > 8(4): 95-106. > DOI: 10.16516/j.gedi.issn2095-8676.2021.04.013 CSTR: 32391.14.j.gedi.issn2095-8676.2021.04.013

WANG Zhongfu.Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling[J].Southern Energy Construction,2021,08(04):95-106.. DOI: 10.16516/j.gedi.issn2095-8676.2021.04.013

Citation:

Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

Zhongfu WANG

China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China

More Information

Received Date: May 09, 2021
Revised Date: June 09, 2021

Abstract

Abstract

Introduction New energy power generation is intermittent and random, and its power is uncertain data, which will cause changes in grid voltage and frequency, thus pose a threat to the safe operation of the power system. In order to ensure the safety of grid voltage after large-scale new energy grid connection, considering the uncertainty of new energy generation, a reactive power optimization strategy of power grid incorporating renewable energy based on interval modeling is proposed.
Method This strategy used interval to describe uncertain parameters in reactive power optimization model, and then established interval reactive power optimization model. The interval power flow algorithm based on optimization scenario was used to solve the interval power flow equation, thus obtaining the interval of state variables and determining the feasibility of control variables. On this basis, the improved particle swarm optimization algorithm was used to solve the interval reactive power optimization model, and the local search method and discrete variable cross-processing operation were added to the particle swarm optimization algorithm to improve optimization ability. In order to verify the effectiveness and superiority of the proposed method, IEEE 14 - bus and IEEE 30 - bus examples were used for simulation, and the proposed algorithm was compared with the adaptive genetic algorithm and the ordinary particle swarm optimization algorithm.
Result The simulation results show that compared with adaptive genetic algorithm and ordinary particle swarm optimization algorithm, the improved particle swarm interval reactive power optimization strategy has a faster convergence speed, stronger optimization capabilities, and can effectively solve the discrete variables in the model.
Conclusion Our data suggest that the proposed strategy can effectively solve the interval reactive power optimization problem and ensure the operation safety of grid voltage after large-scale new energy grid connection.

FullText(HTML)

李辉

WANG Zhongfu.Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling[J].Southern Energy Construction,2021,08(04):95-106.

References (19)

