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

Fan ZHANG; Lei WANG; Juan ZHAO; Lei WU

doi:10.16513/j.2096-2185.DE.2012004

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Distributed Energy ›› 2021, Vol. 6 ›› Issue (1) : 35-43. DOI: 10.16513/j.2096-2185.DE.2012004

Basic Research

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

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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.

Key words

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

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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

References

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

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. Cited in this article [2]

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. Cited in this article [1]

[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. Cited in this article [1]

[8]	ABIDO M A. Optimal power flow using particle swarm optimization[J]. International Journal of Electrical Power & Energy Systems, 2002, 24(7): 563-571. Cited in this article [1]

[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. Cited in this article [1]

[10]	BOUCHEKARA H R E H. Optimal power flow using black-hole-based optimization approach[J]. Applied Soft Computing, 2014, 24: 879-888. Cited in this article [1]

[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. Cited in this article [2]

[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. Cited in this article [2]

[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. Cited in this article [1]

[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.

Cited in this article [2]

[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

, 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.

Cited in this article [1]

[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. Cited in this article [1]

[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.

Cited in this article [1]

[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. Cited in this article [2]

[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. Cited in this article [2]

[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.

Cited in this article [2]

[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. Cited in this article [1]

[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.

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.

Cited in this article [1]

[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. Cited in this article [2]