Two-Stage Robust Optimization for Flexible Interconnected Distribution Systems Considering Energy Storages and Dynamic Reconfiguration

doi:10.12204/j.issn.1000-7229.2022.09.010

Abstract

Abstract:

Large scale integration of intermittent distributed generation (DG) challenges the reliable and secure operation of distribution systems. Therefore, this paper proposes a two-stage robust scheduling method for flexible interconnected distribution systems considering energy storage and dynamic reconfiguration. Firstly, a two-stage robust scheduling model for flexible interconnected distribution systems is established, considering flexible distribution switches (FDSs), energy storage and dynamic reconfiguration. In the first stage, the network topologies and energy storage outputs are globally optimized considering their temporal correlation and day-ahead forecasting uncertainties of the DG outputs. In the second stage, the FDS power in each time slot is dispatched according to the decisions of the first stage and ultra-short-term DG forecast. Then, an improved column and constraint generation algorithm is adopted to solve the robust model, where an alternating direction process is used to accelerate the sub-problem. Finally, the effectiveness of the proposed robust scheduling model and algorithm is verified using the 33-node and 69-node distribution systems with promising results.

Key words: flexible interconnected distribution systems, robust optimization, dynamic reconfiguration, uncertainty

CLC Number:

TM732

LIANG Dong, GUO Yuwei, WANG Xiaoxue, LIU Qi, WANG Shouxiang. Two-Stage Robust Optimization for Flexible Interconnected Distribution Systems Considering Energy Storages and Dynamic Reconfiguration[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 94-103.

Figures/Tables 19

Fig.1 Algorithm flowchart

Fig.2 33-node distribution system

Fig.3 DG output bounds

Table 1 Objectives under different Г

Table 2 Scheduling results of network topologies

Fig.4 Remaining energy of energy storages

Table 3 Results of Monte Carlo sampling tests

Fig.5 Day-ahead FDS scheduling reference value (ideal scenario, DA-RO)

Fig.6 Intra-day FDS scheduling results (ideal scenario, DA-RO+IDO)

Fig.7 Comparison before and after IDO based on DA-RO

Fig.8 Day-ahead voltage reference curves of all nodes (worst scenario, DA-DO)

Fig.9 Intra-day voltage curves of all nodes (worst scenario, DA-DO+IDO)

Fig.10 Day-ahead FDS scheduling reference value (worst scenario, DA-RO)

Fig.11 Intra-day FDS scheduling results (worst scenario, DA-RO+IDO)

Fig.12 Day-ahead voltage value of all nodes (worst scenario, DA-RO+IDO)

