计及源-荷多灵活备用资源的随机优化调度

doi:10.12204/j.issn.1000-7229.2021.12.005

电力建设 ›› 2021, Vol. 42 ›› Issue (12): 39-48.doi: 10.12204/j.issn.1000-7229.2021.12.005

• 高渗透率可再生能源发电及其先进并网技术·栏目主持张兴教授、李飞副教授· • 上一篇下一篇

计及源-荷多灵活备用资源的随机优化调度

陶诗洋(), 洪沅伸, 张天辰, 仝霞, 王馨, 蔡宏伟

国网北京市电力公司电力科学研究院, 北京市 100075

收稿日期:2021-03-31 出版日期:2021-12-01 发布日期:2021-11-26
通讯作者: 陶诗洋 E-mail:241866187@qq.com
作者简介:洪沅伸(1974),男,硕士,高级工程师,主要研究方向为需求侧响应;
张天辰(1988),男,硕士,工程师,主要从事电力系统优化调度工作;
仝霞(1981),女,学士,高级工程师,主要研究方向为电力系统分析与控制;
王馨(1983),女,学士,工程师,主要研究方向为电力系统分析与控制;
蔡宏伟(1978),男,学士,工程师,主要从事电力系统优化调度工作。
基金资助:
国家重点研发计划项目(2016YFB0900500)

Stochastic Optimal Scheduling Considering Multiple Flexible Reserve Resources on Both Source and Load Sides

TAO Shiyang(), HONG Yuanshen, ZHANG Tianchen, TONG Xia, WANG Xin, CAI Hongwei

Electric Power Research Institute of State Grid Beijing Electric Power Company, Beijing 100075, China

Received:2021-03-31 Online:2021-12-01 Published:2021-11-26
Contact: TAO Shiyang E-mail:241866187@qq.com
Supported by:
National Key Research and Development Program of China(2016YFB0900500)

摘要/Abstract

摘要：

大规模新能源并网给电力系统的调度运行带来了新的挑战。为缓解系统的备用压力,提出一种计及源-荷多灵活备用资源的随机优化调度方法。首先,基于场景生成方法建立可变场景模型,考虑了风电并网容量和光伏并网面积对新能源出力不确定性的影响。其次,建立电力系统中多种灵活资源的备用模型:在源侧,分别建立常规机组和风电/光伏的备用模型,并考虑了风电/光伏备用的不确定性;在负荷侧,引入激励型需求响应,对需求侧备用进行建模。然后,基于两阶段随机优化方法建立备用调度模型。该模型考虑了日前的运行和备用决策以及日内不确定场景下的弃风、弃光以及切负荷风险。最后,基于改进的IEEE RTS-24测试系统验证了所提模型的有效性。

关键词: 发电-备用调度, 两阶段随机优化, 风电/光伏备用, 激励型需求响应

Abstract:

High proportion of new energy grid-connected brings new challenges to the dispatch and operation of power systems. In order to alleviate the reserve pressure of power systems, a stochastic optimal scheduling model considering multiple flexible reserve resources on both source and load sides is proposed. Firstly, a model of variable scenario is established on the basis of scenario generation method, which considers the influence of the capacity of grid-connected wind power and the area of grid-connected photovoltaic power on the uncertainty of new energy generation. Then, the reserve model of various flexible resources in the system is established. On the source side, the reserve models of conventional units, photovoltaic and wind power are established, respectively, and the uncertainty of wind power and photovoltaic reserve is considered; On the load side, the reserve model is established on the basis of the incentive demand response. Then, the reserve dispatching model is established on the basis of the two-stage stochastic optimization model. The model considers the day-ahead operation and reserve decisions, as well as wind power curtailment, photovoltaic power curtailment, and load shedding risks in uncertain scenarios within the day. Finally, the case studies based on the modified IEEE RTS-24 system verify the effectiveness of the proposed model.

Key words: dispatching of generation and reserve, two-stage stochastic optimization, wind power and photovoltaic reserve, incentive-based demand response

中图分类号:

TM74

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.

TAO Shiyang, HONG Yuanshen, ZHANG Tianchen, TONG Xia, WANG Xin, CAI Hongwei. Stochastic Optimal Scheduling Considering Multiple Flexible Reserve Resources on Both Source and Load Sides[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(12): 39-48.

