樽海鞘群算法在电力系统最优潮流计算中的应用

张凡, 王雷, 赵娟, 吴磊

分布式能源 ›› 2021, Vol. 6 ›› Issue (1) : 35-43.

PDF(1545 KB)
PDF(1545 KB)
分布式能源 ›› 2021, Vol. 6 ›› Issue (1) : 35-43. DOI: 10.16513/j.2096-2185.DE.2012004
学术研究

樽海鞘群算法在电力系统最优潮流计算中的应用

作者信息 +

Application of Salp Swarm Algorithm in Optimal Power Flow Calculation for Power System

Author information +
文章历史 +

摘要

领导者比例的选取对樽海鞘群算法(salp swarm algorithm,SSA)求解电力系统最优潮流问题的计算结果具有较大的影响。以网损、电压偏移、发电成本、电压稳定度为目标,研究了领导者取种群中最优个体或取种群中适应度较好的前10%~50%的个体对算法求解单目标及多目标最优潮流问题优化效果的影响。对IEEE 30节点系统和IEEE 118节点系统的最优潮流计算表明,樽海鞘群算法中的领导者取为种群中适应度较好的前20%~40%个体时,同算法原有的领导者取种群中最优个体的更新策略相比,可以获得更好的最优潮流求解结果。该文的研究成果可为樽海鞘群算法用于最优潮流相关问题提供借鉴参考。

Abstract

Ratio of leaders in salp swarm algorithm(SSA) has significant influence on the optimal power flow calculation results of power system. Setting the network loss, voltage deviation, generation cost and voltage stability as the objective functions, the different optimization effects of SSA in solving single objective and multi-objective optimal power flow problems was studied when the leader of the algorithm is taken as the best individual of the total population or the ratio of leaders is taken as the top 10%~50% of the total population.The calculation results of IEEE 30-bus system and IEEE 118-bus system show that SSA can obtain better optimization results when the ratio of leaders is taken as the top 20%~40% of the total population. This research can provide some reference for the application of SSA in optimal power flow calculation.

关键词

樽海鞘群算法(SSA) / 最优潮流 / 领导者比例 / 优化效果

Key words

salp swarm algorithm(SSA) / optimal power flow / ratio of leaders / optimization effect

引用本文

导出引用
张凡, 王雷, 赵娟, . 樽海鞘群算法在电力系统最优潮流计算中的应用[J]. 分布式能源. 2021, 6(1): 35-43 https://doi.org/10.16513/j.2096-2185.DE.2012004
Fan ZHANG, Lei WANG, Juan ZHAO, et al. Application of Salp Swarm Algorithm in Optimal Power Flow Calculation for Power System[J]. Distributed Energy Resources. 2021, 6(1): 35-43 https://doi.org/10.16513/j.2096-2185.DE.2012004
中图分类号: TM744   

