新型电力系统灵活性资源聚合两阶段调度优化模型

doi:10.12204/j.issn.1000-7229.2023.02.003

电力建设 ›› 2023, Vol. 44 ›› Issue (2): 25-37.doi: 10.12204/j.issn.1000-7229.2023.02.003

• 面向新型电力系统的灵活性资源发展关键技术及应用·栏目主持鞠立伟副教授、谭忠富教授· • 上一篇下一篇

新型电力系统灵活性资源聚合两阶段调度优化模型

樊伟¹(), 李旭东¹(), 王尧²(), 李祥光¹(), 王玉洁¹(), 谭忠富¹(), 鞠立伟¹()

1.华北电力大学经济与管理学院,北京市 102206
2.国网山西省电力公司经济技术研究院,太原市 030000

收稿日期:2022-05-24 出版日期:2023-02-01 发布日期:2023-01-30
通讯作者: 樊伟(1995),男,博士研究生,主要研究方向为能源经济、电力技术经济,E-mail:fanwei@ncepu.edu.cn。
作者简介:李旭东(1998),男,硕士研究生,主要研究方向为能源经济,E-mail:1169771877@qq.com;
王尧(1990),男,博士,主要研究方向为电力技术经济、电网规划,E-mail:wangyaoncepu@163.com;
李祥光(1999),女,硕士研究生,主要研究方向为能源经济、电力技术经济,E-mail:lixiangguang1666@163.com;
王玉洁(1997),女,硕士研究生,主要研究方向为电力技术经济,E-mail:17336320256@163.com;
谭忠富(1964),男,博士,教授,主要研究方向为能源经济、电力技术经济,E-mail:tanzhongfu@sina.com;
鞠立伟(1989),男,博士,副教授,主要研究方向为能源经济、电力技术经济,E-mail:hdlw_ju@ncepu.edu.cn。
基金资助:
国家电网公司科技项目(520533220008)

Two-Stage Scheduling Optimization Model of Flexible Resource Aggregation in New Power System

FAN Wei¹(), LI Xudong¹(), WANG Yao²(), LI Xiangguang¹(), WANG Yujie¹(), TAN Zhongfu¹(), JU Liwei¹()

1. School of Economics and Management, North China Electric Power University, Beijing 102206, China
2. Economic Research Institute, State Grid Shanxi Electric Power Company, Taiyuan 030000, China

Received:2022-05-24 Online:2023-02-01 Published:2023-01-30
Supported by:
State Grid Corporation of China Research Program(520533220008)

摘要/Abstract

摘要：

构建以新能源为主体的新型电力系统是实现碳达峰、碳中和目标的关键驱动力。传统的以可控煤电装机为主导的电源结构,转变为以强不确定性、弱可控的新能源为主体的新型电力系统,将面临着灵活性资源短缺等挑战。以提升新型电力系统灵活性为导向,提出了灵活性资源聚合两阶段调度优化模型。第一阶段模型考虑分时价格型需求响应,以净负荷波动最小为目标平滑负荷曲线;第二阶段模型考虑分段激励型需求响应市场交易机制,融合电化学储能、抽水蓄能、改造火电等灵活性资源,以系统运行成本最小为目标设计最优运行方案。最后,算例结果和场景对比表明,需求响应能够充分挖掘负荷跟随系统调节的互动能力;改造后火电机组能够降低煤耗水平,提高调节能力,加强与系统灵活性需求时空匹配;各类储能积极响应电力系统调峰,促进了新能源消纳。

关键词: 新型电力系统, 灵活性资源, 抽水蓄能, 需求响应, 两阶段

Abstract:

The construction of the new power system dominated by new energy is a key driver to achieve the goal of carbon peaking and carbon neutrality. The traditional power supply structure dominated by controllable coal-fired power installations is transformed into a new power system dominated by new energy with strong uncertainty and weak controllability, which will face challenges such as shortage of flexibility resources. In order to improve the flexibility of the new power system, a two-stage scheduling optimization model of flexible resource aggregation is proposed in this paper. The first stage model considers time-of-price demand response, and takes the minimum net load fluctuation as the goal to smooth the load curve. The second stage model considers segmented incentive-based demand-response market trading mechanism, integrates flexibility resources such as electrochemical energy storage, pumped storage, and reformed thermal power, and designs the optimal operation scheme with the objective of minimizing system operation cost. Finally, the calculation result and scenario comparison show that demand response can fully exploit the interactive ability of load following system regulation. The reformed thermal power units can reduce coal consumption level, improve regulation ability, and enhance the spatial and temporal matching with system flexibility demand. Various types of energy storage actively respond to the power system peak regulation. Taking pumped storage power station as an example, the reservoir storage capacity presents the shape of "double peaks and double valleys" in the dispatching cycle.

Key words: new power system, flexible resource, pumped storage, demand response, two-stage

中图分类号:

TM73

樊伟, 李旭东, 王尧, 李祥光, 王玉洁, 谭忠富, 鞠立伟. 新型电力系统灵活性资源聚合两阶段调度优化模型[J]. 电力建设, 2023, 44(2): 25-37.

FAN Wei, LI Xudong, WANG Yao, LI Xiangguang, WANG Yujie, TAN Zhongfu, JU Liwei. Two-Stage Scheduling Optimization Model of Flexible Resource Aggregation in New Power System[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(2): 25-37.

图/表 23

图1 灵活性资源聚合系统结构

Fig.1 Structure of flexible resource aggregation system

图2 两阶段优化框架

Fig.2 Framework of two-stage optimization

图3 IBDR分段申报削减方案

Fig.3 Scheme of IBDR subsection declaration reduction

图4 系统负荷及风光预测功率

Fig.4 System load and forecasting power of WT and PV

表1 分时电价

Table 1 Time-of-use price

表2 弹性系数矩阵

Table 2 Elastic coefficient matrix

图5 原净负荷曲线与PBDR后净负荷曲线

Fig.5 Original net load curve and net load curve after PBDR

图6 原负荷曲线与PBDR后负荷曲线

Fig.6 Original load curve and load curve after PBDR

图7 火电机组的启停状态

Fig.7 Startup and shutdown status of thermal power units

图8 火电机组的输出功率

Fig.8 Output power of thermal power units

图9 电化学储能的充放电功率及蓄电量

Fig.9 Charge and discharge power and storage capacity of electrochemical energy storage

图10 抽水蓄能电站的充放电功率

Fig.10 Charge and discharge power of pumped storage power station

图11 抽水量、耗水量、上下水库蓄水量

Fig.11 Water extraction, consumption and storage capacity of upper and lower reservoirs

图12 分段激励性需求响应计划

Fig.12 Segmented incentive demand-response plan

图13 各类灵活性资源可提供的上、下备用

Fig.13 Upper and lower standby capacity available from various flexible resources

表3 场景设置

Table 3 Scenario settings

图14 各场景运行成本对比

Fig.14 Comparison of operating costs of various scenarios

表4 各场景火电启停次数及煤耗

Table 4 Startup and shutdown times and coal consumption of thermal power in each scenario

图15 场景III各类资源提供的上、下备用

Fig.15 Upper and lower standby capacity provided by various resources in scenario III

