A Dynamic Reactive Power Optimization Algorithm for Regional Power Grid Based on Decoupling Interior Point Method and Mixed Integer Programming Method

doi:10.11930/j.issn.1004-9649.202106051

Abstract

Abstract: Dynamic reactive power optimization plays an important role in improving the voltage quality of power grid, decreasing the network loss and reducing the daily action times of discrete voltage regulators. Mathematically, it is a multi-period large-scale nonlinear mixed integer programming problem with absolute value constraints, and its efficient solution is a difficult problem. Therefore, this paper proposes a two-stage dynamic reactive power optimization algorithm based on decoupling interior point method and mixed integer programming. Firstly, the sigmoid function is used to deal with the absolute value constraint to realize the continuity of the original model, and the idea of decoupling interior point method is used to construct the diagonal band edge structure of the KKT modified equation, so as to realize the time block decoupling and efficient solution of the model. Secondly, the original model is linearized near the current continuous solution, and a mixed integer linear programming model involving all constraints of the original model is constructed, so as to determine the optimal solution of the discrete reactive power control equipment. The effectiveness of the proposed algorithm is verified through simulation of a 26 bus example.

Key words: dynamic reactive power optimization, mixed integer nonlinear programming, decoupling interior point method, mixed integer linear programming

ZHANG Jie, ZHENG Yunyao, LIU Shengchun, MA Yongfei, YAN Wei, WANG Hengfeng. A Dynamic Reactive Power Optimization Algorithm for Regional Power Grid Based on Decoupling Interior Point Method and Mixed Integer Programming Method[J]. Electric Power, 2023, 56(1): 112-118.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202106051

