含虚拟储能的新能源高渗透电网深度调峰备用决策模型

doi:10.11930/j.issn.1004-9649.201907134

中国电力 ›› 2019, Vol. 52 ›› Issue (11): 35-43.DOI: 10.11930/j.issn.1004-9649.201907134

• 分布式能源系统与综合需求响应互动机制关键技术 • 上一篇下一篇

含虚拟储能的新能源高渗透电网深度调峰备用决策模型

薛晨¹, 任景¹, 张小东¹, 崔伟¹, 刘友波²

1. 国家电网公司西北分部, 陕西西安 710048;
2. 四川大学电气信息学院, 四川成都 610065

收稿日期:2019-07-16 修回日期:2019-09-08 出版日期:2019-11-05 发布日期:2019-11-05
通讯作者: 薛晨(1986-),女,通信作者,高级工程师,从事调度计划和新能源消纳管理工作,E-mail:18509287928@163.com
作者简介:刘友波(1983-),男,博士,副教授,从事智能电网规划与运行,电力系统数据挖掘与知识发现等研究,E-mail:liuyoubo@scu.edu.cn
基金资助:
国家自然科学基金资助项目（51437003）；国家电网公司管理咨询项目（19H0338）

A Reserve Decision Model for High-Proportional Renew Energy Integrated Power Grid Based on Deep Peak-Shaving and Virtual Storage

XUE Chen¹, REN Jing¹, ZHANG Xiaodong¹, CUI Wei¹, LIU Youbo²

1. Northwest Branch of State Grid Corporation of China, Xi'an 710048, China;
2. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China

Received:2019-07-16 Revised:2019-09-08 Online:2019-11-05 Published:2019-11-05
Supported by:
This work is supported by the National Natural Science Foundation of China (No.51437003) and the Management Consulting Project of State Grid Corporation of China (No.19H0338)

摘要/Abstract

摘要： 随着西北新能源并网容量的快速增长，新能源消纳需求与反调峰特性的矛盾成为电网运行面临的严峻挑战，但电力运行的逐步市场化也为新能源消纳提供了新途径。基于此提出新能源高渗电网中虚拟储能、深度调峰共同参与备用的市场决策模型。首先，考虑新能源的波动性，构建引入不确定度的新能源备用模型；针对用户侧资源的价格敏感性，构建基于“虚拟储能”的“充放电”能力的备用模型；其次，基于火电机组深度调峰技术确定不同调峰深度的补偿机制，构建火电深度调峰参与备用的模型；最后，以新能源消纳为核心，系统调峰备用成本最小为目标，构建虚拟储能、火电深度调峰共同参与的备用决策模型。算例结果验证了所提决策模型对保证系统调峰备用容量的有效性。

关键词: 新能源消纳, 深度调峰, 虚拟储能, 调峰备用, 交易决策

Abstract: With the capacity of renew energy continued to grow rapidly in the northwest China, the contradiction between the consumption demand of renew energy and the characteristics of its inverse peak shaving has become the severe challenges. On the other hand, the gradual marketization of power operation also provides a new way for large-scale consumption of renew energy. Based on this, a market decision model of adjustment standby is proposed based on deep peak-shaving and virtual storage in new energy high-permeability grid. First, this paper proposes a peaking reserve model of new energy with uncertainty participation which consider new energy output volatility. Based on the price sensitivity of user-side resources, an alternate model based on the "charge and discharge" capability of "virtual energy storage" is constructed. Secondly, based on deep peak-shaving technology of thermal power units, the compensation mechanism of different peak regulation depth is determined and the standby model for deep peak shaving of thermal power unit is used. Lastly, taking the new energy consumption as the core, the system peaking and standby cost is the minimum, and the standby decision model of virtual energy storage and thermal power deep participation is built. The simulation of the example showed that the proposed decision model is effective to ensure the capacity of adjustment standby.

Key words: new energy accommodation, deep peak-shaving, virtual storage, reserve of peak-shaving, transactions decisions

中图分类号:

TM73

薛晨, 任景, 张小东, 崔伟, 刘友波. 含虚拟储能的新能源高渗透电网深度调峰备用决策模型[J]. 中国电力, 2019, 52(11): 35-43.