表A1 灵活性改造火电机组参数

Table A1 Parameters of thermal power unit for flexible transformation

图A1 场景II中TP启停状况

Fig.A1 TP startup and shutdown conditions in scenario II

图A2 场景III中TP启停状况

Fig.A2 TP startup and shutdown conditions in scenario III

图A3 场景IV中TP启停状况

Fig.A3 TP startup and shutdown conditions in scenario IV

参考文献 28

[1]	郑亚先, 杨争林, 冯树海, 等. 碳达峰目标场景下全国统一电力市场关键问题分析[J]. 电网技术, 2022, 46(1): 1-20.
	ZHENG Yaxian, YANG Zhenglin, FENG Shuhai, et al. Key issue analysis in national unified power market under target scenario of carbon emission peak[J]. Power System Technology, 2022, 46(1): 1-20.
[2]	李宏仲, 汪瑶, 胡哲晟, 等. 双碳背景下配电网对多元接入体的承载能力评估[J]. 电网技术, 2022, 46(9): 3595-3604.
	LI Hongzhong, WANG Yao, HU Zhesheng, et al. Carrying capacity assessment of distribution network for multiple access bodies under the background of double carbon[J]. Power System Technology, 2022, 46(9): 3595-3604.
[3]	任凯奇, 张东英, 黄越辉, 等. 基于新能源出力比例的大规模系统惯量估计[J]. 电网技术, 2022, 46(4): 1307-1315.
	REN Kaiqi, ZHANG Dongying, HUANG Yuehui, et al. Large-scale system inertia estimation based on new energy output ratio[J]. Power System Technology, 2022, 46(4): 1307-1315.
[4]	陈光宇, 吴文龙, 戴则梅, 等. 计及故障场景集的风光储混合系统区域无功储备多目标优化[J]. 电力系统自动化, 2022, 46(17):194-204.
	CHEN Guangyu, WU Wenlong, DAI Zemei, et al. Multi-objective optimization of regional reactive power reserve in hybrid system with wind, photovoltaic and energy storage considering fault scenario set[J]. Automation of Electric Power Systems, 2022, 46(17):194-204.
[5]	杨寅平, 曾沅, 秦超, 等. 面向深度调峰的火电机组灵活性改造规划模型[J]. 电力系统自动化, 2021, 45(17): 79-88.
	YANG Yinping, ZENG Yuan, QIN Chao, et al. Planning model for flexibility reformation of thermal power units for deep peak regulation[J]. Automation of Electric Power Systems, 2021, 45(17): 79-88.
[6]	李兴华, 牛拥军, 雷鸣, 等. 火电机组脱硫系统超低排放改造节能优化[J]. 热力发电, 2017, 46(11): 119-123.
	LI Xinghua, NIU Yongjun, LEI Ming, et al. Energy-saving optimization of ultra-low emission retrofit for desulfurization system in thermal power plants[J]. Thermal Power Generation, 2017, 46(11): 119-123.
[7]	施雄华, 薛忠, 唐健, 等. 考虑火电调峰资源协调优化的广西调峰辅助服务市场实践[J]. 电力系统自动化, 2021, 45(22): 183-190.
	SHI Xionghua, XUE Zhong, TANG Jian, et al. Practice of peak regulation ancillary service market in Guangxi of China considering coordinative optimization of thermal power peak regulation resources[J]. Automation of Electric Power Systems, 2021, 45(22): 183-190.
[8]	潘尔生, 田雪沁, 徐彤, 等. 火电灵活性改造的现状、关键问题与发展前景[J]. 电力建设, 2020, 41(9): 58-68. doi: 10.12204/j.issn.1000-7229.2020.09.007
	PAN Ersheng, TIAN Xueqin, XU Tong, et al. Status, critical problems and prospects of flexibility retrofit of thermal power in China[J]. Electric Power Construction, 2020, 41(9): 58-68. doi: 10.12204/j.issn.1000-7229.2020.09.007
[9]	赵书强, 吴杨, 李志伟, 等. 考虑风光出力不确定性的电力系统调峰能力及经济性分析[J]. 电网技术, 2022, 46(5): 1752-1761.
	ZHAO Shuqiang, WU Yang, LI Zhiwei, et al. Analysis of power system peaking capacity and economy considering uncertainty of wind and solar output[J]. Power System Technology, 2022, 46(5): 1752-1761.
[10]	潘华, 姚正, 黄玲玲, 等. 考虑需求响应和储能的风水火联合系统经济调度[J]. 科学技术与工程, 2021, 21(19): 8039-8047.
	PAN Hua, YAO Zheng, HUANG Lingling, et al. Economic dispatching of wind-hydro-thermal combined system considering demand response and energy storage[J]. Science Technology and Engineering, 2021, 21(19): 8039-8047.
[11]	杨修宇, 穆钢, 柴国峰, 等. 考虑灵活性供需平衡的源-储-网一体化规划方法[J]. 电网技术, 2020, 44(9): 3238-3246.
	YANG Xiuyu, MU Gang, CHAI Guofeng, et al. Source-storage-grid integrated planning considering flexible supply-demand balance[J]. Power System Technology, 2020, 44(9): 3238-3246.
[12]	程孟增, 唐一金, 商文颖, 等. 风-光-火-抽蓄联合系统中抽水蓄能电站最佳容量配置[J]. 电力建设, 2021, 42(11): 72-81. doi: 10.12204/j.issn.1000-7229.2021.11.008
	CHENG Mengzeng, TANG Yijin, SHANG Wenying, et al. Optimal capacity configuration of pumped-storage power station in wind-PV-fire-pump storage system[J]. Electric Power Construction, 2021, 42(11): 72-81. doi: 10.12204/j.issn.1000-7229.2021.11.008
[13]	夏依莎, 刘俊勇, 刘继春, 等. 基于电量共享的梯级水光蓄联合发电系统优化调度策略[J]. 电力自动化设备, 2021, 41(9): 118-125.
	XIA Yisha, LIU Junyong, LIU Jichun, et al. Optimal scheduling strategy of cascaded hydro-photovoltaic-pumped storage hybrid generation system based on electric energy sharing[J]. Electric Power Automation Equipment, 2021, 41(9): 118-125.
[14]	张军六, 樊伟, 谭忠富, 等. 计及需求响应的气电互联虚拟电厂多目标调度优化模型[J]. 电力建设, 2020, 41(2): 1-10. doi: 10.3969/j.issn.1000-7229.2020.02.001
	ZHANG Junliu, FAN Wei, TAN Zhongfu, et al. Multi-objective optimization model of gas-electricity interconnected virtual power plant considering demand response[J]. Electric Power Construction, 2020, 41(2): 1-10. doi: 10.3969/j.issn.1000-7229.2020.02.001
[15]	王宝, 叶斌, 朱刘柱, 等. 市场环境下我国电力需求响应实践与探索[J]. 电力需求侧管理, 2021, 23(5): 91-95.
	WANG Bao, YE Bin, ZHU Liuzhu, et al. Practice and exploration of China’s power demand response in electricity market[J]. Power Demand Side Management, 2021, 23(5): 91-95.
[16]	白雪敏, 张如意, 徐璐. 电改背景下售电商分时电价优化策略研究: 考虑用户侧价格型需求响应的分析[J]. 价格理论与实践, 2021(10): 54-57, 193.
	BAI Xuemin, ZHANG Ruyi, XU Lu. Research on optimization strategy of time-of-use electricity price for e-commerce sales under the background of electricity reform:analysis considering user-side and price-based demand response[J]. Price: Theory & Practice, 2021(10): 54-57, 193.
[17]	张宇华, 王丛, 孙晓鹏, 等. 基于多步逆向半云模型的价格型需求响应不确定性建模[J/OL]. 电力系统自动化:1-11. (2022-04-13)[2022-04-18]. http://kns.cnki.net/kcms/detail/32.1180.TP.20220412.0957.004.html.
	ZHANG Yuhua, WANG Cong, SUN Xiaopeng, et al. Multi-step backward semi-cloud model-based uncertainty modeling for price-based demand response[J/OL]. Automation of Electric Power Systems: 1-11. (2022-04-13)[2022-04-18]. http://kns.cnki.net/kcms/detail/32.1180.TP.20220412.0957.004.html.
[18]	陈中瑶, 林凯颖, 王蓓蓓, 等. 单侧放开市场中基于Stackelberg均衡分析的最低效率损失需求响应激励机制设计[J]. 电网技术, 2022, 46(5): 1790-1800.
	CHEN Zhongyao, LIN Kaiying, WANG Beibei, et al. Design of demand response incentive mechanism considering lowest efficiency loss based on stackelberg equilibrium analysis in unilateral liberalization market[J]. Power System Technology, 2022, 46(5): 1790-1800.
[19]	杨晓萍, 张凡凡, 解骞, 等. 基于激励型需求响应的主动配电网态势利导方法[J]. 太阳能学报, 2022, 43(3): 133-140.
	YANG Xiaoping, ZHANG Fanfan, XIE Qian, et al. Situation orientation method of active distribution network based on incentive demand response[J]. Acta Energiae Solaris Sinica, 2022, 43(3): 133-140.
[20]	柴剑雪, 张立辉, 聂青云, 等. 生态文明建设下风、光发电生态效益模拟与评估: 以甘肃省为例[J]. 生态经济, 2021, 37(1): 136-146.
	CHAI Jianxue, ZHANG Lihui, NIE Qingyun, et al. Simulation and assessment of the ecological benefits of wind power and photovoltaic power under ecological civilization construction: a case study of Gansu Province[J]. Ecological Economy, 2021, 37(1): 136-146.
[21]	国家发展和改革委员会. 关于完善水电上网电价形成机制的通知[EB/OL].(2014-01-23)[2022-04-18]. https://news.bjx.com.cn/html/20140123/488422.shtml.
[22]	张钰. “双碳”目标下抽水蓄能电价政策研究: 以沂蒙抽水蓄能电站项目为例[J]. 价格理论与实践, 2021(12): 35-37, 158.
	ZHANG Yu. Research on the price policy of pumping storage power under the target of double carbon: take Yimeng pumping water storage power station project as an example[J]. Price (Theory & Practice), 2021(12): 35-37, 158.
[23]	刘长义, 谢勇刚. 抽水蓄能在新型电力系统中的功能作用分析[J]. 水电与抽水蓄能, 2021, 7(6): 7-10.
	LIU Changyi, XIE Yonggang. Analysis on the function and effect of pumped storage in new power system[J]. Hydropower and Pumped Storage, 2021, 7(6): 7-10.
[24]	王博, 詹红霞, 张勇, 等. 考虑风电不确定性的风蓄火联合优化经济调度研究[J]. 电力工程技术, 2022, 41(1): 93-100.
	WANG Bo, ZHAN Hongxia, ZHANG Yong, et al. Combined optimal economic dispatch of wind-storage-fire considering wind power uncertainty[J]. Electric Power Engineering Technology, 2022, 41(1): 93-100.
[25]	赵栩, 刘继春, 张帅. 市场环境下光伏抽蓄联合电站的优化运行与收益分配策略[J]. 水电能源科学, 2021, 39(8): 208-212.
	ZHAO Xu, LIU Jichun, ZHANG Shuai. Optimal operation and profit distribution strategy of photovoltaic pumped storage complementary plant under market condition[J]. Water Resources and Power, 2021, 39(8): 208-212.
[26]	崔杨, 修志坚, 刘闯, 等. 计及需求响应与火-储深度调峰定价策略的电力系统双层优化调度[J]. 中国电机工程学报, 2021, 41(13): 4403-4415.
	CUI Yang, XIU Zhijian, LIU Chuang, et al. Dual level optimal dispatch of power system considering demand response and pricing strategy on deep peak regulation[J]. Proceedings of the CSEE, 2021, 41(13): 4403-4415.
[27]	许博, 岳欣明, 关艳, 等. 针对用电负荷“峰谷倒挂”现象的混合型电力需求响应策略[J]. 系统工程理论与实践, 2022, 42(8): 2129-2138. doi: 10.12011/SETP2021-1896
	XU Bo, YUE Xinming, GUAN Yan, et al. A hybrid demand response strategy to solve the problem of “peak valley inversion” in power grid[J]. Systems Engineering-Theory & Practice, 2022, 42(8): 2129-2138.
[28]	李明轩, 齐步洋, 贺大玮. 工业园区需求响应资源聚合优化配置方法[J]. 电网技术, 2022, 46(9): 3543-3549.
	LI Mingxuan, QI Buyang, HE Dawei. Optimization of aggregation of demand response resources in industrial park[J]. Power System Technology, 2022, 46(9): 3543-3549.