Table 4 Test plans

Table 5 Test results of different scenarios

Table 6 CPU time comparison of different systems

Table 7 CPU time under different DG numbers

References 21

[1]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.
[2]	文劲宇, 周博, 魏利屾. 中国未来电力系统储电网初探[J]. 电力系统保护与控制, 2022, 50(7): 1-10.
	WEN Jinyu, ZHOU Bo, WEI Lishen. Preliminary study on an energy storage grid for future power system in China[J]. Power System Protection and Control, 2022, 50(7): 1-10.
[3]	杨欢, 蔡云旖, 屈子森, 等. 配电网柔性开关设备关键技术及其发展趋势[J]. 电力系统自动化, 2018, 42(7): 153-165.
	YANG Huan, CAI Yunyi, QU Zisen, et al. Key techniques and development trend of soft open point for distribution network[J]. Automation of Electric Power Systems, 2018, 42(7): 153-165.
[4]	祁琪, 姜齐荣, 许彦平. 智能配电网柔性互联研究现状及发展趋势[J]. 电网技术, 2020, 44(12): 4664-4676.
	QI Qi, JIANG Qirong, XU Yanping. Research status and development prospect of flexible interconnection for smart distribution networks[J]. Power System Technology, 2020, 44(12): 4664-4676.
[5]	LIU W X, FU M D, YANG M Y, et al. A bi-level interval robust optimization model for service restoration in flexible distribution networks[J]. IEEE Transactions on Power Systems, 2021, 36(3): 1843-1855. doi: 10.1109/TPWRS.2020.3038400 URL
[6]	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
[7]	贺帅佳, 阮贺彬, 高红均, 等. 分布鲁棒优化方法在电力系统中的理论分析与应用综述[J]. 电力系统自动化, 2020, 44(14): 179-191.
	HE Shuaijia, RUAN Hebin, GAO Hongjun, et al. Overview on theory analysis and application of distributionally robust optimization method in power system[J]. Automation of Electric Power Systems, 2020, 44(14): 179-191.
[8]	JI H R, WANG C S, LI P, et al. Robust operation of soft open points in active distribution networks with high penetration of photovoltaic integration[J]. IEEE Transactions on Sustainable Energy, 2019, 10(1): 280-289. doi: 10.1109/TSTE.2018.2833545 URL
[9]	孙充勃, 李敬如, 原凯, 等. 基于区间优化的配电网智能软开关与储能系统联合优化方法[J]. 高电压技术, 2021, 47(1): 45-54.
	SUN Chongbo, LI Jingru, YUAN Kai, et al. Two-stage optimization method of soft open point and energy storage system in distribution network based on interval optimization[J]. High Voltage Engineering, 2021, 47(1): 45-54.
[10]	王成山, 宋关羽, 李鹏, 等. 一种联络开关和智能软开关并存的配电网运行时序优化方法[J]. 中国电机工程学报, 2016, 36(9): 2315-2321.
	WANG Chengshan, SONG Guanyu, LI Peng, et al. A hybrid optimization method for distribution network operation with SNOP and Tie switch[J]. Proceedings of the CSEE, 2016, 36(9): 2315-2321.
[11]	武忠山, 秦超, 余贻鑫. 考虑网络动态重构的配电系统两阶段灵活性提升优化调度方法[J]. 电网技术, 2020, 44(12): 4644-4653.
	WU Zhongshan, QIN Chao, YU Yixin. Two-stage optimal dispatching method for distribution system flexibility improvement considering dynamic reconfiguration[J]. Power System Technology, 2020, 44(12): 4644-4653.
[12]	董逸超, 王守相, 闫秉科. 配电网分布式电源接纳能力评估方法与提升技术研究综述[J]. 电网技术, 2019, 43(7): 2258-2266.
	DONG Yichao, WANG Shouxiang, YAN Bingke. Review on evaluation methods and improvement techniques of DG hosting capacity in distribution network[J]. Power System Technology, 2019, 43(7): 2258-2266.
[13]	张旭, 么莉, 陈晨, 等. 交直流混合配电网网络重构与无功优化协同的两阶段鲁棒优化模型[J]. 电网技术, 2022, 46(3): 1149-1162.
	ZHANG Xu, YAO Li, CHEN Chen, et al. A novel two-stage robust model for co-optimization of reconfiguration and reactive power in AC/DC hybrid distribution network[J]. Power System Technology, 2022, 46(3): 1149-1162.
[14]	HAGHIGHAT H, ZENG B. Distribution system reconfiguration under uncertain load and renewable generation[J]. IEEE Transactions on Power Systems, 2016, 31(4): 2666-2675. doi: 10.1109/TPWRS.2015.2481508 URL
[15]	LEE C, LIU C, MEHROTRA S, et al. Robust distribution network reconfiguration[J]. IEEE Transactions on Smart Grid, 2015, 6(2): 836-842. doi: 10.1109/TSG.2014.2375160 URL
[16]	孙充勃, 原凯, 李鹏, 等. 基于SOP的多电压等级混联配电网运行二阶锥规划方法[J]. 电网技术, 2019, 43(5): 1599-1605.
	SUN Chongbo, YUAN Kai, LI Peng, et al. A second-order cone programming method for hybrid multiple voltage level distribution networks based on soft open points[J]. Power System Technology, 2019, 43(5): 1599-1605.
[17]	翟鹤峰, 杨明, 赵利刚, 等. 提升分布式电源接纳能力的配电网三相鲁棒动态重构[J]. 电力系统自动化, 2019, 43(18): 35-42.
	ZHAI Hefeng, YANG Ming, ZHAO Ligang, et al. Three-phase robust dynamic reconfiguration of distribution network to improve acceptance ability of distributed generator[J]. Automation of Electric Power Systems, 2019, 43(18): 35-42.
[18]	ZHANG C, XU Y, DONG Z Y, et al. Three-stage robust inverter-based voltage/var control for distribution networks with high-level PV[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 782-793. doi: 10.1109/TSG.2017.2752234 URL
[19]	ZENG B, ZHAO L. Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013, 41(5): 457-461. doi: 10.1016/j.orl.2013.05.003 URL
[20]	LORCA A, SUN A. Adaptive robust optimization with dynamic uncertainty sets for multi-period economic dispatch under significant wind[C]// 2015 IEEE Power & Energy Society General Meeting. Denver, CD, LISA: IEEE, 2015.
[21]	FOURER R, GAY D M, KERNIGHAN B W. A modeling language for mathematical programming[J]. Management Science, 1990, 36(5): 519-554. doi: 10.1287/mnsc.36.5.519 URL