考虑风光与负荷时序特性的主动配电网分布式电源优化配置

doi:10.12204/j.issn.1000-7229.2022.11.006

电力建设 ›› 2022, Vol. 43 ›› Issue (11): 53-62.doi: 10.12204/j.issn.1000-7229.2022.11.006

• 新型电力系统下配电网规划与运行优化关键技术研究及应用·栏目主持王守相教授、赵倩宇博士· • 上一篇下一篇

考虑风光与负荷时序特性的主动配电网分布式电源优化配置

初壮(), 孙健浩(), 赵蕾(), 孙旭()

东北电力大学电气工程学院,吉林省吉林市 132012

收稿日期:2022-04-07 出版日期:2022-11-01 发布日期:2022-11-03
通讯作者: 孙健浩 E-mail:chuzhuang@hotmail.com;2295269596@qq.com;zhaolei321666@163.com;623004587@qq.com
作者简介:初壮(1973),男,博士,副教授,硕士生导师,主要研究方向为电力系统优化运行、配电网计算分析,E-mail: chuzhuang@hotmail.com;
赵蕾(1999),女,硕士研究生,主要研究方向为综合能源系统优化调度,E-mail: zhaolei321666@163.com;
孙旭(1997),女,硕士研究生,主要研究方向为配电网优化调度,E-mail: 623004587@qq.com。
基金资助:
国家自然科学基金项目(51977030)

Optimal Configuration of Distributed Power Generation in Active Distribution Network Considering the Characteristics of Wind Power and Load Time Series

CHU Zhuang(), SUN Jianhao(), ZHAO Lei(), SUN Xu()

College of Electrical Engineering,Northeast Electric Power University, Jilin 132012, Jilin Province, China

Received:2022-04-07 Online:2022-11-01 Published:2022-11-03
Contact: SUN Jianhao E-mail:chuzhuang@hotmail.com;2295269596@qq.com;zhaolei321666@163.com;623004587@qq.com
Supported by:
National Natural Science Foundation of China(51977030)

摘要/Abstract

摘要：

主动配电网中分布式电源优化配置时,风光机组出力与负荷时序波动特性使场景过多,这既会增加模型求解的难度,也会影响优化配置结果。为此,首先引入戴维森堡丁指数(Davies-Bouldin index,DBI)结合K-means算法对全小时场景进行聚类缩减。然后,建立考虑风光与负荷时序特性的主动配电网双层优化配置模型,上层以系统年综合经济成本最优为目标,决策变量为风光机组接入配电网的位置及容量;下层以系统各时段运行电压水平最优为目标,决策变量为配电网主动管理措施下的运行成本;综合考虑风光机组时序出力和负荷侧时序响应,实现主动配电网分布式电源优化规划。最后,采用改进的自适应遗传算法对模型进行求解。IEEE 33节点系统算例表明,所提方法及模型在确保场景选取的多样性和合理性的同时,兼顾了风光机组出力与负荷时序波动特性,提高了系统运行水平,降低了系统运行成本。

关键词: 时序特性, 分布式电源, 自适应遗传算法, 戴维森堡丁指数(DBI), 双层优化配置模型

Abstract:

In the optimal configuration of distributed generation in active distribution network, the output and load timing fluctuation characteristics of wind power cause too many scenes, which will not only increase the difficulty of model solution, but also affect the optimal configuration results. For this reason, this paper first introduces Davies-Bouldin index (DBI) and K-means algorithm to cluster and reduce the full-hour scenes. Secondly, a two-level optimal allocation model of active distribution network considering the time series characteristics of wind power and load is established. The upper layer aims to optimize the annual comprehensive economic cost of the system, and the decision variables are the locations and capacities of wind power units connected to the distribution network; The lower level takes the optimal operation voltage level of the system in each period as the objective, and the decision variables are the operation costs under the active management measures of the distribution network; Considering the time sequence outputs of wind power and the time sequence response of load sides, the economic planning of distributed generation in active distribution network is realized. Finally, an improved adaptive genetic algorithm is used to solve the model. The IEEE 33-node system example shows that the proposed method and model not only ensures the diversity and rationality of scenario selection, but also takes into account the wind power output and load timing fluctuation characteristics, and improves the system operation level and reduce the system operation cost.

Key words: timing characteristics, distributed generation, adaptive genetic algorithm, Davies-Bouldin index(DBI), bi-level optimal allocation mode

中图分类号:

TM73

初壮, 孙健浩, 赵蕾, 孙旭. 考虑风光与负荷时序特性的主动配电网分布式电源优化配置[J]. 电力建设, 2022, 43(11): 53-62.