参考文献

[1]
DOMMEL H W, TINNEY W F. Optimal power flow solutions[J]. IEEE Transactions on Power Apparatus and Systems, 1968, PAS-87 (10): 1866-1876.
[2]
SUN D I, ASHLEY B, BREWER B, et al. Optimal power flow by newton approach[J]. IEEE Transactions on Power Apparatus and Systems, 1984, PAS-103(10): 2864-2880.
[3]
LEE K. Y, PARK Y M, ORTIZ J L. A united approach to optimal real and reactive power dispatch[J]. IEEE Transactions on Power Apparatus and Systems, 1985, PAS-104(5): 1147-1153.
[4]
MOTA-PALOMINO R, QUINTANA V H. Sparse reactive power scheduling by a penalty function-linear programming technique[J]. IEEE Transactions on Power Systems, 1986, 1(3): 31-39.
[5]
HABIBOLLAHZADEH H, LUO G X, SEMLYEN A, et al. Hydrothermal optimal power flow based on a combined linear and nonlinear programming methodology[J]. IEEE Transactions on Power Systems, 1989, 4(2): 530-537.
[6]
YAN X, QUINTANA V H. Improving an interior-point-based OPF by dynamic adjustments of step sizes and tolerances-discussion[J]. IEEE Transactions on Power Systems, 1999, 14(2): 709-717.
[7]
LAI L L, MA J T, YOKOYAMA R, et al. Improved genetic algorithms for optimal power flow under both normal and contingent operatiosn states[J]. International Journal of Electrical Power & Energy Systems, 1997, 19(5): 287-292.
[8]
ABIDO M A. Optimal power flow using particle swarm optimization[J]. International Journal of Electrical Power & Energy Systems, 2002, 24(7): 563-571.
[9]
ELA A A A E, ABIDO M A, SPEA S R. Optimal power flow using differential evolution algorithm[J]. Electrical Engineering, 2010, 80(7): 878-885.
[10]
BOUCHEKARA H R E H. Optimal power flow using black-hole-based optimization approach[J]. Applied Soft Computing, 2014, 24: 879-888.
[11]
MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114: 163-191.
[12]
王彦军,王秋萍,王晓峰. 改进的樽海鞘群算法及在焊接梁问题中的应用[J]. 西安理工大学学报2019, 35(4): 484-493.
WANG Yanjun, WANG Qiuping, WANG Xiaofeng. An improved salp swarm algorithm and its application to welding beam problem[J]. Journal of Xi'an University of Technology, 2019, 35(4): 484-493.
[13]
余跃,王宏伦. 一种高超声速飞行器再入轨迹优化方法[J]. 宇航学报2020(7): 926-936.
YU Yue, WANG Honglun. A reentry trajectory optimization method for hypersonic vehicles[J]. Journal of Astronautics, 2020(7): 926-936.
[14]
郭立民,谢强,陈涛,等. 运动单站单基线解模糊定位算法[J]. 中南大学学报(自然科学版), 2019, 50(2): 336-342.
GUO Limin, XIE Qiang, CHEN Tao, et al. A single moving observer ambiguity resolution locating algorithm using single baseline interferometer[J]. Journal of Central South University (Science and Technology), 2019, 50(2): 336-342.
[15]
杨博,钟林恩,朱德娜,等. 部分遮蔽下改进樽海鞘群算法的光伏系统最大功率跟踪[J]. 控制理论与应用2019, 36(3): 339-352.
YANG Bo, ZHONG Linen, ZHU Dena, et al. Modified salp swarm algorithm based maximum power point tracking of power-voltage system under partial shading condition[J]. Control Theory & Applications, 2019, 36(3): 339-352.
[16]
王明超,董佳圆,李继影,等. 基于ISSA的STATCOM模型参数解耦辨识研究[J]. 东北电力大学学报2020, 40(1): 81-89.
WANG Mingchao, DONG Jiayuan, LI Jiying, et al. Research on parameter decoupling identification of STATCOM model based on ISSA[J]. Journal of Northeast Electric Power University, 2020, 40(1): 81-89.
[17]
林国营,卢世祥,郭昆健,等. 基于主从博弈的电网公司需求响应补贴定价机制[J]. 电力系统自动化2020, 44(10): 59-67.
LIN Guoying, LU Shixiang, GUO Kunjian, et al. Stackelberg game based incentive pricing mechanism of demand response for power grid corporatiosns[J]. Automation of Electric Power Systems, 2020, 44(10): 59-67.
[18]
GUVENC U, KAYMAZ E, DUMAN S. Economic dispatch of power system including wind power using salp swarm algorithm[C]//7th International Conference on Advanced Technologies (ICAT'18). Antalya, TURKEY: Power System Optimization, 2018.
[19]
CHAUDHARY V, DUBEY H M, PANDIT M, et al. Multi-area economic dispatch with stochastic wind power using Salp Swarm Algorithm[J]. Array, 2020, 8: 100044.
[20]
杨蕾,吴琛,黄伟,等. 含高比例风光新能源电网的多目标无功优化算法[J]. 电力建设2020, 41(7): 100-109.
YANG Lei, WU Chen, HUANG Wei, et al. Pareto-based multi-objective reactive power optimization for power grid with high-penetratiosn wind and solar renewable energies[J]. Electric Power Construction, 2020, 41(7): 100-109.
[21]
张衍. 电压稳定在线监控的最小奇异值指标[C]//中国电机工程学会年会. 成都:中国电机工程学会,2013: 1-5.
ZHANG Yan. Minimum singular value index for voltage stability online monitoring[C]//CSEE Annual Meeting. Chengdu: CSEE, 2013: 1-5.
[22]
李鸿鑫,李银红,陈金富,等. 自适应选择进化算法的多目标无功优化方法[J]. 中国电机工程学报2013, 33(10): 71-78.
LI Hongxin, LI Yinhong, CHEN Jinfu, et al. Multiple evolutionary algorithms with adaptive selection strategies for multi-objective optimal reactive power flow[J]. Proceedings of CSEE, 2013, 33(10): 71-78.
[23]
张凡,王雷,赵娟,等. 粒子群算法种群规模和迭代次数对系统优化效果的影响研究[J]. 青海电力2020(2): 12-20.
ZHANG Fan, WANG Lei, ZHAO Juan, et al. Influence of population size and number of iteratiosns of PSO on the optimization effect of power system[J]. Qinghai Electric Power, 2020(2): 12-20.

基金

国家电网公司科技项目(5200-201956111A-0-0-00)

PDF(1545 KB)

Accesses

Citation

Detail

段落导航
相关文章

/