Distributionally Robust Optimal Operation of Distribution Network Considering Switch Reconfiguration and Demand Response

doi:10.12204/j.issn.1000-7229.2023.06.011

Abstract

Abstract:

Under the connection of high-penetration distributed photovoltaic generation, new challenges face the economic and safe operation of distribution networks. In this study, a distributionally robust optimization model considering switch reconfiguration and demand response was established to optimize the operation of a distribution network. First, considering switch reconfiguration and price-based demand response, topology optimization and operation schedules of the distribution network were obtained with the optimization objective of minimizing daily operating cost. Second, using the historical photovoltaic (PV) output data, the typical scenarios of PV output and its probability distribution are obtained through clustering, and the 1-norm and ∞-norm are used to constrain the confidence interval of the uncertainty probability distribution, and a data-driven distributionally robust two-stage optimization model is established. Finally, with the modified IEEE 33-node system, the Cplex solver was used based on the column-and-constraint generation algorithm, and the feasibility of the model was verified.

Key words: switch reconfiguration, demand response, operation of distribution network, distributionally robust optimization

CLC Number:

TM74

SHU Junlin, LIU Junyong, GAO Hongjun, HE Xin, HU Renchuan. Distributionally Robust Optimal Operation of Distribution Network Considering Switch Reconfiguration and Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 101-111.