图/表 12

图1 激励型DR报价曲线

Fig.1 Bidding curve of IDR

图2 两阶段随机备用调度模型的求解流程

Fig.2 Solving process of the two-stage stochastic reserve scheduling model

图3 预测风电和光伏发电数据

Fig.3 Forecast data of wind power and photovoltaic power

表1 激励型DR参数

Table 1 Parameters of IDR

表2 对比模型设置

Table 2 Settings of comparison models

表3 Optimization results of five models美元

Table 3

图4 模型1和模型2中风电出力的不确定场景

Fig.4 Uncertain scenarios of wind power output in model 1 and model 2

图5 4种模型的备用优化结果

Fig.5 Reserve optimization results of four models

表4 各模型的新能源消纳比例

Table 4 Consumption rates of new energy power of five models%

表5 Optimization results considering insufficient conventional unit reserve美元

Table 5

图6 运行成本和计算时间随场景数的变化

Fig.6 Change of operation result and calculation time with the number of scenarios

表A1 常规机组参数

Table A1 Parameters of conventional generators

参考文献 22

[1]	应益强, 王正风, 吴旭, 等. 计及新能源随机特性的电网深度调峰多目标策略[J]. 电力系统保护与控制, 2020, 48(6): 34-42.
	YING Yiqiang, WANG Zhengfeng, WU Xu, et al. Multi-objective strategy for deep peak shaving of power grid considering uncertainty of new energy[J]. Power System Protection and Control, 2020, 48(6): 34-42.
[2]	Renewables 2020 analysis and forecast to 2025[EB/OL].(2020-11-12)[2021-05-15]. https://www.iea.org/reports/renewables-2020.
[3]	2020年全国电力工业统计数据[EB/OL]. (2021-01-20)[2021-05-15]. http://www.nea.gov.cn/2021-01/20/c_139683739.htm.
[4]	朱涛, 陈嘉俊, 段秦刚, 等. 基于近似动态规划的工业园区源-网-荷-储联合运行在线优化算法[J]. 电网技术, 2020, 44(10): 3744-3752.
	ZHU Tao, CHEN Jiajun, DUAN Qingang, et al. Approximate dynamic programming-based online algorithm for combination operation of source-network-load-storage in the industrial park[J]. Power System Technology, 2020, 44(10): 3744-3752.
[5]	彭春华, 余愿, 孙惠娟. 基于源网荷协同优化的配电网光储联合系统规划[J]. 电网技术, 2019, 43(11): 3944-3951.
	PENG Chunhua, YU Yuan, SUN Huijuan. Planning of combined PV-ESS system for distribution network based on source-network-load collaborative optimization[J]. Power System Technology, 2019, 43(11): 3944-3951.
[6]	HEDAYATI-MEHDIABADI M, ZHANG J S, HEDMAN K W. Wind power dispatch margin for flexible energy and reserve scheduling with increased wind generation[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4): 1543-1552. doi: 10.1109/TSTE.2015.2455552 URL
[7]	林峰, 汪震, 王冠中, 等. 考虑风电降载的电力系统鲁棒备用调度模型[J]. 电力系统自动化, 2018, 42(19): 64-70.
	LIN Feng, WANG Zhen, WANG Guanzhong, et al. Robust reserve scheduling model of electric power system considering WTG de-loading capability[J]. Automation of Electric Power Systems, 2018, 42(19): 64-70.
[8]	DVORKIN Y, ORTEGA-VAZUQEZ M A, KIRSCHEN D S. Wind generation as a reserve provider[J]. IET Generation, Transmission & Distribution, 2015, 9(8): 779-787. doi: 10.1049/iet-gtd.2014.0614 URL
[9]	LIN Y, DING Y, SONG Y H, et al. A multi-state model for exploiting the reserve capability of wind power[J]. IEEE Transactions on Power Systems, 2018, 33(3): 3358-3372. doi: 10.1109/TPWRS.2017.2775188 URL
[10]	刘小聪, 王蓓蓓, 李扬, 等. 计及需求侧资源的大规模风电消纳随机机组组合模型[J]. 中国电机工程学报, 2015, 35(14): 3714-3723.
