Bi-level planning for low-carbon distribution networks based on an adaptive ε-constraint method considering OLTC fuzzy control

被引:0
|
作者
Yang X. [1 ]
Deng F. [1 ]
Zhang Z. [1 ]
Tang C. [1 ]
Hu Y. [1 ]
Deng Y. [1 ]
机构
[1] College of Information Engineering, Nanchang University, Nanchang
基金
中国国家自然科学基金;
关键词
adaptive ξ constraint method; bi-level planning; low carbon distribution network; OLTC; opportunity constraint programming;
D O I
10.19783/j.cnki.pspc.221840
中图分类号
学科分类号
摘要
The low-carbon development goal brings huge challenges to the distribution network, and a bi-level planning model for low-carbon distribution network, considering on-load tap changer fuzzy control, is proposed to reduce the carbon emission while ensuring power supply. The location of micro gas turbines, new energy sources, energy storage and capacitors is determined by network loss sensitivity. The bi-level model is used to fulfill low carbon planning. The planning layer is aimed at minimizing the comprehensive cost, considering the investment and operation cost of micro gas turbines, new energy sources, energy storage and capacitors, and the model is analyzed by the improved whale algorithm. The operation layer is aimed at minimizing the operation cost and voltage offset, considering OLTC fuzzy control, capacitor dropout, new energy uncertainty, micro-gas turbine and energy storage scheduling. It adopts the adaptive ε-constraint method multi-objective particle swarm algorithm to obtain the uniform Pareto front and then uses TOPSIS decision method to select the optimal solution. The proposed method can achieve low carbon and economic operation, improve tide distribution, increase the voltage quality and reduce network loss. © 2023 Power System Protection and Control Press. All rights reserved.
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页码:1 / 11
页数:10
相关论文
共 23 条
  • [1] ZHANG Shenxi, WANG Danyang, CHENG Haozhong, Et al., Key technologies and challenges of low-carbon integrated energy system planning for carbon emission peak and carbon neutrality, Automation of Electric Power Systems, 46, 8, pp. 189-207, (2022)
  • [2] ZHANG Yonghui, YANG Ruyue, PAN Chao, Et al., Active distribution network source-storage planning strategy based on grey relation dominance, Acta Energiae Solaris Sinica, 43, 1, pp. 242-248, (2022)
  • [3] ZHU Jiayuan, LIU Yang, XU Lixiong, Et al., Adjustable robust optimization for energy storage system in distribution network based on wind power full accommodation, Power System Technology, 42, 6, pp. 1875-1883, (2018)
  • [4] YIN Wei, CHEN Jie, YI Benshun, Intelligent controller based on fuzzy control for energy-saving operation of distribution transformers, Electric Power Automation Equipment, 29, 5, pp. 74-77, (2009)
  • [5] LIU Bin, LIU Feng, MEI Shengwei, Et al., Optimal power flow in active distribution networks with on-load tap changer based on second-order cone programming, Automation of Electric Power Systems, 39, 19, pp. 40-47, (2015)
  • [6] GAO Hongjun, LIU Junyong, SHEN Xiaodong, Et al., Optimal power flow research in active distribution network and its application examples, Proceedings of the CSEE, 37, 6, pp. 1634-1645, (2017)
  • [7] WANG Ziling, ZHOU Jinhui, CHEN Ming, Et al., Optimal configuration of energy storage for high PV permeability distribution network with on-load voltage regulation, Proceedings of the CSU-EPSA, 32, 8, pp. 123-129, (2020)
  • [8] XIAO Chuanliang, ZHAO Bo, ZHOU Jinhui, Et al., Network partition based cluster voltage control of high-penetration distributed photovoltaic systems in distribution networks, Automation of Electric Power Systems, 41, 21, pp. 147-155, (2017)
  • [9] WEN Fengrui, LI Huaqiang, WEN Xiangyu, Et al., Optimal allocation of energy storage systems considering flexibility deficiency risk in active distribution network, Power System Technology, 43, 11, pp. 3952-3962, (2019)
  • [10] CHEN Zexiong, ZHANG Xinmin, WANG Xuefeng, Et al., A distributionally robust optimal allocation method for distributed photovoltaic generation stations integrated into a distribution network, Power System Protection and Control, 49, 13, pp. 30-42, (2021)