Figures/Tables 14

Fig.1 Schematic diagram of switch reconfiguration of distribution network

Fig.2 Modified IEEE 33 node power distribution system

Table 1 Related parameters

Table 2 Price elasticity coefficients for different users

Fig.3 Typical daily load curve

Fig.4 PV output forecast scenario

Fig.5 Real-time electricity prices for different types of loads

Fig.6 Comparison of load active power before/after demand response

Fig.7 Comparison of switch states with/without demand response

Fig.8 Distribution of branch active power flow under different cases

Fig.9 Node voltage distribution under different cases

Table 3

Table 4

Fig.10 Switch states under stochastic optimization method

References 24

[1]	中共中央办公厅, 国务院. 关于推进以县城为重要载体的城镇化建设的意见[EB/OL]. 新华社, 2022-05-06,(1)
	The General Office of the Central Committee of the Communist Party of China, the State Council. Opinions on Promoting Urbanization Construction with County as an Important Carrier[EB/OL]. Xinhua News Agency, 2022-05-06, (1)
[2]	王雅丽, 刘洪, 赵娟, 等. 基于网络重构和电热联合需求响应的配电网安全经济调度[J]. 电力建设, 2019, 40(4):90-97 doi: 10.3969/j.issn.1000-7229.2019.04.011
	WANG Yali, LIU Hong, ZHAO Juan, et al. Distribution network economic dispatch based on network reconfiguration and power-heat combined demand response[J]. Electric Power Construction, 2019, 40(4):90-97 doi: 10.3969/j.issn.1000-7229.2019.04.011
[3]	申洪涛, 岳凡丁, 史轮, 等. 考虑DG及负荷时序性的多目标配电网重构与DG调控综合优化规划[J]. 现代电力, 2022, 39(2):182-192.
	SHEN Hongtao, YUE Fanding, SHI Lun, et al. Comprehensive optimal planning of multi-objective distribution network reconfiguration and DG regulation considering DG and load sequence[J]. Modern Electric Power, 2022, 39(2):182-192.
[4]	谢冬清, 李兵, 丁炀, 等. 含低风速分散式风电的配电网动态鲁棒重构[J]. 太阳能学报, 2021, 42(11): 226-233.
	XIE Dongqing, LI Bing, DING Yang, et al. Dynamic robust reconfiguration of distribution network involving low-wind-speed dispersed wind power[J]. Acta Energiae Solaris Sinica, 2021, 42(11): 226-233.
[5]	LI J Q, WANG G B, TANG T C, et al. Optimization of distribution network reconfiguration based on Markov chain Monte Carlo method[J]. Energy Reports, 2022, 8(S10).
[6]	HUY T H B, VAN TRAN T, NGOC VO D, et al. An improved metaheuristic method for simultaneous network reconfiguration and distributed generation allocation[J]. Alexandria Engineering Journal, 2022, 61(10): 8069-8088. doi: 10.1016/j.aej.2022.01.056 URL
[7]	O D. MONTOYA-GIRALDO, W GIL-GONZ LEZ, L F. GRISALES-NORE A, et al. On the Optimal Reconfiguration of Radial AC Distribution Networks Using an MINLP Formulation: A GAMS-Based Approach[J]. Ingeniería e Investigación, 2022, 42(2)e91192 doi: 10.15446/ing.investig.91192 URL
[8]	唐文虎, 张银, 钱瞳. 考虑配电网络重构和楼宇群主动需求响应的集成楼宇群配电网优化控制[J]. 中国电机工程学报, 2023, 43(8):2966-2978.
	TANG Wenhu, ZHANG Yin, QIAN Tong. Integrated building group distribution network optimization control considering distribution network reconfiguration and building group active demand response[J]. Proceedings of the CSEE, 2023, 43(8):2966-2978.
[9]	LI Z H, WU W C, ZHANG B M, et al. An decomposition algorithm for distribution network reconfiguration schedule considering demand response[C]// 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). October 7-10, 2018, Kota Kinabalu, Malaysia. IEEE, 2018: 71-76.
[10]	AJOULABADI A, RAVADANEGH S N, MOHAMMADI-IVATLOO B. Flexible scheduling of reconfigurable microgrid-based distribution networks considering demand response program[J]. Energy, 2020, 196: 117024. doi: 10.1016/j.energy.2020.117024 URL
[11]	王冠, 李鹏, 焦扬, 等. 计及风光不确定性的虚拟电厂多目标随机调度优化模型[J]. 中国电力, 2017, 50(5)107-113
	WANG Guan, LI Peng, JIAO Yang, et al. Multi-objective stochastic scheduling optimization model for virtual power plant considering uncertainty of wind and photovoltaic power[J]. Electric Power, 2017, 50(5)107-113
[12]	李鹏, 韩鹏飞, 陈安伟, 等. 含高密度间歇性能源的交直流混合微网模糊随机优化运行[J]. 中国电机工程学报, 2018, 38(10): 2956-2969.
	LI Peng, HAN Pengfei, CHEN Anwei, et al. Fuzzy random optimal operation of hybrid AC/DC microgrid with high density intermittent energy sources[J]. Proceedings of the CSEE, 2018, 38(10): 2956-2969.
[13]	刘一欣, 郭力, 王成山. 微电网两阶段鲁棒优化经济调度方法[J]. 中国电机工程学报, 2018, 38(14): 4013-4022.
	LIU Yixin, GUO Li, WANG Chengshan. Economic dispatch of microgrid based on two stage robust optimization[J]. Proceedings of the CSEE, 2018, 38(14): 4013-4022.
[14]	彭春华, 谢鹏, 陈臣. 大规模光伏电站接入电网可调节鲁棒优化调度[J]. 中国电机工程学报, 2014, 34(25): 4324-4332.
	PENG Chunhua, XIE Peng, CHEN Chen. Adjustable robust optimal dispatch of power system with large-scale photovoltaic power stations[J]. Proceedings of the CSEE, 2014, 34(25): 4324-4332.
[15]	陈泽雄, 张新民, 王雪锋, 等. 分布式光伏电站接入配电网的分布鲁棒优化配置方法[J]. 电力系统保护与控制, 2021, 49(13): 30-42.
	CHEN Zexiong, ZHANG Xinmin, WANG Xuefeng, et al. A distributionally robust optimal allocation method for distributed photovoltaic generation stations integrated into a distribution network[J]. Power System Protection and Control, 2021, 49(13): 30-42.
[16]	阮贺彬, 高红均, 刘俊勇, 等. 考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型[J]. 中国电机工程学报, 2019, 39(3): 685-695.
	RUAN Hebin, GAO Hongjun, LIU Junyong, et al. A distributionally robust reactive power optimization model for active distribution network considering reactive power support of DG and switch reconfiguration[J]. Proceedings of the CSEE, 2019, 39(3): 685-695.
[17]	张衡, 程浩忠, 曾平良, 等. 基于随机优化理论的输电网规划研究综述[J]. 电网技术, 2017, 41(10): 3121-3129.
	ZHANG Heng, CHENG Haozhong, ZENG Pingliang, et al. Overview of transmission network expansion planning based on stochastic optimization[J]. Power System Technology, 2017, 41(10): 3121-3129.
[18]	宫成, 宋靓云, 王卫, 等. 鲁棒优化在电力系统机组组合中的应用综述[J]. 科学技术与工程, 2022, 22(12): 4687-4695.
	GONG Cheng, SONG Liangyun, WANG Wei, et al. An overview of robust optimization used for power system unit commitment[J]. Science Technology and Engineering, 2022, 22(12): 4687-4695.
[19]	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.
[20]	高红均, 刘俊勇. 考虑不同类型DG和负荷建模的主动配电网协同规划[J]. 中国电机工程学报, 2016, 36(18): 4911-4922.
	GAO Hongjun, LIU Junyong. Collaborative planning of active distribution network considering different types of DG and load modeling[J]. Proceedings of the CSEE, 2016, 36(18): 4911-4922.
[21]	贺帅佳, 高红均, 刘俊勇, 等. 计及需求响应柔性调节的分布鲁棒DG优化配置[J]. 中国电机工程学报, 2019, 39(8): 2253-2264.
	HE Shuaijia, GAO Hongjun, LIU Junyong, et al. Distributionally robust optimal DG allocation model considering flexible adjustment of demand response[J]. Proceedings of the CSEE, 2019, 39(8): 2253-2264.
[22]	崔金兰, 刘天琪, 李兴源. 含有分布式发电的配电网重构研究[J]. 电力系统保护与控制, 2008, 36(15): 37-40, 49.
	CUI Jinlan, LIU Tianqi, LI Xingyuan. Network reconfiguration at the distribution system with distributed generation[J]. Power System Protection and Control, 2008, 36(15): 37-40, 49.
[23]	MISHRA S, DAS D, PAUL S. A comprehensive review on power distribution network reconfiguration[J]. Energy Systems, 2017, 8(2): 227-284. doi: 10.1007/s12667-016-0195-7 URL
[24]	WANG Y Q, GAO S, JIA W H, et al. Data-driven distributionally robust economic dispatch for park integrated energy systems with coordination of carbon capture and storage devices and combined heat and power plants[J]. IET Renewable Power Generation, 2022, 16(12): 2617-2629. doi: 10.1049/rpg2.v16.12 URL