Mixed-Integer Second-Order Cone Programming for Active Distribution Networks Considering Low-Carbon and Flexible Loads

doi:10.12204/j.issn.1000-7229.2022.12.007

Abstract

Abstract:

In the context of low-carbon distribution network, this paper proposes a mixed-integer second-order cone programming model for active distribution networks considering carbon emissions and flexible loads, with the investment strategy with minimum total cost. Considering the uncertainty of renewable energy, load and energy price, a scenario clustering method based on K-means is proposed. The decision variables of the model are the replacement of overloaded lines, the construction of new energy and energy storage devices, and the construction of voltage control equipment such as voltage regulators and capacitor banks. The polynomial voltage-dependent flexible loads, network reconfiguration and carbon emission constraints are considered. Aiming at the non-convex nonlinear characteristics of the planning model, the virtual demand method is used to model the network reconstruction as a mixed integer linear programming form, and an improved second-order cone relaxation method based on Taylor expansion is proposed to solve the problem of traditional second-order cone relaxation caused by flexible load model. The model is tested with a 69-node system, and the results show that the proposed model not only has a lower overall planning cost, but also helps reduce carbon emissions.

Key words: active distribution network, low-carbon planning, flexible load, second-order cone relaxation, stochastic planning

CLC Number:

TM741

ZHANG Junxiao, GAO Chong, LI Jingping, YUAN Hui, WANG Tianlin, LIU Ruikuan. Mixed-Integer Second-Order Cone Programming for Active Distribution Networks Considering Low-Carbon and Flexible Loads[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(12): 66-73.

Figures/Tables 5

Fig.1 Construction method for uncertain parameter scenarios

Fig.2 Initial configuration of the 69-node system.

Table 1 Overview of the solution of the 69-node system

Table 2 Comparison of different strategies

Table 3 Comparison of results of different cluster numbers

References 18

[1]	张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46(8):189-207.
	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[J]. Automation of Electric Power Systems, 2022, 46(8):189-207.
[2]	李立新, 周宇昊, 郑文广. 能源转型背景下分布式能源技术发展前景[J]. 发电技术, 2020, 41(6): 571-577. doi: 10.12096/j.2096-4528.pgt.20116
	LI Lixin, ZHOU Yuhao, ZHENG Wenguang. Development prospect of distributed energy technology under the background of energy transformation[J]. Power Generation Technology, 2020, 41(6): 571-577. doi: 10.12096/j.2096-4528.pgt.20116
[3]	陈旷, 罗益珍, 胡志坚, 等. 面向低碳经济的配电网多目标分层规划[J]. 武汉大学学报(工学版), 2020, 53(7): 619-626.
	CHEN Kuang, LUO Yizhen, HU Zhijian, et al. Multi-objective hierarchical planning of distribution network towards low-carbon economy[J]. Engineering Journal of Wuhan University, 2020, 53(7): 619-626.
[4]	TANAKA I, YUGE H, OHMORI H. Formulation and evaluation of long-term allocation problem for renewable distributed generations[J]. IET Renewable Power Generation, 2017, 11: 1584-1596. doi: 10.1049/iet-rpg.2017.0068 URL
[5]	MELGAR DOMINGUEZ O D, POURAKBARI KASMAEI M, LAVORATO M, et al. Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems[J]. Energy Systems, 2018, 9(3): 529-550. doi: 10.1007/s12667-017-0254-8 URL
[6]	MELGAR-DOMINGUEZ O D, POURAKBARI-KASMAEI M, MANTOVANI J R S. Adaptive robust short-term planning of electrical distribution systems considering siting and sizing of renewable energy based DG units[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 158-169. doi: 10.1109/TSTE.2018.2828778 URL
[7]	王志强, 郭晨阳, 刘文霞, 等. 计及负荷特性的配电网多时间尺度电压控制及协调修正[J]. 电力系统自动化, 2017, 41(15): 51-57.
	WANG Zhiqiang, GUO Chenyang, LIU Wenxia, et al. Multi-time scale voltage control and coordinated correction for distribution networks considering load characteristics[J]. Automation of Electric Power Systems, 2017, 41(15): 51-57.
[8]	范心明, 彭飞进, 伍肇龙, 等. 基于混合整数凸规划的主动配电网无功电压优化运行方法[J]. 电力电容器与无功补偿, 2018, 39(4): 99-105.
	FAN Xinming, PENG Feijin, WU Zhaolong, et al. Optimization operation method of reactive power voltage of active distribution network based on mixed integer convex programming[J]. Power Capacitor & Reactive Power Compensation, 2018, 39(4): 99-105.
[9]	HOME-ORTIZ J M, VARGAS R, MACEDO L H, et al. Joint reconfiguration of feeders and allocation of capacitor banks in radial distribution systems considering voltage-dependent models[J]. International Journal of Electrical Power & Energy Systems, 2019, 107: 298-310. doi: 10.1016/j.ijepes.2018.11.035 URL
[10]	谢亮, 李远卓, 崔慧军, 等. 考虑蓄电池储能的配电网动态网络重构[J]. 电力需求侧管理, 2021, 23(3): 70-74.
	XIE Liang, LI Yuanzhuo, CUI Huijun, et al. Dynamic network reconfiguration of distribution network considering battery energy storage[J]. Power Demand Side Management, 2021, 23(3): 70-74.
[11]	孙伟卿, 刘唯, 张婕. 高比例可再生能源背景下配电网动态重构与移动储能协同优化[J]. 电力系统自动化, 2021, 45(19): 80-90.
	SUN Weiqing, LIU Wei, ZHANG Jie. Collaborative optimization for dynamic reconfiguration of distribution network and mobile energy storage in background of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(19): 80-90.
[12]	曹昉, 张姚, 李赛. 基于网架结构相似性和适应性的主动配电网动态重构[J]. 电力系统自动化, 2019, 43(16): 78-85.
	CAO Fang, ZHANG Yao, LI Sai. Dynamic reconfiguration of active distribution network based on similarity and adaptability of network structure[J]. Automation of Electric Power Systems, 2019, 43(16): 78-85.
[13]	毕鹏翔, 刘健, 张文元. 配电网络重构的改进支路交换法[J]. 中国电机工程学报, 2001, 21(8): 98-103.
	BI Pengxiang, LIU Jian, ZHANG Wenyuan. A refined branch-exchange algorithm for distribution networks reconfiguration[J]. Proceedings of the CSEE, 2001, 21(8): 98-103.
[14]	麻秀范, 张粒子. 基于十进制编码的配网重构遗传算法[J]. 电工技术学报, 2004, 19(10): 65-69.
	MA Xiufan, ZHANG Lizi. Distribution network reconfiguration based on genetic algorithm using decimal encoding[J]. Transactions of China Electrotechnical Society, 2004, 19(10): 65-69.
[15]	XING H J, CHENG H Z, ZHANG L B, et al. Second-order cone model for active distribution network expansion planning[C]// 2015 IEEE Power & Energy Society General Meeting.IEEE, 2015.
[16]	JI H R, WANG C S, LI P, et al. An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks[J]. Applied Energy, 2017, 208: 986-995. doi: 10.1016/j.apenergy.2017.09.051 URL
[17]	WANG J D. An introduction to stochastic programming[J]. Chinese Journal of Operations Research, 1984(1):22-27.
[18]	WU J. Advances in k-means clustering[M]. Springer Theses, 2012:187-190.