新型电力系统灵活性资源聚合两阶段调度优化模型

Two-Stage Scheduling Optimization Model of Flexible Resource Aggregation in New Power System

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 23

参考文献 28

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张逸, 吴逸帆, 陈晶腾. 新型电力系统背景下电压暂降风险评估技术挑战与展望[J]. 电力建设, 2023, 44(2): 15-24.
[2]	靳冰洁, 李家兴, 彭虹桥, 罗澍忻, 卢治霖, 冷媛, 董萍, 梁梓杨. 需求响应下计及电碳市场耦合的多元主体成本效益分析[J]. 电力建设, 2023, 44(2): 50-60.
[3]	于昊正, 赵寒杰, 李科, 朱星旭, 皇甫霄文, 樊江川, 李翠萍, 李军徽. 计及需求响应的分布式光伏集群承载能力评估[J]. 电力建设, 2023, 44(2): 122-130.
[4]	余彬, 翁利国, 练德强, 王思斌, 黄媛, 姚昊天. 计及价格引导机制的含分布式资源园区协调运行策略[J]. 电力建设, 2023, 44(2): 145-154.
[5]	张尧翔, 刘文颖, 庞清仑, 李亚楼, 安宁, 李芳. 计及综合需求响应参与消纳受阻新能源的多时间尺度优化调度策略[J]. 电力建设, 2023, 44(1): 1-11.
[6]	温钱惠, 魏震波, 张勇林, 梁政, 何永祥. 考虑保险机制与用户需求响应的售电公司优化运营[J]. 电力建设, 2023, 44(1): 47-54.
[7]	刘至纯, 李华强, 王俊翔, 陆杨, 游祥, 何永祥. 基于多时间尺度综合需求响应策略的综合能源系统优化运行[J]. 电力建设, 2022, 43(9): 54-65.
[8]	杨志豪, 刘沆, 文明, 赵海彭, 廖菁, 苗世洪. 考虑综合需求响应混合不确定性的综合能源系统优化调度策略[J]. 电力建设, 2022, 43(9): 66-76.
[9]	苏鹏, 陈璐, 吴坚, 刘鑫, 马继涛. 新型电力系统多能源能量惯性动态优化控制模型[J]. 电力建设, 2022, 43(9): 87-93.
[10]	范辉, 罗蓬, 王弘利, 梁纪峰, 李乾, 杨军, 吴赋章. 基于社交网上演化博弈的光伏台区用户需求响应特性研究[J]. 电力建设, 2022, 43(8): 150-158.
[11]	刁利, 李光, 宋雪莹, 德格吉日夫, 谭忠富. 新能源分比例渗透下基于博弈的多主体效益综合评估方法[J]. 电力建设, 2022, 43(6): 43-55.
[12]	徐加银, 汪涛, 王加庆, 刘琛, 马英浩. 考虑微电网灵活性资源属性的配电网规划方法[J]. 电力建设, 2022, 43(6): 84-92.
[13]	王萍萍, 许建中, 闫庆友, 林宏宇. 计及灵活性负荷资源需求响应和不确定性的楼宇微网调度双层优化模型[J]. 电力建设, 2022, 43(6): 128-140.
[14]	兰洲, 蒋晨威, 谷纪亭, 文福拴, 杨侃, 王坤. 促进可再生能源发电消纳和碳减排的数据中心优化调度与需求响应策略[J]. 电力建设, 2022, 43(4): 1-9.
[15]	李彬, 魏吟娬, 祁兵, 孙毅, 陈宋宋. 基于EEMD-LSTM的需求响应终端DDoS攻击检测方法[J]. 电力建设, 2022, 43(4): 81-90.