计及需求侧响应的主动配电网多时间尺度优化调度

doi:10.12204/j.issn.1000-7229.2023.03.004

电力建设 ›› 2023, Vol. 44 ›› Issue (3): 36-48.doi: 10.12204/j.issn.1000-7229.2023.03.004

• 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

计及需求侧响应的主动配电网多时间尺度优化调度

李振坤¹(), 黄滢¹(), 李谅², 傅坚², 汪璇璇¹()

1.上海电力大学电气工程学院,上海市 200090
2.上海电力实业有限公司,上海市 200001

收稿日期:2022-03-23 出版日期:2023-03-01 发布日期:2023-03-02
通讯作者: 李振坤(1982),男,博士,教授,研究方向为配电网规划及运行控制、分布式电源并网及微电网、主动配电网技术,E-mail:lzk021@163.com
作者简介:黄滢(1999),女,硕士研究生,研究方向为配电网优化运行、虚拟电厂,E-mail:2116506020@qq.com
李谅(1979),男,工程师,技师,研究方向为主动配电网、变电站安全运行
傅坚(1965),男,硕士,高级工程师,主要从事电动汽车、高压直流输电技术的相关工作
汪璇璇(1996),男,硕士研究生,研究方向为配电网优化运行, E-mail:computer201@163.com
基金资助:
国家自然科学基金项目(52177098)

Multi-Time Scale Optimal Dispatching of Active Distribution Network Considering Demand-Side Response

LI Zhenkun¹(), HUANG Ying¹(), LI Liang², FU Jian², WANG Xuanxuan¹()

1. School of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. Shanghai Electric Power Industry Co., Ltd., Shanghai 200001,China

Received:2022-03-23 Online:2023-03-01 Published:2023-03-02
Supported by:
National Natural Science Foundation of China(52177098)

摘要/Abstract

摘要：

考虑分布式电源出力的波动性、负荷预测的误差以及各类调度资源的运行特性在时间尺度上存在差异等问题,提出一种在需求侧响应机制下主动配电网的多时间尺度优化调度模型。首先,负荷参与需求侧响应的形式分为价格型和激励型两类,分别对这两种需求侧响应的形式进行建模分析。其次,建立基于模型预测控制的主动配电网多时间尺度调度框架,根据模型预测控制方法分别建立日前调度、日内滚动和实时反馈三个阶段的配电网优化调度模型。考虑到配电网调度过程中的有功无功耦合特性,通过控制无功调度资源使得配电网各节点电压控制在允许范围。最后,通过对改进的31节点算例进行仿真,验证了所提模型可以有效地降低预测产生的误差并提高主动配电网运行的经济性和安全性。

关键词: 负荷预测, 需求侧响应, 激励型, 无功耦合

Abstract:

Considering the fluctuation of distributed generation output, the error of load forecasting and the difference of operation characteristics of various scheduling resources in time scale, in this paper, a multi-time scale optimal dispatching model of active distribution network under demand-side response mechanism is proposed. Firstly, the forms of load participating in demand-side response are divided into price type and incentive type, and the two forms of demand-side response are modeled and analyzed separately. Secondly, a multi-time scale scheduling framework based on model predictive control for active distribution network is established. On the basis of the model predictive control method, three optimal dispatching models of daily distribution, intraday rolling and real-time feedback are established separately. Considering the coupling characteristics of the active and reactive powers in the process of distribution network dispatching, the voltage of each node of distribution network is controlled within the allowable range by controlling reactive power dispatching resources. Finally, through the simulation of the improved 31-node example, it is verified that the proposed model can effectively reduce the prediction error and improve the economy and safety of active distribution network operation.

Key words: load forecasting, demand-side response, incentive type, reactive power coupling

中图分类号:

TM73

李振坤, 黄滢, 李谅, 傅坚, 汪璇璇. 计及需求侧响应的主动配电网多时间尺度优化调度[J]. 电力建设, 2023, 44(3): 36-48.

LI Zhenkun, HUANG Ying, LI Liang, FU Jian, WANG Xuanxuan. Multi-Time Scale Optimal Dispatching of Active Distribution Network Considering Demand-Side Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 36-48.