XUE Chen, REN Jing, ZHANG Xiaodong, CUI Wei, LIU Youbo. A Reserve Decision Model for High-Proportional Renew Energy Integrated Power Grid Based on Deep Peak-Shaving and Virtual Storage[J]. Electric Power, 2019, 52(11): 35-43.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201907134

https://www.electricpower.com.cn/CN/Y2019/V52/I11/35

参考文献

[1] 吴迪, 王佳明, 李晖, 等. 以促进可再生能源消纳为目标的我国西北-西南联网容量与送电时序研究[J]. 电网技术, 2018, 42(7):2103-2110 WU Di, WANG Jiaming, LI Hui, et al. Research on capacity and delivery sequence of interconnected northwestern-southwestern power grid towards renewable energy accommodation[J]. Power System Technology, 2018, 42(7):2103-2110
[2] 徐少华, 李建林, 宋新甫, 等. 大规模储能系统提升西北地区可再生能源消纳能力分析[J]. 电力建设, 2018, 39(4):67-74 XU Shaohua, LI Jianlin, SONG Xinfu, et al. Large-scale energy storage system help to improve northwest area's renewable energy absorption ability[J]. Electric Power Construction, 2018, 39(4):67-74
[3] SEBASTIAN R. Application of a battery energy storage for frequency regulation and peak shaving in a wind diesel power system[J]. IET Generation, Transmission & Distribution, 2016, 10(3):764-770.
[4] 王鹏, 马晓伟, 任冲, 等. 基于甘肃新能源全网消纳的西北电网跨省辅助服务机制探讨[J]. 供用电, 2016, 33(9):79-84 WANG Peng, MA Xiaowei, REN Chong, et al. The study on the inter-provincial ancillary service mechanism to absorb the new energy of Gansu within the whole northwest power grid[J]. Distribution & Utilization, 2016, 33(9):79-84
[5] LAI Xiaowen, XIE Le, XIA Qing, et al. Decentralized multi-area economic dispatch via dynamic multiplier based lagrangian relaxation[J]. IEEE Transactions on Power Systems, 2015, 30(6):3225-3233.
[6] 姚高瑞, 雷雪娇, 刘西林, 等. 促进风电消纳的供需互动合约机制与多市场购电策略[J]. 电力系统自动化, 2019, 43(7):113-119 YAO Gaorui, LEI Xuejiao, LIU Xilin, et al. Supply-demand interaction contract mechanism and multi-market electricity purchasing strategy for improving wind power accommodation[J]. Automation of Electric Power Systems, 2019, 43(7):113-119
[7] 菅学辉, 张利, 杨立滨, 等. 高比例风电并网下基于卡尔改进的深度调峰机制[J]. 电力系统自动化, 2018, 42(8):110-118 JIAN Xuehui, ZHANG Li, YANG Libin, et al. Deep-peak regulation mechanism based on kaldor improvement under high-penetration wind power[J]. Automation of Electric Power Systems, 2018, 42(8):110-118
[8] TANG Wenyuan, JAIN R. Market mechanisms for buying rand wind[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4):1615-1623.
[9] 梁子鹏, 陈皓勇, 雷佳, 等. 考虑风电不确定度的风-火-水-气-核-抽水蓄能多源协同旋转备用优化[J]. 电网技术, 2018, 42(7):2121-2123 LIANG Zipeng, CHEN Haoyong, LEI Jia, et al. A multi-source coordinated spinning reserve model considering wind power uncertainty[J]. Power System Technology, 2018, 42(7):2121-2123
[10] 黄瀚燕, 周明, 李庚银. 考虑多重不确定性和备用互济的含风电互联电力系统分散协调调度方法[J]. 电网技术, 2019, 43(2):381-389 HUANG Hanyan, ZHOU Ming, LI Gengyin. Coordinated decentralized dispatch of wind-power-integrated multi-area interconnected power systems considering multiple uncertainties and mutual reserve support[J]. Power System Technology, 2019, 43(2):381-389
[11] FERNANDEZ B R, DVORKIN Y, ORTEGA V M A. Probabilistic security-constrained unit commitment with generation and transmission contingencies[J]. IEEE Transactions on Power Systems, 2017, 32(1):228-239.
[12] 林峰, 汪震, 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):71-75 LIN Feng, WANG Zhen, GUAN Zhong, et al. Robust reserve scheduling model of electric power system considering WTG de-loading capability[J]. Automation of Electric Power Systems, 2018, 42(19):71-75
[13] 刘兴宇, 温步瀛, 江岳文. 考虑用户侧响应的含风电电力系统旋转备用效益研究[J]. 电力科学与技术学报, 2018, 33(2):27-34 LIU Xingyu, WEN Buying, JIANG Yuewen. Study on the spinning reserve benefit considering demand response in power system containing wind power[J]. Journal of Electric Power Science and Technology, 2018, 33(2):27-34
[14] 吴迪, 程海花, 赵晋泉, 等. 基于平衡成本的风电分段及火电调峰补偿方法[J]. 电力系统自动化, 2019, 43(3):116-123 WU Di, CHENG Haihua, ZHAO Jinquan, et al. Balancing cost based wind power segmentation and compensation method of peak regulation for thermal power[J]. Automation of Electric Power Systems, 2019, 43(3):116-123
[15] 张振宇, 孙骁强, 万筱钟, 等. 基于统计学特征的新能源纳入西北电网备用研究[J]. 电网技术, 2018, 42(7):2047-2054 ZHANG Zhenyu, SUN Xiaoqiang, WAN Xiaozhong, et al. Research on reserve of northwest power grid considering renewable energy based on statistical characteristics[J]. Power System Technology, 2018, 42(7):2047-2054
[16] 林俐, 田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术, 2017, 41(7):2255-2263 LIN Li, TIAN Xinyu. Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7):2255-2263
[17] SUN Jianwei, TANG Shengwei, WANG Dan, et al. An communication and battery performance evaluation of the demand response and battery storage coordination system potential for providing microgrid tie-line smoothing services[J]. Advanced Materials Research, 2013, 860/861/862/863:2023-2034.
[18] VAN R J, LEEMPUT N, GETH F, et al. Electric vehicle charging in an office building microgrid with distributed energy resources[J]. IEEE Transactions on Sustainable Energy, 2014, 5(4):1389-1396.

