An evolutionary algorithm for Volt/Var control in an active distribution network with a deep learning surrogate mode

被引：0
|
作者：
Pan S. [1 ]
Liu Y. [1 ]
Tang Z. [1 ]
Zhang X. [1 ]
Qi H. [1 ]
Liu J. [1 ]
机构：
[1] College of Electrical Engineering, Sichuan University, Chengdu
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 17期
基金：
中国国家自然科学基金;
关键词：
active distribution network; highway neural networks; non-dominated sequencing genetic algorithm; surrogate-assisted model; three-phase unbalance; Volt/Var control;
D O I：
10.19783/j.cnki.pspc.211509
中图分类号：
学科分类号：
摘要：
The integration of large-scale distributed renewable energy sources brings new challenges to the active distribution network (ADN), including the three-phase imbalance problem, unexpected voltage violations and increased line losses. However, due to the incomplete installation of measurement equipment in the current distribution network, it is difficult to accurately obtain the load data of some nodes. Therefore, the traditional ADN voltage control method based on global observation is difficult to meet the actual control requirements. To solve these problems, a fast Volt/Var control (VVC) evolutionary algorithm with a deep learning surrogate model is proposed. In the development of the algorithm, a highway neural network is first designed as the surrogate model to accurately fit the mapping relationship between limited measured load information, voltage regulation control strategy and system performance indices. Then, the trained surrogate model is embedded into the iterative optimization process of the non-dominated sorting genetic algorithm, and the voltage deviation rate, three-phase unbalance degree and line losses indicators are directly calculated, and the data-driven distribution network VVC strategy can be quickly obtained. A modified IEEE 123-node three-phase distribution network is employed to verify the advantages and efficiency of the proposed algorithm. © 2022 Power System Protection and Control Press. All rights reserved.
引用
收藏
页码：97 / 106
页数：9
相关论文
共 33 条
  • [11] SU X, MASOUM M A S, WOLFS P J., Optimal PV inverter reactive power control ADN real power curtailment to improve performance of unbalanced four-wire LV distribution networks, IEEE Transactions on Sustainable Energy, 5, 3, pp. 967-977, (2014)
  • [12] DONG Lei, TIAN Aizhong, YU Ting, Et al., Reactive power optimization for distribution network with distributed generators based on mixed integer semi-definite programming, Automation of Electric Power Systems, 39, 21, pp. 66-72, (2015)
  • [13] HUAI Quan, HOU Xiaohu, HE Liangce, Et al., A method of state estimation for middle voltage and low voltage distribution network with distributed generations, Power System Protection and Control, 46, 21, pp. 69-77, (2018)
  • [14] LI Zhenkun, CHEN Siyu, FU Yang, Et al., Optimal allocation of ESS in distribution network containing DG based on timing-voltage-sensitivity analysis, Proceedings of the CSEE, 37, 16, pp. 4630-4640, (2017)
  • [15] WANG Shu, Research on multi-objective optimal placement of PMU in smart distribution network considering the accuracy of state estimation, (2019)
  • [16] LEAL-ROMO F, CHAVEZ-HURTADO J L, RAYAS-SANCHEZ J E., Selecting surrogate-based modeling techniques for power integrity analysis, 2018 IEEE MTT-S Latin America Microwave Conference (LAMC), (2018)
  • [17] HENNERON T, PIERQUIN A, CLENET S., Surrogate model based on the POD combined with the RBF interpolation of nonlinear magnetostatic FE model, IEEE Transactions on Magnetics, 56, 1, pp. 1-4, (2020)
  • [18] CUI Chenggang, HAO Huiling, YANG Ning, Et al., Scenario analysis based on the optimization Kriging model for solving unit commitment problems, Power System Protection and Control, 48, 22, pp. 49-56, (2020)
  • [19] DUAN Xiangxi, ZOU Wan, LI Yi, Et al., Data driven surrogate model-based operation quality control strategy of an urban transmission network, Power System Protection and Control, 49, 2, pp. 65-73, (2021)
  • [20] ZHANG Yujing, QIAO Ying, LU Zongxiang, Et al., Voltage control for partially visible distribution networks with high DG penetration, Power System Technology, 43, 5, pp. 1528-1535, (2019)
1234