基于多时间尺度随机优化的年度调峰辅助服务需求评估模型

doi:10.11930/j.issn.1004-9649.202201044

中国电力 ›› 2022, Vol. 55 ›› Issue (6): 9-17,24.DOI: 10.11930/j.issn.1004-9649.202201044

基于多时间尺度随机优化的年度调峰辅助服务需求评估模型

刘或让¹, 颜伟¹, 林祖贵¹, 谭洪¹, 李国强², 文旭³

1. 重庆大学输配电装备及系统安全与新技术国家重点实验室，重庆　400044;
2. 西藏电力交易中心有限公司，西藏拉萨　850000;
3. 国家电网有限公司西南分部，四川成都　610041

收稿日期:2022-01-14 修回日期:2022-04-14 出版日期:2022-06-28 发布日期:2022-06-18
作者简介:刘或让（1996—），男，硕士研究生，从事电力系统中长期优化调度、调峰辅助服务研究，E-mail：lhrang@sina.com;颜伟（1968—），男，通信作者，博士，教授，博士生导师，从事电力系统调度、交易与规划研究，E-mail：cquyanwei@cqu.edu.cn;林祖贵(1998—)，男，硕士研究生，从事电力系统中长期运行与交易研究，E-mail：linzugui19@163.com;谭洪(1991—)，男，博士研究生，从事电力系统与综合能源系统运行优化研究，E-mail：tan_hong@foxmail.com;文旭(1978—)，男，博士，高级工程师，从事电力系统优化运行及风险评估、清洁能源消纳、能源互联网及电力市场运行与管理研究，E-mail：wenxu@cqu.edu.cn
基金资助:
国家自然科学基金资助项目（51677012）。

An Annual Peak Regulation Auxiliary Service Demand Assessment Model Based on Multi-time Scale Stochastic Optimization

LIU Huorang¹, YAN Wei¹, LIN Zugui¹, TAN Hong¹, LI Guoqiang², WEN Xu³

1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China;
2. Tibet Electric Power Trading Center Co., Ltd., Lhasa 850000, China;
3. Southwest Branch of State Grid corporation of China, Chengdu 610041, China

Received:2022-01-14 Revised:2022-04-14 Online:2022-06-28 Published:2022-06-18
Supported by:
This work is supported by National Natural Science Foundation of China (No.51677012)

摘要/Abstract

摘要： 为探寻电力系统的年度调峰需求，构建了基于多时间尺度随机优化的年度调峰辅助服务需求评估模型。该优化模型包含中长期、短期以及调峰服务3个阶段：在中长期阶段考虑日电量与最大最小负荷的随机性及其平衡关系，优化机组检修、跨日启停及水库水位的中长期计划；在短期阶段考虑源荷小时功率平衡及其波动性，基于中长期计划逐旬优化机组出力；在调峰阶段通过分析日调峰能力与弃水情况实现对辅助服务启动条件的模拟，考虑火电机组深度调峰与启停调峰能力对短期阶段进行矫正优化。基于中国某省级电网实际数据的仿真分析结果表明，所提模型能有效模拟多时间尺度下的源荷随机性，实现年度调峰辅助服务的需求评估。

关键词: 调峰辅助服务市场, 年度发电计划, 多时间尺度优化, 随机优化, 需求评估

Abstract: In order to assess the annual peak regulation demand of power system, an annual peak regulation auxiliary service demand assessment model based on multi-time scale stochastic optimization is constructed, which covers three stages: the mid-and-long term stage, short term stage and peak regulation service stage. For the mid-and-long term stage, the randomness and the balance relationship of daily electricity, maximum and minimum load are considered to optimize the mid-and-long term plan for unit maintenance, cross-day start-stop and reservoir water level. For the short-term stage, the hourly power balance of source-load and its fluctuation is considered to optimize the units output on a ten-day basis according to the mid-and-long term plan. For the peak regulation stage, the start-up conditions of the auxiliary services are simulated through analyzing the daily peak regulation capacity and water curtailment, and the deep peak regulation and start-stop peak regulation capacity of thermal power units is considered to correct and optimize the short-term plan. Based on the actual data of a provincial power grid in China, a simulation analysis is conducted, which shows that the model can effectively simulate the randomness of source-load under multi-time scale, and realize the annual peak regulation demand assessment.