图/表 23

图1 多时间尺度优化调度流程图

Fig.1 Flow chart of multi-time scale optimal scheduling

图2 实时反馈流程图

Fig.2 Flow chart of real-time feedback

图3 实时反馈模型的计算流程

Fig.3 Calculation flow of real-time feedback model

图4 改进的31节点配电系统结构图

Fig.4 Structure diagram of improved 31-node distribution system

表1 风机、光伏和负荷的预测误差

Table 1 Forecast errors of wind power, PV and load

图5 光伏、风机和负荷的出力预测曲线

Fig.5 Forecast curves of wind power, PV and load

表2 分时电价信息

Table 2 Time-of-use tariff information

图6 日前负荷参与需求侧响应前后对比

Fig.6 Comparison before and after day-ahead load participating in demand-side response

图7 日前各调度资源有功出力情况

Fig.7 Day-ahead active power output of dispatching resources

图8 ESS1的充放电计划和荷电状态

Fig.8 Charging and discharging plan and state-of-charge of ESS1

图9 变压器T1的分接开关位置、电容器组C1的投切组数

Fig.9 Tap-changer position of transformer T1 and the number of switching groups of capacitor bank C1

图10 日前各调度资源无功出力

Fig.10 Day-ahead reactive power output of dispatching resources

图11 配电网各节点在各个时段的电压值

Fig.11 Voltage value of each node of distribution network in each period

图12 日内负荷调度结果

Fig.12 Intraday load dispatching result

图13 日内各调度资源的出力情况

Fig.13 Intraday output of dispatching resources

图14 实时负荷调度结果

Fig.14 Real-time load dispatching result

图15 优化调度前后各节点电压值

Fig.15 Voltage value of each node before and after optimal dispatching

图16 实时各调度资源的出力情况

Fig.16 Real-time output of each dispatching resource

表3 Scheduling cost of each stage 元

Table 3

表A1 负荷分类及参与激励型DR补偿价格参数

Table A1 Load classification and participating incentive DR compensation price parameters

