Welcome to Francis Academic Press

Academic Journal of Engineering and Technology Science, 2023, 6(6); doi: 10.25236/AJETS.2023.060602.

Research on second-order cone relaxation algorithm for multi-time linearization of distribution networks with distributed power sources

Author(s)

Zhu Meiyuan, Ai Yongle

Corresponding Author:
Zhu Meiyuan
Affiliation(s)

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan, 454150, China

Abstract

Distributed power supply connected to distribution network brings impact on system voltage distribution as well as tidal current analysis, which causes degradation of system power quality. For this reason, a system optimization model is needed to analyze and optimize system voltage and tidal current. The linearized second-order cone relaxation optimization algorithm for distribution networks with distributed power sources is proposed. First, the distribution network branch tide model is introduced to analyze the tide of the distribution network with distributed power supply access; based on this, the distributed distribution network tide optimization model is established. Secondly, the distributed distribution network tidal optimization model is simplified, and the multi-time second-order cone relaxation optimization algorithm is proposed for the non-convex nonlinearity in tidal analysis, and the segmental linearization is performed for the non-convex nonlinearity of capacitor bank and on-load regulator transformer. Finally, a modified IEEE33 node test system is used on the MATLAB simulation platform to verify the tidal optimization of this distributed distribution network tidal optimization system. The simulation results show that the tidal optimization algorithm can reasonably dispatch the output of on-load regulating transformers, capacitor banks and distributed power sources, effectively reduce the network loss and the voltage deviation of the grid.

Keywords

optimal power flow; mixed integer linear programming; segmented linear programming; multi-period SOCP algorithm

Cite This Paper

Zhu Meiyuan, Ai Yongle. Research on second-order cone relaxation algorithm for multi-time linearization of distribution networks with distributed power sources. Academic Journal of Engineering and Technology Science (2023) Vol. 6, Issue 6: 5-16. https://doi.org/10.25236/AJETS.2023.060602.

References

[1] Zhou L, Wang J, Xie L, et al. Multi-objective reactive power optimization of distribution network with distributed generation [J].Journal of Chongqing University of Posts and Telecommunications ( Natural Science Edition ), 2022,34 ( 05 ) : 818-827.

[2] Sun S Q, Wu C Y, Yan W L, et al. Optimal power flow calculation based on particle swarm with random decay factor [J]. Power System Protection and Control, 2021, 49(13): 43-52.

[3] Rong Y J, Feng H C, Song L W, et al. Distribution grid reactive power optimization based on adaptive genetic algorithm containing photovoltaic power supply [J]. Electrical and Energy Efficiency Management Technology, 2020 (11): 85-91.

[4] Zhang R, Li T C, Li X M, et al. Distributed voltage reactive power optimization in distribution networks considering equipment action losses [J]. Power System Protection and Control, 2021, 49(24): 31-40.

[5] Lin Z, Hu Z C, Song Y H. A review of convex relaxation techniques for optimal flow problems [J]. Chinese Journal of Electrical Engineering, 2019, 39(13): 3717-3728.

[6] Zhang X, Yao L, Chen C, et al. A two-stage robust optimization model for hybrid AC-DC distribution network reconfiguration with reactive power optimization synergy [J]. Power Grid Technology, 2022, 46(03): 1149-1162. 

[7] Xu T R, Ding T, Li L, et al. A second-order cone relaxation model for adapting reactive power optimization in three-phase unbalanced active distribution networks [J]. Power System Automation, 2021, 45(24):81-88.

[8] Z. Yang, A. Bose, H. Zhong, etc. “Optimal reactive power dispatch with accurately modeled discrete control devices: a successive linear approximation approach,” IEEE Trans. Power Syst, vol. 32, no. 3, pp. 2435–2444, 2017.

[9] Gao J L, Song S, Wang L Y, et al. Reactive power optimization of active distribution network based on improved krill herd algorithm [J].Journal of Jilin University ( Information Science Edition ), 2022,40 (06) : 954-962.