Key words: peak regulation auxiliary service market, annual power generation plan, multi-time scale optimization, stochastic optimization, demand assessment

刘或让, 颜伟, 林祖贵, 谭洪, 李国强, 文旭. 基于多时间尺度随机优化的年度调峰辅助服务需求评估模型[J]. 中国电力, 2022, 55(6): 9-17,24.

LIU Huorang, YAN Wei, LIN Zugui, TAN Hong, LI Guoqiang, WEN Xu. An Annual Peak Regulation Auxiliary Service Demand Assessment Model Based on Multi-time Scale Stochastic Optimization[J]. Electric Power, 2022, 55(6): 9-17,24.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202201044

https://www.electricpower.com.cn/CN/Y2022/V55/I6/9

参考文献

[1] 国家能源局. 完善电力辅助服务补偿(市场)机制工作方案[Z]. 2017.
[2] 华中能源监管局. 川渝一体化电力调峰辅助服务市场运营规则(试行)[Z]. 2021.
[3] 林俐, 邹兰青, 周鹏, 等. 规模风电并网条件下火电机组深度调峰的多角度经济性分析[J]. 电力系统自动化, 2017, 41(7): 21–27
LIN Li, ZOU Lanqing, ZHOU Peng, et al. Multi-angle economic analysis on deep peak regulation of thermal power units with large-scale wind power integration[J]. Automation of Electric Power Systems, 2017, 41(7): 21–27
[4] 李铁, 李正文, 杨俊友, 等. 计及调峰主动性的风光水火储多能系统互补协调优化调度[J]. 电网技术, 2020, 44(10): 3622–3630
LI Tie, LI Zhengwen, YANG Junyou, et al. Coordination and optimal scheduling of multi-energy complementary system considering peak regulation initiative[J]. Power System Technology, 2020, 44(10): 3622–3630
[5] 林俐, 岳晓宇, 许冰倩, 等. 计及抽水蓄能和火电深度调峰效益的抽蓄–火电联合调峰调用顺序及策略[J]. 电网技术, 2021, 45(1): 20–32
LIN Li, YUE Xiaoyu, XU Bingqian, et al. Sequence and strategy of pumped storage-thermal combined peak shaving considering benefits of pumped storage and deep regulation of thermal power[J]. Power System Technology, 2021, 45(1): 20–32
[6] 李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961–3970
LI Junhui, ZHANG Jiahui, MU Gang, et al. Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power System Technology, 2019, 43(11): 3961–3970
[7] 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
[8] 王淑云, 娄素华, 刘文霞, 等. 考虑火电深度调峰的电力系统低碳发电优化研究[J]. 全球能源互联网, 2019, 2(3): 226–231
WANG Shuyun, LOU Suhua, LIU Wenxia, et al. Study on optimization of low-carbon power generation in power system considering the depth peak regulation of thermal power units[J]. Journal of Global Energy Interconnection, 2019, 2(3): 226–231
[9] 刘赫川, 周孝信, 杨小煜, 等. 考虑天然气季节性存储的综合能源系统年度运行方式研究[J]. 中国电力, 2022, 55(4): 145–155
LIU Hechuan, ZHOU Xiaoxin, YANG Xiaoyu, et al. Annual operation mode study of integrated energy system considering seasonal natural gas storage[J]. Electric Power, 2022, 55(4): 145–155
[10] 张新城, 刘志珍, 侯延进, 等. 考虑出行温度影响的电动汽车充电功率需求分析[J]. 电力科学与技术学报, 2021, 36(4): 124–131
ZHANG Xincheng, LIU Zhizhen, HOU Yanjin, et al. Analysis of charging power demand of electric vehicles considering the influence of travel temperature[J]. Journal of Electric Power Science and Technology, 2021, 36(4): 124–131
[11] 邵随拓. 考虑水库月间协调的年度电力电量平衡方法研究[D]. 重庆: 重庆大学, 2019.
SHAO Suituo. Study on annual power supply-demand balancing considering monthly reservoirs coordination[D]. Chongqing: Chongqing University, 2019.
[12] 邵成成, 冯陈佳, 王雅楠, 等. 含大规模清洁能源电力系统的多时间尺度生产模拟[J]. 中国电机工程学报, 2020, 40(19): 6103–6113
SHAO Chengcheng, FENG Chenjia, WANG Yanan, et al. Multiple time-scale production simulation of power system with large-scale renewable energy[J]. Proceedings of the CSEE, 2020, 40(19): 6103–6113