References

[1]	MOKARIM, MORADIM H. Security constraint optimal reactive power dispatch under uncertainty in a wind integrated power system [C]//Politecnico di Torino. International Universities Power Engineering Conference, Turin, Italy, September 1-4, 2020. Turin: IEEE, 2020: 1-6.
[2]	DINGT, YANGQ, YANGY, et al. A data-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method [J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4994-5004.
[3]	SARASWATA, UCHENIYAR, GUPTAY. Two-stage stochastic optimization for reactive power dispatch with wind power uncertainties [C]//Amity University. International Conference on Computation, Automation and Knowledge Management, Dubai, United Arab Emirates, January 9-10, 2020. Dubai: IEEE, 2020: 332-337.
[4]	KEERIOM U, ALIA, SALEEMM, et al. Multi-objective optimal reactive power dispatch considering probabilistic load demand along with wind and solar power integration [C]//ACM Singapore Chapter. International Conference on Smart Power & Internet Energy Systems, Bangkok, Thailand, September 15-18, 2020. Bangkok: IEEE, 2020: 502-507.
[5]	GAOH, WANGJ, LIUY, et al. An improved ADMM-based distributed optimal operation model of AC/DC hybrid distribution network considering wind power uncertainties [J]. IEEE Systems Journal, 2020, PP(99): 1-11.
[6]	LIUS, DINGT, BIEZ, et al. A second order cone based relaxation and decomposition algorithm for multi-period reactive power optimization considering uncertain PV integration in active distribution networks [C]//International Association on Environment and Electrical Engineering. IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe, Milan, Italy, June 6-9, 2017. Milan: IEEE, 2017: 1-6.
[7]	ZHANGC, CHENH, LIANGZ, et al. Reactive power optimization under interval uncertainty by the linear approximation method and its modified method [J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4587-4600.
[8]	ZHANGC, CHENH, SHIK, et al. A multi-time reactive power optimization under interval uncertainty of renewable power generation by an interval sequential quadratic programming method [J]. IEEE Transactions on Sustainable Energy, 2019, 10(3): 1086-1097.
[9]	ZHANGC, CHENH, HUAD, et al. Solution of interval reactive power optimization model through defining security limits [C]//Chinese Society for Electrical Engineering. International Conference on Power System Technology, Guangzhou, China, November 6-8, 2018. Guangzhou: IEEE, 2018: 1384-1389.
[10]	ZHANGC, CHENH, NGANH, et al. Solution of reactive power optimisation including interval uncertainty using genetic algorithm [J]. Iet Generation Transmission & Distribution, 2017, 11(15): 3657-3664.
[11]	欧阳森, 杨家豪, 安晓华, 等. 基于时段解耦的含特殊负荷的配电网动态无功优化 [J]. 华南理工大学学报(自然科学版), 2016, 44(2): 97-106. OUYANGS, YANGJ H, ANX H, et al. Dynamic reactive power optimization of distribution network containing special load based on time decoupling [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(2): 97-106.
[12]	高峰, 赵莹, 朱伟民, 等. 基于区间算术的含光伏电站的电网动态无功优化 [J]. 电气应用, 2016, 35(6): 37-41. GAOF, ZHAOY, ZHUW M, et al. Dynamic reactive power optimization of power grid with photovoltaic power station based on interval algorithm [J]. Electrotechnical Application, 2016, 35(6): 37-41.
[13]	张晓英, 张艺, 王琨, 等. 基于改进NSGA-Ⅱ算法的含分布式电源配电网无功优化 [J]. 电力系统保护与控制, 2020, 48(1): 55-64. ZHANGX Y, ZHANGY, WANGK, et al. Reactive power optimization of distribution network with distributed generations based on improved NSGA-II algorithm [J]. Power System Protection and Control, 2020, 48(1): 55-64.
[14]	黄博, 曾驰宇, 王全胜, 等. 一种求解电力系统无功优化的改进粒子群算法 [J]. 电工技术, 2020(12): 107-109. HUANGB, ZENGC Y, WANGQ S, et al. An improved particle swarm optimization algorithm for reactive power optimization of power system [J]. Electric Engineering, 2020(12): 107-109.
[15]	苏福清, 匡洪海, 钟浩, 等. 基于柯西变异改进粒子群算法的无功优化 [J]. 电气工程学报, 2021, 16(1): 55-61. SUF Q, KUANGH H, ZHONGH, et al. Reactive power optimization based on Cauchy mutation and improved adaptive particle swarm optimization [J]. Journal of Electrical Engineering, 2021, 16(1): 55-61.
[16]	邓长虹, 马庆, 肖永, 等. 基于自学习迁移粒子群算法及高斯罚函数的无功优化方法 [J]. 电网技术, 2014, 38(12): 3341-3346. DENGC H, MAQ, XIAOY, et al. Reactive power optimization based on self-learning migration particle swarm optimization and Gaussian penalty function [J]. Power System Technology, 2014, 38(12): 3341-3346.
[17]	张勇军, 苏杰和, 羿应棋. 基于区间算术的含分布式电源电网无功优化方法 [J]. 电力系统保护与控制, 2014, 42(15): 21-26. ZHANGY J, SUJ H, YIY Q. Reactive power optimization based on interval arithmetic with distributed power grid [J]. Power System Protection and Control, 2014, 42(15): 21-26.
[18]	ZHANGC, CHENH, SHIK, et al. An interval power flow analysis through optimizing-scenarios method [J]. IEEE Transactions on Smart Grid, 2018, 9(5): 5217-5226.
[19]	SHIY, EBERHARTR. A modified particle swarm optimizer [C]//Institute of Electrical and Electronics Engineers. IEEE International Conference on Evolutionary Computation Proceedings, Anchorage, Alaska, May 4-9, 1998. Alaska: IEEE, 2002: 69-73.