	LIU Xiaocong, WANG Beibei, LI Yang, et al. Stochastic unit commitment model for high wind power integration considering demand side resources[J]. Proceedings of the CSEE, 2015, 35(14): 3714-3723.
[11]	FALSAFI H, ZAKARIAZADEH A, JADID S. The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming[J]. Energy, 2014, 64: 853-867. doi: 10.1016/j.energy.2013.10.034 URL
[12]	陈哲, 张伊宁, 马光, 等. 计及需求侧响应日前—日内两阶段鲁棒备用优化[J]. 电力系统自动化, 2019, 43(24): 67-76.
	CHEN Zhe, ZHANG Yining, MA Guang, et al. Two-stage day-ahead and intra-day robust reserve optimization considering demand response[J]. Automation of Electric Power Systems, 2019, 43(24): 67-76.
[13]	苏俊妮, 张鑫, 赵俊炜, 等. 计及需求响应和储能的电-气综合能源系统优化调度[J]. 电力建设, 2020, 41(5): 1-8.
	SU Junni, ZHANG Xin, ZHAO Junwei, et al. Optimal dispatching of integrated electricity-gas system considering demand response and energy storage[J]. Electric Power Construction, 2020, 41(5): 1-8.
[14]	ZOU J K, AHMED S, SUN X A. Multistage stochastic unit commitment using stochastic dual dynamic integer programming[J]. IEEE Transactions on Power Systems, 2019, 34(3): 1814-1823. doi: 10.1109/TPWRS.2018.2880996 URL
[15]	BIENSTOCK D, CHERTKOV M, HARNETT S. Chance-constrained optimal power flow: Risk-aware network control under uncertainty[J]. SIAM Review, 2014, 56(3): 461-495. doi: 10.1137/130910312 URL
[16]	YU H, CHUNG C Y, WONG K P, et al. Probabilistic load flow evaluation with hybrid Latin hypercube sampling and cholesky decomposition[J]. IEEE Transactions on Power Systems, 2009, 24(2): 661-667. doi: 10.1109/TPWRS.2009.2016589 URL
[17]	PAPPALA V S, ERLICH I, ROHRIG K, et al. A stochastic model for the optimal operation of a wind-thermal power system[J]. IEEE Transactions on Power Systems, 2009, 24(2): 940-950. doi: 10.1109/TPWRS.2009.2016504 URL
[18]	MA X Y, SUN Y Z, FANG H L. Scenario generation of wind power based on statistical uncertainty and variability[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4): 894-904. doi: 10.1109/TSTE.2013.2256807 URL
[19]	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.
[20]	黄华, 李琦, 陈宝平, 等. 基于推广超分位数的多随机因素两阶段联合优化调度[J]. 电测与仪表, 2018, 55(9): 113-120.
	HUANG Hua, LI Qi, CHEN Baoping, et al. Two-stage joint optimal dispatch of multiple uncertainties based on generalized alpha quantile method[J]. Electrical Measurement & Instrumentation, 2018, 55(9): 113-120.
[21]	许梦瑶, 艾小猛, 方家琨, 等. 考虑用户积极性的电动汽车与机组联合调频的两阶段随机优化调度模型[J/OL]. 电网技术, 2021[2021-11-01]. https://doi.org/10.13335/j.1000-3673.pst.2021.0922.
	XU Mengyao, AI Xiaomeng, FANG Jiakun, et al. Two-stage stochastic optimal scheduling model for joint regulation of EV and thermal units considering users enthusiasm[J/OL]. Power System Technology, 2021[2021-11-01]. https://doi.org/10.13335/j.1000-3673.pst.2021.0922.
[22]	刘斌, 刘锋, 王程, 等. 考虑风电场灵活性及出力不确定性的机组组合[J]. 电网技术, 2015, 39(3): 730-736.
	LIU Bin, LIU Feng, WANG Cheng, et al. Unit commitment considering flexibility and uncertainty of wind power generation[J]. Power System Technology, 2015, 39(3): 730-736.

计及源-荷多灵活备用资源的随机优化调度

Stochastic Optimal Scheduling Considering Multiple Flexible Reserve Resources on Both Source and Load Sides

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