表A2 主动配电网各设备参数

Table A2 Parameters of equipment in active distribution network

表A3 各节点刚性负荷百分比

Table A3 Rigid load percentage of each node

图A1 光伏、风机和负荷的出力曲线

Fig.A1 Output curve of photovoltaic, fan and load

参考文献 22

[1]	邱伟强, 王茂春, 林振智, 等. “双碳”目标下面向新能源消纳场景的共享储能综合评价[J]. 电力自动化设备, 2021, 41(10): 244-255.
	QIU Weiqiang, WANG Maochun, LIN Zhenzhi, et al. Comprehensive evaluation of shared energy storage towards new energy accommodation scenario under targets of carbon emission peak and carbon neutrality[J]. Electric Power Automation Equipment, 2021, 41(10): 244-255.
[2]	王珺, 田恩东, 马建, 等. 基于数据驱动的非全实时观测配电网无功优化方法[J]. 电力建设, 2021, 42(2): 68-76. doi: 10.12204/j.issn.1000-7229.2021.02.009
	WANG Jun, TIAN Endong, MA Jian, et al. Reactive power optimization of partial real-time visible distribution network based on data driven[J]. Electric Power Construction, 2021, 42(2): 68-76. doi: 10.12204/j.issn.1000-7229.2021.02.009
[3]	戴松灵, 王晞, 刘方, 等. 考虑综合承载力的主动配电网优化调度与运行[J]. 电力建设, 2020, 41(2): 67-75. doi: 10.3969/j.issn.1000-7229.2020.02.008
	DAI Songling, WANG Xi, LIU Fang, et al. Optimal scheduling and operating of active distribution network considering comprehensive carrying capacity[J]. Electric Power Construction, 2020, 41(2): 67-75. doi: 10.3969/j.issn.1000-7229.2020.02.008
[4]	朱志芳, 许苑, 岑海凤, 等. 考虑需求侧响应的园区综合能源系统优化配置[J]. 智慧电力, 2022, 50(1): 37-44.
	ZHU Zhifang, XU Yuan, CEN Haifeng, et al. Optimal configuration of park-level integrated energy system considering demand response[J]. Smart Power, 2022, 50(1): 37-44.
[5]	罗纯坚, 李姚旺, 许汉平, 等. 需求响应不确定性对日前优化调度的影响分析[J]. 电力系统自动化, 2017, 41(5): 22-29.
	LUO Chunjian, LI Yaowang, XU Hanping, et al. Influence of demand response uncertainty on day-ahead optimization dispatching[J]. Automation of Electric Power Systems, 2017, 41(5): 22-29.
[6]	仉梦林, 胡志坚, 王小飞, 等. 基于动态场景集和需求响应的二阶段随机规划调度模型[J]. 电力系统自动化, 2017, 41(11): 68-76.
	ZHANG Menglin, HU Zhijian, WANG Xiaofei, et al. Two-stage stochastic programming scheduling model based on dynamic scenario sets and demand response[J]. Automation of Electric Power Systems, 2017, 41(11): 68-76.
[7]	雷敏, 魏务卿, 曾进辉, 等. 考虑需求响应的负荷控制对供电可靠性影响分析[J]. 电力系统自动化, 2018, 42(10): 53-59.
	LEI Min, WEI Wuqing, ZENG Jinhui, et al. Effect of load control on power supply reliability considering demand response[J]. Automation of Electric Power Systems, 2018, 42(10): 53-59.
[8]	LIU D N, WANG L X, GAO Y, et al. Study on the mechanism of flexible demand-side resources participating in the electricity market under new situation[J]. IOP Conference Series: Earth and Environmental Science, 2021, 804(4): 042047. doi: 10.1088/1755-1315/804/4/042047
[9]	李秀磊, 耿光飞, 季玉琦, 等. 主动配电网中储能和需求侧响应的联合优化规划[J]. 电网技术, 2016, 40(12): 3803-3810.
	LI Xiulei, GENG Guangfei, JI Yuqi, et al. Integrated optimal planning of energy storage and demand side response in active power distribution network[J]. Power System Technology, 2016, 40(12): 3803-3810.
[10]	魏震波, 张海涛, 魏平桉, 等. 考虑动态激励型需求响应的微电网两阶段优化调度[J]. 电力系统保护与控制, 2021, 49(19): 1-10.
	WEI Zhenbo, ZHANG Haitao, WEI Pingan, et al. Two-stage optimal dispatching for microgrid considering dynamic incentive-based demand response[J]. Power System Protection and Control, 2021, 49(19): 1-10.
[11]	SHENG H Z, WANG C F, LI B W, et al. Multi-timescale active distribution network scheduling considering demand response and user comprehensive satisfaction[J]. IEEE Transactions on Industry Applications, 2021, 57(3): 1995-2005. doi: 10.1109/TIA.2021.3057302 URL
[12]	雷旭, 马鹏飞, 宋智帅, 等. 计及风电预测误差的柔性负荷日内调度模型[J]. 发电技术, 2022, 43(3): 485-491. doi: 10.12096/j.2096-4528.pgt.20083
	LEI Xu, MA Pengfei, SONG Zhishuai, et al. A flexible intraday load dispatch model considering wind power prediction errors[J]. Power Generation Technology, 2022, 43(3): 485-491. doi: 10.12096/j.2096-4528.pgt.20083
[13]	寇凌峰, 吴鸣, 李洋, 等. 主动配电网分布式有功无功优化调控方法[J]. 中国电机工程学报, 2020, 40(6): 1856-1865.
	KOU Lingfeng, WU Ming, LI Yang, et al. Optimization and control method of distributed active and reactive power in active distribution network[J]. Proceedings of the CSEE, 2020, 40(6): 1856-1865.
[14]	蒲天骄, 刘威, 陈乃仕, 等. 基于一致性算法的主动配电网分布式优化调度[J]. 中国电机工程学报, 2017, 37(6): 1579-1590.
	PU Tianjiao, LIU Wei, CHEN Naishi, et al. Distributed optimal dispatching of active distribution network based on consensus algorithm[J]. Proceedings of the CSEE, 2017, 37(6): 1579-1590.
[15]	王志南, 张宇华, 黄珂, 等. 计及多种柔性负荷的虚拟电厂热电联合鲁棒优化调度模型[J]. 电力建设, 2021, 42(7): 1-10. doi: 10.12204/j.issn.1000-7229.2021.07.001
	WANG Zhinan, ZHANG Yuhua, HUANG Ke, et al. Robust optimal scheduling model of virtual power plant combined heat and power considering multiple flexible loads[J]. Electric Power Construction, 2021, 42(7): 1-10. doi: 10.12204/j.issn.1000-7229.2021.07.001
[16]	高阳, 席皛, 刘小慧, 等. 考虑生物质供给价格弹性的工业园区综合能源系统规划[J]. 电力建设, 2021, 42(4): 49-58. doi: 10.12204/j.issn.1000-7229.2021.04.006
	GAO Yang, XI Xiao, LIU Xiaohui, et al. Integrated energy system planning method for industrial parks considering the price elasticity of biomass supply[J]. Electric Power Construction, 2021, 42(4): 49-58. doi: 10.12204/j.issn.1000-7229.2021.04.006
[17]	AN X, WANG L Y, HU X Y, et al. Collaborative regulation control optimization on demand side of microgrid based on multi-agent[C]// 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG). IEEE, 2019: 493-496.
[18]	卢颖辉. 计及微网储能系统多尺度不确定性容量协调优化[J]. 储能科学与技术, 2021, 10(6): 2235-2243.
	LU Yinghui. Capacity coordination and optimization considering multi-scale uncertainty of micro-grid energy storage system[J]. Energy Storage Science and Technology, 2021, 10(6): 2235-2243.
[19]	李滨, 黎智能, 陈碧云. 电力市场中配电网的空调群调控策略[J]. 电力系统自动化, 2019, 43(15): 124-131.
	LI Bin, LI Zhineng, CHEN Biyun. Air conditioning group dispatch control strategy of distribution network in electricity market[J]. Automation of Electric Power Systems, 2019, 43(15): 124-131.
[20]	王成山, 吕超贤, 李鹏, 等. 园区型综合能源系统多时间尺度模型预测优化调度[J]. 中国电机工程学报, 2019, 39(23): 6791-6803.
	WANG Chengshan, LÜ Chaoxian, LI Peng, et al. Multiple time-scale optimal scheduling of community integrated energy system based on model predictive control[J]. Proceedings of the CSEE, 2019, 39(23): 6791-6803.
[21]	何畅, 程杉, 徐建宇, 等. 基于多时间尺度和多源储能的综合能源系统能量协调优化调度[J]. 电力系统及其自动化学报, 2020, 32(2): 77-84.
	HE Chang, CHENG Shan, XU Jianyu, et al. Coordinated optimal scheduling of integrated energy system considering multi-time scale and hybrid energy storage system[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 77-84.
[22]	葛晓琳, 居兴, 王定美. 考虑需求响应不确定性的主动配电网优化调度[J]. 电测与仪表, 2023, 60(1): 104-110.
	GE Xiaolin, JU Xing, WANG Dingmei. Optimal scheduling of active distribution network considering uncertainty of demand response[J]. Electrical Measurement & Instrumentation, 2023, 60(1): 104-110.