[13] 林弋莎, 孙荣富, 鲁宗相, 等. 考虑中长期电量不确定性的可再生能源系统嵌套运行优化[J]. 电网技术, 2020, 44(9): 3272–3281
LIN Yisha, SUN Rongfu, LU Zongxiang, et al. Medium-and long-term nested scheduling for renewable energy system considering electricity uncertainty[J]. Power System Technology, 2020, 44(9): 3272–3281
[14] 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
[15] 白斌, 韩明亮, 林江, 等. 含风电和光伏的可再生能源场景削减方法[J]. 电力系统保护与控制, 2021, 49(15): 141–149
BAI Bin, HAN Mingliang, LIN Jiang, et al. Scenario reduction method of renewable energy including wind power and photovoltaic[J]. Power System Protection and Control, 2021, 49(15): 141–149
[16] XU J, YI X K, SUN Y Z, et al. Stochastic optimal scheduling based on scenario analysis for wind farms[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1548–1559.
[17] 裘昕月, 朱自伟, 黄春辉, 等. 考虑风电出力不确定性的综合能源系统鲁棒优化[J]. 智慧电力, 2020, 48(5): 1–6,59
QIU Xinyue, ZHU Ziwei, HUANG Chunhui, et al. Robust optimization of integrated energy system considering uncertainty of wind power output[J]. Smart Power, 2020, 48(5): 1–6,59
[18] 郭尊, 李庚银, 周明, 等. 面向风电消纳的电–气联合系统分散协调鲁棒优化调度模型[J]. 中国电机工程学报, 2020, 40(20): 6442–6455
GUO Zun, LI Gengyin, ZHOU Ming, et al. A decentralized and robust optimal scheduling model of integrated electricity-gas system for wind power accommodation[J]. Proceedings of the CSEE, 2020, 40(20): 6442–6455
[19] ODETAYO B, KAZEMI M, MACCORMACK J, et al. A chance constrained programming approach to the integrated planning of electric power generation, natural gas network and storage[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6883–6893.
[20] 李志伟, 赵书强, 刘金山. 基于机会约束目标规划的风-光-水-气-火-储联合优化调度[J]. 电力自动化设备, 2019, 39(8): 214–223
LI Zhiwei, ZHAO Shuqiang, LIU Jinshan. Coordinated optimal dispatch of wind-photovoltaic-hydro-gas-thermal-storage system based on chance-constrained goal programming[J]. Electric Power Automation Equipment, 2019, 39(8): 214–223
[21] 黄越辉, 曲凯, 李驰, 等. 基于K-means MCMC算法的中长期风电时间序列建模方法研究[J]. 电网技术, 2019, 43(7): 2469–2476
HUANG Yuehui, QU Kai, LI Chi, et al. Research on modeling method of medium-and long-term wind power time series based on K-means MCMC algorithm[J]. Power System Technology, 2019, 43(7): 2469–2476
[22] 陶新民, 徐晶, 杨立标, 等. 一种改进的粒子群和K均值混合聚类算法[J]. 电子与信息学报, 2010, 32(1): 92–97
TAO Xinmin, XU Jing, YANG Libiao, et al. Improved cluster algorithm based on K-means and particle swarm optimization[J]. Journal of Electronics & Information Technology, 2010, 32(1): 92–97
[23] 李辉, 任洲洋, 胡博, 等. 基于时序生成对抗网络的月度风光发电功率场景分析方法[J]. 中国电机工程学报, 2022, 42(2): 537–548
LI Hui, REN Zhouyang, HU Bo, et al. A sequential generative adversarial network based monthly scenario analysis method for wind and photovoltaic power[J]. Proceedings of the CSEE, 2022, 42(2): 537–548
[24] 国家能源局华中监管局, 重庆市经济和信息化委员会. 重庆电力辅助服务(调峰)交易规则[A]. 2019.
[25] 张爱枫, 刘或让, 王勇, 等. 计及深度调峰辅助服务与多典型日的年度发电计划优化模型[J/OL]. 重庆大学学报: 1-15[2021-12-07]. http://kns.cnki.net/kcms/detail/50.1044.n.20211201.1034.002.html.
ZHANG Aifeng, LIU Huorang, WANG Yong, et al. Annual power generation plan optimization model considering deep peak regulation auxiliary services and multiple typical days[J/OL]. Journal of Chongqing University. 1-15[2021-12-07]. http://kns.cnki.net/kcms/ detail/50.1044.n.20211201.1034.002.html.
[26] 国家能源局华中监管局. 华中区域并网发电厂辅助服务管理实施细则[A]. 2020.