https://www.electricpower.com.cn/EN/Y2023/V56/I1/112

References

[1] MOHSENI-BONAB S M, RABIEE A, MOHAMMADI-IVATLOO B, et al. A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem[J]. International Journal of Electrical Power & Energy Systems, 2016, 75: 194–204.
[2] CAN?IZARES C A, BHATTACHARYA K, EL-SAMAHY I, et al. Re-defining the reactive power dispatch problem in the context of competitive electricity markets[J]. IET Generation, Transmission & Distribution, 2010, 4(2): 162.
[3] LU F C, HSU Y Y. Fuzzy dynamic programming approach to reactive power/voltage control in a distribution substation[J]. IEEE Transactions on Power Systems, 1997, 12(2): 681–688.
[4] LIANG R H, CHENG C K. Dispatch of main transformer ULTC and capacitors in a distribution system[J]. IEEE Transactions on Power Delivery, 2001, 16(4): 625–630.
[5] LIU Y T, ZHANG P, QIU X Z. Optimal volt/var control in distribution systems[J]. International Journal of Electrical Power & Energy Systems, 2002, 24(4): 271–276.
[6] 刘明波, 朱春明, 钱康龄, 等. 计及控制设备动作次数约束的动态无功优化算法[J]. 中国电机工程学报, 2004, 24(3): 34–40
LIU Mingbo, ZHU Chunming, QIAN Kangling, et al. Dynamic reactive-power optimization algorithm incorporating action number constraints of control devices[J]. Proceedings of the CSEE, 2004, 24(3): 34–40
[7] 赖永生, 刘明波. 电力系统动态无功优化问题的快速解耦算法[J]. 中国电机工程学报, 2008, 28(7): 32–39
LAI Yongsheng, LIU Mingbo. Fast decomposition algorithm for solution of dynamic reactive power optimization problem in power systems[J]. Proceedings of the CSEE, 2008, 28(7): 32–39
[8] 李子健, 郭佩乾, 马宁宁, 等. 融合双重策略粒子群算法的分布式电源配网无功优化[J]. 南方电网技术, 2022, 16(6): 14–22, 81
LI Zijian, GUO Peiqian, MA Ningning, et al. Reactive power optimization for distribution system with DG by particle swarm optimization algorithm integrating dual strategies[J]. Southern Power System Technology, 2022, 16(6): 14–22, 81
[9] 崔挺, 李雪萍, 颜畅, 等. 基于模型预测控制的风电场故障穿越有功无功优化控制策略[J]. 电力系统保护与控制, 2022, 50(2): 12–20
CUI Ting, LI Xueping, YAN Chang, et al. Active and reactive power optimization control strategy for wind farm fault ride-through based on model predictive control[J]. Power System Protection and Control, 2022, 50(2): 12–20
[10] 王文宾, 石磊磊, 贾清泉, 等. 低压光伏集群无功运行模式优化[J]. 电力科学与技术学报, 2021, 36(1): 117–126
WANG Wenbin, SHI Leilei, JIA Qingquan, et al. Study on reactive power operation mode optimization of low voltage photovoltaic cluster[J]. Journal of Electric Power Science and Technology, 2021, 36(1): 117–126
[11] 王韶, 张煜成, 周鑫, 等. 基于一种改进蚁群算法的动态无功优化[J]. 电力系统保护与控制, 2012, 40(17): 100–104, 109
WANG Shao, ZHANG Yucheng, ZHOU Xin, et al. Dynamic reactive power optimization based on a modified ant colony algorithm[J]. Power System Protection and Control, 2012, 40(17): 100–104, 109
[12] 杨亘烨, 孙荣富, 丁然, 等. 计及光伏多状态调节能力的配电网多时间尺度电压优化[J]. 中国电力, 2022, 55(3): 105–114
YANG Genye, SUN Rongfu, DING Ran, et al. Multi time scale reactive power and voltage optimization of distribution network considering photovoltaic multi state regulation capability[J]. Electric Power, 2022, 55(3): 105–114
[13] 沈茂亚, 丁晓群, 王宽, 等. 自适应免疫粒子群算法在动态无功优化中应用[J]. 电力自动化设备, 2007, 27(1): 31–35
SHEN Maoya, DING Xiaoqun, WANG Kuan, et al. Application of adaptive immune PSO in dynamic reactive power optimization[J]. Electric Power Automation Equipment, 2007, 27(1): 31–35
[14] 颜伟, 田甜, 张海兵, 等. 考虑相邻时段投切次数约束的动态无功优化启发式策略[J]. 电力系统自动化, 2008, 32(10): 71–75
YAN Wei, TIAN Tian, ZHANG Haibing, et al. Heuristic strategy for dynamic reactive power optimization incorporating action time constraints between adjacent time intervals[J]. Automation of Electric Power Systems, 2008, 32(10): 71–75
[15] MANTOVANI J R S, MODESTO S A G, GARCIA A V. VAr planning using genetic algorithm and linear programming[J]. IEE Proceedings-Generation, Transmission and Distribution, 2001, 148(3): 257.
[16] 陈建华, 阎帅, 张瑶, 等. 基于IPM-intPSO的两阶段动态无功优化算法[J]. 电力自动化设备, 2020, 40(3): 174–180
CHEN Jianhua, YAN Shuai, ZHANG Yao, et al. Two-stage dynamic reactive power optimization algorithm based on IPM-intPSO[J]. Electric Power Automation Equipment, 2020, 40(3): 174–180
[17] 陈倩, 王维庆, 王海云. 基于需求侧响应的主动配电网双层优化方法[J]. 电力系统保护与控制, 2022, 50(16): 1–13
CHEN Qian, WANG Weiqing, WANG Haiyun. Bi-level optimization model of an active distribution network based on demand response[J]. Power System Protection and Control, 2022, 50(16): 1–13
[18] 林少华, 吴杰康, 莫超, 等. 基于二阶锥规划的含分布式电源配电网动态无功分区与优化方法[J]. 电网技术, 2018, 42(1): 238–246
LIN Shaohua, WU Jiekang, MO Chao, et al. Dynamic partition and optimization method for reactive power of distribution networks with distributed generation based on second-order cone programming[J]. Power System Technology, 2018, 42(1): 238–246
[19] 孙田, 邹鹏, 杨知方, 等. 动态无功优化的多阶段求解方法[J]. 电网技术, 2016, 40(6): 1804–1810
SUN Tian, ZOU Peng, YANG Zhifang, et al. A multi-stage solution approach for dynamic reactive power optimization[J]. Power System Technology, 2016, 40(6): 1804–1810
[20] 丁涛, 郭庆来, 柏瑞, 等. 松弛MPEC和MIQP的启发-校正两阶段动态无功优化算法[J]. 中国电机工程学报, 2014, 34(13): 2100–2107
DING Tao, GUO Qinglai, BAI Rui, et al. Two-stage heuristic-correction for dynamic reactive power optimization based on relaxation-MPEC and MIQP[J]. Proceedings of the CSEE, 2014, 34(13): 2100–2107
[21] 张进, 胡存刚, 芮涛. 基于交替方向乘子法的主动配电网日前两阶段分布式优化调度策略[J]. 中国电力, 2021, 54(5): 91–100
ZHANG Jin, HU Cungang, RUI Tao. A day-ahead two-stage distributed optimal scheduling method for active distribution network based on ADMM[J]. Electric Power, 2021, 54(5): 91–100
[22] YANG Z F, ZHONG H W, XIA Q, et al. Optimal power flow based on successive linear approximation of power flow equations[J]. IET Generation, Transmission & Distribution, 2016, 10(14): 3654–3662.
[23] 刘方. 关于电力系统动态最优潮流的几种模型与算法研究[D]. 重庆: 重庆大学, 2007.
LIU Fang. Study on some models and methods for dynamic optimal power flow of electrical power system[D]. Chongqing: Chongqing University, 2007.
[24] 余娟, 颜伟, 徐国禹, 等. 基于预测-校正原对偶内点法的无功优化新模型[J]. 中国电机工程学报, 2005, 25(11): 146–151
YU Juan, YAN Wei, XU Guoyu, et al. A new model of reactive optimization based on predictor corrector primal dual interior point method[J]. Proceedings of the CSEE, 2005, 25(11): 146–151
[25] XIE K, SONG Y H. Dynamic optimal power flow by interior point methods[J]. IEE Proceedings - Generation, Transmission and Distribution, 2001, 148(1): 76–84.