计及需求侧响应的主动配电网多时间尺度优化调度

Multi-Time Scale Optimal Dispatching of Active Distribution Network Considering Demand-Side Response

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 23

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈逸枞, 张大海, 李宇欣, 王颖. 基于DWT-MOSMA-SVM的多目标优化短期母线负荷预测[J]. 电力建设, 2023, 44(3): 49-55.
[2]	许野, 何哲晨, 谭钧元, 郭军红, 李薇, 李亚楼. 基于TRNSYS的气候变化下“冷-热-电”联供系统运行优化[J]. 电力建设, 2023, 44(1): 39-46.
[3]	鲍海波, 吴阳晨, 张国应, 李江伟, 郭小璇, 黎静华. 基于特征加权Stacking集成学习的净负荷预测方法[J]. 电力建设, 2022, 43(9): 104-116.
[4]	孙玉芹, 王亚文, 朱威, 李彦. 基于考虑气温影响的门限自回归移动平均模型居民日用电负荷预测[J]. 电力建设, 2022, 43(9): 117-124.
[5]	孙磊, 王一均, 丁江, 丁明, 王芃. 计及激励型需求侧响应的发输电鲁棒规划策略[J]. 电力建设, 2022, 43(7): 63-72.
[6]	卜飞飞, 王圆圆, 白宏坤, 华远鹏, 韩丁, 王涵, 贾一博, 辛玉华. 基于区块链的需求侧响应方案设计与实现[J]. 电力建设, 2022, 43(5): 79-89.
[7]	钟士元, 张文锦, 罗路平, 王伟, 肖异瑶, 廖志伟. 基于行业聚类电量曲线分解的中期负荷预测[J]. 电力建设, 2022, 43(2): 81-88.
[8]	刘文杰, 刘禾, 王英男, 杨国田, 李新利. 基于完整自适应噪声集成经验模态分解的LSTM-Attention网络短期电力负荷预测方法[J]. 电力建设, 2022, 43(2): 98-108.
[9]	赵允文, 李鹏, 孙煜皓, 沈鑫, 杨晓华. 基于相空间重构和随机配置网络的电力负荷短期预测[J]. 电力建设, 2021, 42(9): 120-128.
[10]	黄冬梅, 庄兴科, 胡安铎, 孙锦中, 时帅, 孙园, 唐振. 基于灰色关联分析和K均值聚类的短期负荷预测[J]. 电力建设, 2021, 42(7): 110-117.
[11]	王哲, 万宝, 凌天晗, 董晓红, 穆云飞, 邓友均, 唐舒懿. 基于谱聚类和LSTM神经网络的电动公交车充电负荷预测方法[J]. 电力建设, 2021, 42(6): 58-66.
[12]	段秦刚, 陈永椿, 王一, 吴明兴, 别佩, 董萍. 电力现货市场下激励型需求响应交易机制及出清模型[J]. 电力建设, 2021, 42(6): 145-156.
[13]	刘敦楠, 张悦, 彭晓峰, 刘明光, 王文, 加鹤萍, 秦光宇, 王峻, 杨烨. 计及相似日与气象因素的电动汽车充电负荷聚类预测[J]. 电力建设, 2021, 42(2): 43-49.
[14]	陶诗洋, 洪沅伸, 张天辰, 仝霞, 王馨, 蔡宏伟. 计及源-荷多灵活备用资源的随机优化调度[J]. 电力建设, 2021, 42(12): 39-48.
[15]	苏宁, 李笑彤, 梁惠施, 贡晓旭, 吕风波, 席嫣娜, 齐步洋. 基于空调负荷数据挖掘的配电网扩展规划方法[J]. 电力建设, 2021, 42(11): 100-107.