含虚拟储能的新能源高渗透电网深度调峰备用决策模型

A Reserve Decision Model for High-Proportional Renew Energy Integrated Power Grid Based on Deep Peak-Shaving and Virtual Storage

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 15

编辑推荐

Metrics

[1]	苏娟, 李拓, 刘峻玮, 夏越, 杜松怀. 综合能源系统下虚拟储能建模方法与应用场景研究综述及展望[J]. 中国电力, 2024, 57(6): 53-68.
[2]	李咸善, 丁胜彪, 李飞, 李欣. 考虑水电调节费用补偿的风光水联盟优化调度策略[J]. 中国电力, 2024, 57(5): 26-38.
[3]	武群丽, 朱新宇. 可再生能源配额制下风光储联合参与现货市场的交易决策[J]. 中国电力, 2024, 57(4): 77-88.
[4]	刘硕, 张梦晗, 于松泰, 向明旭, 杨知方. 计及跨区备用辅助服务互济的互联电网出清方法[J]. 中国电力, 2023, 56(9): 35-47.
[5]	许凌, 张希鹏, 曹益奇, 张丙金, 董成, 谭振飞. 考虑备用互济的省间现货电能与备用耦合出清模型[J]. 中国电力, 2023, 56(9): 48-56.
[6]	任景, 高敏, 程松, 张小东, 刘友波. 面向新能源不确定性的西北电力电量平衡机制[J]. 中国电力, 2023, 56(9): 66-78.
[7]	齐步洋, 卓振宇, 杜尔顺, 张宁, 康重庆. 考虑储能装置寿命的电网侧规模化电化学储能规划与评估方法[J]. 中国电力, 2023, 56(8): 1-9,47.
[8]	彭生江, 杨德州, 孙传帅, 袁铁江, 刘永成. 基于氢负荷需求的氢能系统容量规划[J]. 中国电力, 2023, 56(7): 13-20,32.
[9]	魏震波, 李银江, 张雯雯, 杨超. 基于改进Myerson值法的云储能双层优化运营模型[J]. 中国电力, 2023, 56(7): 198-206.
[10]	任鑫, 王渡, 金亚飞, 王志刚. 基于能耗、经济性及碳排放的热电联产机组运行优化[J]. 中国电力, 2023, 56(4): 201-210.
[11]	刘联涛, 刘飞, 吉平, 林伟芳, 张祥成, 田旭, 高菲. 储能参与新能源消纳的优化控制策略[J]. 中国电力, 2023, 56(3): 137-143.
[12]	李雄威, 王昕, 顾佳伟, 徐家豪. 考虑火电深度调峰的风光火储系统日前优化调度[J]. 中国电力, 2023, 56(1): 1-7,48.
[13]	张明理, 张娜, 武志锴, 高靖, 徐熙林, 李健, 吕泉. 日前电能市场与深度调峰市场联合出清模型[J]. 中国电力, 2022, 55(2): 138-144.
[14]	古宸嘉, 王建学, 李清涛, 张耀. 新能源集中并网下大规模集中式储能规划研究述评[J]. 中国电力, 2022, 55(1): 2-12,83.
[15]	胡娱欧, 高志远, 张晶, 张涛. 市场化环境下优先发电实施方案[J]. 中国电力, 2021, 54(9): 102-108.

模态框（Modal）标题

含虚拟储能的新能源高渗透电网深度调峰备用决策模型

A Reserve Decision Model for High-Proportional Renew Energy Integrated Power Grid Based on Deep Peak-Shaving and Virtual Storage

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 15

编辑推荐

Metrics