CHU Zhuang, SUN Jianhao, ZHAO Lei, SUN Xu. Optimal Configuration of Distributed Power Generation in Active Distribution Network Considering the Characteristics of Wind Power and Load Time Series[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(11): 53-62.

图/表 17

图1 不同时间风速的形状参数与比例参数

Fig.1 Shape parameters and proportion parameters of wind speed at different times

图2 不同时间光照形状参数

Fig.2 Illumination shape parameters at different times

图3 不同时间负荷标准差与数学期望

Fig.3 Load standard deviation and mathematical expectation at different times

图4 双层优化配置模型

Fig.4 Two-layer optimal configuration model

图5 模型求解流程

Fig.5 Model solving process

图6 IEEE 33节点系统图

Fig.6 Diagram of IEEE 33-node system

表1 AM调控措施范围

Table 1 Scope of AM regulation measures

表2 仿真数据

Table 2 Simulation data

图7 DBI指标变化趋势

Fig.7 Variation trend of Davies Bouldin index

图8 聚类前后的风光场景

Fig.8 Scenes before and after clustering

表3 聚类后各场景对应结果

Table 3 Corresponding results of each scene after clustering

表4 不同K'场景下对应全场景结果

Table 4 Corresponding full-scene results under different K' scenarios

表5 考虑时序性的方案对比

Table 5 Comparison of schemes considering timing

表6 三种方案各指标对比

Table 6 Comparison of indices of three schemes

图9 节点电压运行对比

Fig.9 Comparison of node voltage operation

图10 算法收敛对比曲线

Fig.10 Comparison curve of algorithm convergence

表7 算法寻优对比结果

Table 7 Comparison results of algorithm optimization

参考文献 20

[1]	蔡佳铭, 张涛, 王承民, 等. 计及源荷不确定性的主动配电网网架扩展柔性规划[J]. 电力自动化设备, 2019, 39(10): 109-115.
	CAI Jiaming, ZHANG Tao, WANG Chengmin, et al. Flexible planning of active distribution network expansion considering source and load uncertainty[J]. Electric Power Automation Equipment, 2019, 39(10): 109-115.
[2]	赵海洲, 陈建凯, 杨海跃, 等. 考虑传输能力的含分布式电源配电网扩展规划方法[J]. 电力自动化设备, 2021, 41(12): 70-77.
	ZHAO Haizhou, CHEN Jiankai, YANG Haiyue, et al. Expansion planning method of distribution network with distributed generation considering transmission capacity[J]. Electric Power Automation Equipment, 2021, 41(12): 70-77.
[3]	ZHANG X S, XU Y Y, LU S N, et al. Joint planning of distributed PV stations and EV charging stations in the distribution systems based on chance-constrained programming[J]. IEEE Access, 2021, 9: 6756-6768. doi: 10.1109/ACCESS.2021.3049568 URL
[4]	任智君, 郭红霞, 杨苹, 等. 含高比例可再生能源配电网灵活资源双层优化配置[J]. 太阳能学报, 2021, 42(9): 33-38.
	REN Zhijun, GUO Hongxia, YANG Ping, et al. Double-layer optimal configuration of flexible resources with high proportion of renewable energy distribution network[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 33-38.
[5]	张晓辉, 梁军雪, 赵翠妹, 等. 基于碳交易的含燃气机组的低碳电源规划[J]. 太阳能学报, 2020, 41(7): 92-98.
	ZHANG Xiaohui, LIANG Junxue, ZHAO Cuimei, et al. Research on low-carbon power planning with gas turbine units based on carbon transactions[J]. Acta Energiae Solaris Sinica, 2020, 41(7): 92-98.
[6]	YANG Y, LUO Z, YUAN X Y, et al. Bi-level multi-objective optimal design of integrated energy system under low-carbon background[J]. IEEE Access, 2021, 9: 53401-53407. doi: 10.1109/ACCESS.2021.3070654 URL
[7]	李洪志, 杨俊友, 崔嘉. 考虑网损和电压的多分布式电源最优选址和定容研究[J]. 电气工程学报, 2015, 10(9): 79-86.
	LI Hongzhi, YANG Junyou, CUI Jia. Optimal location and sizing of multi-distributed generators considering power loss and voltage[J]. Journal of Electrical Engineering, 2015, 10(9): 79-86.
[8]	马麟, 刘建鹏. 考虑时序特性和环境效益的多目标多类型分布式电源规划[J]. 电力系统保护与控制, 2016, 44(19): 32-40.
	MA Lin, LIU Jianpeng. Multi-objective planning of multi-type distributed generation considering timing characteristics and environmental benefits[J]. Power System Protection and Control, 2016, 44(19): 32-40.
[9]	TAHA H A, ALHAM M H, YOUSSEF H K M. Multi-objective optimization for optimal allocation and coordination of wind and solar DGs, BESSs and capacitors in presence of demand response[J]. IEEE Access, 2022, 10: 16225-16241. doi: 10.1109/ACCESS.2022.3149135 URL
[10]	张沈习, 李珂, 程浩忠, 等. 考虑相关性的间歇性分布式电源选址定容规划[J]. 电力系统自动化, 2015, 39(8): 53-58.
	ZHANG Shenxi, LI Ke, CHENG Haozhong, et al. Optimal siting and sizing of intermittent distributed generator considering correlations[J]. Automation of Electric Power Systems, 2015, 39(8): 53-58.
[11]	ELKADEEM M R, ABD ELAZIZ M, ULLAH Z, et al. Optimal planning of renewable energy-integrated distribution system considering uncertainties[J]. IEEE Access, 7: 164887-164907. doi: 10.1109/ACCESS.2019.2947308 URL
[12]	初壮, 乔福泉. 考虑风电与负荷时序性的分布式风电源选址定容[J]. 电力系统及其自动化学报, 2017, 29(10): 85-90.
	CHU Zhuang, QIAO Fuquan. Locating and sizing of distributed wind generation considering wind generation and the timing characteristics of load[J]. Proceedings of the CSU-EPSA, 2017, 29(10): 85-90.
[13]	MA H, ZHANG P F, CHEN Z Y, et al. Optimal design of multi-energy complementary distributed energy system considering load uncertainty[C]// 2020 5th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2020: 160-164.
[14]	牛壮壮, 刘三明, 刘扬. 考虑时序性和相关性的EVCS在配电网中的最优规划[J]. 智慧电力, 2021, 49(3): 95-102.
	NIU Zhuangzhuang, LIU Sanming, LIU Yang. Optimal planning of EVCS in distribution network considering sequential feature and correlation[J]. Smart Power, 2021, 49(3): 95-102.
[15]	倪识远, 张林垚, 胡志坚. 考虑电源和负荷时序特性的主动配电网网源协调规划方法[J]. 电力系统保护与控制, 2020, 48(10): 12-21.
	NI Shiyuan, ZHANG Linyao, HU Zhijian. Network-generation coordination planning of an active distribution network considering the time sequence characteristic of sources and loads[J]. Power System Protection and Control, 2020, 48(10): 12-21.
[16]	贾清泉, 赵美超, 孙玲玲, 等. 主动配电网中计及时序性与相关性的分布式光伏并网规划[J]. 中国电机工程学报, 2018, 38(6): 1719-1728.
	JIA Qingquan, ZHAO Meichao, SUN Lingling, et al. Planning for grid-connection of distributed PVs considering the sequential feature and correlation in active distribution network[J]. Proceedings of the CSEE, 2018, 38(6): 1719-1728.
[17]	李柏君. 基于大型风电场机组分类的功率预测研究[D]. 郑州: 华北水利水电大学, 2019.
	LI Baijun. Power prediction based on classification of large wind farm units[D]. Zhengzhou: North China University of Water Resources and Electric Power, 2019.
[18]	LI R, WANG W, XIA M C. Cooperative planning of active distribution system with renewable energy sources and energy storage systems[J]. IEEE Access, 2017, 6: 5916-5926. doi: 10.1109/ACCESS.2017.2785263 URL
[19]	康鲁豫, 朱顺, 刘刚. 考虑电压稳定和网损的分布式电源最优选址和定容[J]. 电气技术, 2015(2): 1-5.
	KANG Luyu, ZHU Shun, LIU Gang. Optimal locating and sizing of distributed generator considering voltage stability and power losses[J]. Electrical Engineering, 2015(2): 1-5.
[20]	陈俊岭, 何欣恒, 丛欧. 基于改进遗传算法的钢-混组合式风电机组塔架优化设计研究[J]. 太阳能学报, 2021, 42(7): 359-365.
	CHEN Junling, HE Xinheng, CONG Ou. Design optimization of steel-concrete hybrid wind turbine tower based on improved genetic algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42(7): 359-365.

考虑风光与负荷时序特性的主动配电网分布式电源优化配置

Optimal Configuration of Distributed Power Generation in Active Distribution Network Considering the Characteristics of Wind Power and Load Time Series

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