[1]	LU Haotian, LIU Shaopeng, WANG Kai. Capacity Optimization Method for Photovoltaic Hydrogen Production Systems Based on Multi-Objective Particle Swarm Algorithm[J]. SOUTHERN ENERGY CONSTRUCTION, 2025, 12(3): 133-143. DOI: 10.16516/j.ceec.2024-373
[2]	ZHANG Cheng, CHEN Yu, LI Can, LU Liangshuai, XIA Zhenglong. Reactive Power Optimization of Distribution Network Considering the Reactive Power Output of Doubly Fed Induction Generator[J]. SOUTHERN ENERGY CONSTRUCTION, 2025, 12(1): 168-176. DOI: 10.16516/j.ceec.2024-293
[3]	LIU Junwei, LIANG Zhanhong, LIU Zhanzhi, ZHONG Jiefeng, ZHANG Zifan. Optimization Calculation Method of Injection Voltage and Series Converter Capacity for Unified Power Flow Controller[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(5): 157-165. DOI: 10.16516/j.gedi.issn2095-8676.2023.05.019
[4]	ZHANG Dongqing, XU Lingling, LI Yanlong, ZHANG Guohua. LCC-HVDC Converter Station Reactive Power Optimization and Two-Layer Coordination Strategy Research Based on Synchronous Sondenser[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(5): 24-33. DOI: 10.16516/j.gedi.issn2095-8676.2023.05.004
[5]	Jianzhao LI, Min XIE, Shujia LI, Shengzhen LIN, Binbin HUANG. Optimal Dispatch of Unit Commitment and Multi-Scenario Reserve Decision Considering CVaR[J]. SOUTHERN ENERGY CONSTRUCTION, 2021, 8(4): 50-65. DOI: 10.16516/j.gedi.issn2095-8676.2021.04.008
[6]	Qiang LI. Optimization and Improvement of Frequency Modulation Auxiliary Service Control System for the 700 MW Unit[J]. SOUTHERN ENERGY CONSTRUCTION, 2021, 8(3): 114-121. DOI: 10.16516/j.gedi.issn2095-8676.2021.03.017
[7]	Luyao PEI. Design and Scenario Verification of Grid Management System in Power Grid Enterprise[J]. SOUTHERN ENERGY CONSTRUCTION, 2020, 7(4): 81-86. DOI: 10.16516/j.gedi.issn2095-8676.2020.04.012
[8]	Liag CHEN, Xuan CAI, Ying ZHANG, Chao XU, Shuang CHEN. Dust Sampling Method Optimization at Low Concentration of Super Low Emission[J]. SOUTHERN ENERGY CONSTRUCTION, 2017, 4(1): 105-108. DOI: 10.16516/j.gedi.issn2095-8676.2017.01.020
[9]	LUN Zhenjian, HU Ke, GUO Jinchuan, GUO Fang. Research on BESS Capacity Optimization Methods in Micro-grid[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(S1): 5-9. DOI: 10.16516/j.gedi.issn2095-8676.2015.S1.002
[10]	Jian LI, Bin MA, Zhihua ZHANG, Yu LIN, Xiaofang YANG. Research on a Grid-based Optimal Planning Method for Urban Distribution System[J]. SOUTHERN ENERGY CONSTRUCTION, 2015, 2(3): 38-42. DOI: 10.16516/j.gedi.issn2095-8676.2015.03.007

Cited By

Cited by

Periodical cited type(2)

1.	李庆豪，李晓莉，郭志达，陈远欣，姚远湘，赵岭，王翎羽. 基于ADWOA算法的风光水互补系统优化调度研究. 电力大数据. 2024(02): 69-76 .
2.	张智光，倪秋龙，廖培，石博隆，吴振杰，李城达，许大卫. 计及新能源消纳的地区电网无功电压优化控制策略. 浙江电力. 2023(01): 46-53 .

Other cited types(5)

Zhongfu WANG, wangzhongfu@gedi.com.cn

China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China

Figures(12) / Tables(7)

Get Citation

XML

Article Metrics

Article views (935) PDF downloads (40) Cited by(7)

Turn off MathJax

Article Contents

Abstract

李辉

References

Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

Abstract