基于多时间尺度随机优化的年度调峰辅助服务需求评估模型

An Annual Peak Regulation Auxiliary Service Demand Assessment Model Based on Multi-time Scale Stochastic Optimization

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

[1]	张亚健, 陈茨, 薛飞, 马丽, 郑敏. 电制氢协同的含高比例光伏配电网两阶段电压随机优化控制[J]. 中国电力, 2024, 57(8): 23-35.
[2]	李江南, 程韧俐, 周保荣, 刘稼瑾, 毛田, 赵文猛, 王滔, 黄光磊, 许银亮. 含碳捕集及电转氢设备的低碳园区综合能源系统随机优化调度[J]. 中国电力, 2024, 57(5): 149-156.
[3]	李茜, 黄海涛, 晏小彬, 廖长江, 彭欣, 张安安. 海上油气微能系统的低碳优化运行研究[J]. 中国电力, 2023, 56(3): 13-22.
[4]	李鹏, 杨莘博, 魏澄宙, 张艺涵, 田春筝, 王世谦, 薛帆, 谭忠富. 计及能源政策的园区综合能源系统鲁棒随机优化模型[J]. 中国电力, 2022, 55(11): 109-120.
[5]	彭穗, 龚贤夫, 刘新苗, 卢洵, 吴云芸, 薛熙臻, 周博, 艾小猛. 计及直流调节能力的含风电电力系统储能优化配置[J]. 中国电力, 2022, 55(1): 37-45.
[6]	任大伟, 金晨, 肖晋宇, 侯金鸣, 杜尔顺, 周原冰. 计及灵活性基于时序的“十四五”储能需求分析[J]. 中国电力, 2021, 54(8): 190-198.
[7]	薛晨, 任景, 马晓伟, 崔伟, 刘友波, 王潇笛. 面向高比例新能源消纳的西北调峰辅助服务市场机制及实践[J]. 中国电力, 2021, 54(11): 19-28.
[8]	王冠, 李鹏, 焦扬, 何楠, 张玮, 谭忠富. 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.

模态框（Modal）标题

基于多时间尺度随机优化的年度调峰辅助服务需求评估模型

An Annual Peak Regulation Auxiliary Service Demand Assessment Model Based on Multi-time Scale Stochastic Optimization

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics