风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型

doi:10.16183/j.cnki.jsjtu.2022.054

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (5): 505-512.doi: 10.16183/j.cnki.jsjtu.2022.054

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 下一篇

风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型

顾慧杰¹, 彭超逸¹, 孙书豪¹, 刘明涛², 谢俊²(), 施雄华³, 鲍永³

1.中国南方电网电力调度控制中心,广州 510623
2.河海大学能源与电气学院,南京 211100
3.南京南瑞继保电气有限公司,南京 211102

收稿日期:2022-03-05 修回日期:2022-09-23 接受日期:2022-11-28 出版日期:2023-05-28 发布日期:2023-06-02
通讯作者: 谢俊 E-mail:eejxie@163.com.
作者简介:顾慧杰(1985-),高级工程师,主要研究方向为电力市场.
基金资助:
国家重点研发计划(2019YFE0105200);国家自然科学基金(U1965104)

A Short-Term Production Simulation Model of Wind-PV-Hydrogen-Pumped Storage Zero Carbon Power System

GU Huijie¹, PENG Chaoyi¹, SUN Shuhao¹, LIU Mingtao², XIE Jun²(), SHI Xionghua³, BAO Yong³

1. Dispatching and Control Center, China Southern Power Grid, Guangzhou 510623, China
2. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
3. NR Electric Co., Ltd., Nanjing 211102, China

Received:2022-03-05 Revised:2022-09-23 Accepted:2022-11-28 Online:2023-05-28 Published:2023-06-02
Contact: XIE Jun E-mail:eejxie@163.com.

摘要/Abstract

摘要：

为实现碳达峰碳中和目标,构建以新能源为主体、以能源供给清洁化和能源消费电气化为特征的新型电力系统迫在眉睫.考虑风力发电、光伏发电的间歇性和随机性,以及抽蓄电站、电制氢的储能特性和灵活性特点,基于随机规划理论提出一种风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型.在满足柔性氢负荷总量需求的基础上,以绿电上网电量最大为目标,对风电-光伏-电制氢-抽蓄零碳电力系统进行短期生产模拟,包括日前发电-制氢计划、备用容量、抽水蓄能-放水发电功率、弃风光等.以我国张北风电-光伏-电制氢-抽蓄零碳电力系统示范工程为例,设置多个运行情景对所提模型进行模拟仿真.仿真结果表明:该模型能够有效模拟系统在任意风光出力场景集下的绿电上网计划情况,柔性氢负荷、抽蓄电站能有效促进风光消纳,增加系统综合效益.

关键词: 零碳电力系统, 风力发电, 光伏发电, 电制氢, 抽蓄电站, 短期生产模拟模型

Abstract:

To achieve the goal of carbon peaking and carbon neutrality, it is urgent to build a new power system with renewable energy as the main body, characterized by clean energy supply and electrification of energy consumption. Considering the intermittency and randomness of wind-solar power, as well as the energy storage and flexibility of pumped storage power stations and power-to-hydrogen, a short-term production simulation model of wind-PV-hydrogen-pumped storage zero carbon power system is established based on the stochastic programming theory. In the proposed short-term production simulation model, on the basis of meeting the total demand of flexible hydrogen load, the short-term production simulation is implemented, including electricity-hydrogen production schedule, reserve capacity, pumped storage-water discharge power output and wind-solar curtailment, with the goal of maximizing green on-grid electricity. Taking Zhangbei zero carbon power system of China as an example, many operation scenarios are established to simulate the proposed model. The simulation results show that the proposed model can effectively simulate the on-grid scheme situation of green power in which the system deals with the randomness of wind-solar output in any output scenarios of wind-solar power scene set. The flexible hydrogen load and the pumped storage power station can effectively promote wind-solar energy accommodation and increase the comprehensive benefit of the combined system.

Key words: zero carbon power system, wind power, photovoltaic (PV) power, power-to-hydrogen, pumped storage station, short-term production simulation model

中图分类号:

TM731
TM614

顾慧杰, 彭超逸, 孙书豪, 刘明涛, 谢俊, 施雄华, 鲍永. 风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型[J]. 上海交通大学学报, 2023, 57(5): 505-512.

GU Huijie, PENG Chaoyi, SUN Shuhao, LIU Mingtao, XIE Jun, SHI Xionghua, BAO Yong. A Short-Term Production Simulation Model of Wind-PV-Hydrogen-Pumped Storage Zero Carbon Power System[J]. Journal of Shanghai Jiao Tong University, 2023, 57(5): 505-512.

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参考文献 17

[1]	王锡凡. 电力系统规划基础[M]. 北京: 中国电力出版社, 1994.
	WANG Xifan. Power system planning fundamentals[M]. Beijing: China Electric Power Press, 1994.
[2]	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.
[3]	舒印彪, 张智刚, 郭剑波, 等. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报, 2017, 37(1): 1-9.
	SHU Yinbiao, ZHANG Zhigang, GUO Jianbo, et al. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1): 1-9.
[4]	刘明涛, 谢俊, 张秋艳, 等. 碳交易环境下含风电电力系统短期生产模拟[J]. 上海交通大学学报, 2021, 55(12): 1598-1607.
	LIU Mingtao, XIE Jun, ZHANG Qiuyan, et al. Short-term production simulation of power system containing wind power under carbon trading environment[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1598-1607.
[5]	LI C B, LIU X B, CAO Y J, et al. A time-scale adaptive dispatch method for renewable energy power supply systems on Islands[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 1069-1078. doi: 10.1109/TSG.2015.2485664 URL
[6]	黄强, 郭怿, 江建华, 等. “双碳”目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509.
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[7]	潘光胜, 顾伟, 张会岩, 等. 面向高比例可再生能源消纳的电氢能源系统[J]. 电力系统自动化, 2020, 44(23): 1-10.
	PAN Guangsheng, GU Wei, ZHANG Huiyan, et al. Electricity and hydrogen energy system towards accomodation of high proportion of renewable energy[J]. Automation of Electric Power Systems, 2020, 44(23): 1-10.
[8]	BAN M F, YU J L, SHAHIDEHPOUR M, et al. Integration of power-to-hydrogen in day-ahead security-constrained unit commitment with high wind penetration[J]. Journal of Modern Power Systems & Clean Energy, 2017, 5(3): 337-349.
[9]	吕翔, 刘国静, 周莹. 含抽水蓄能的风水火联合机组组合研究[J]. 电力系统保护与控制, 2017, 45(12): 35-43.
	LÜ Xiang, LIU Guojing, ZHOU Ying. Research on combined unit commitment of wind power-hydro power-thermal power for the power system with pumped hydro storage[J]. Power System Protection & Control, 2017, 45(12): 35-43.
[10]	邹金, 赖旭, 汪宁渤. 以减少电网弃风为目标的风电与抽水蓄能协调运行[J]. 电网技术, 2015, 39(9): 2472-2477.
	ZOU Jin, LAI Xu, WANG Ningbo. Mitigation of wind curtailment by coordinating with pumped storage[J]. Power System Technology, 2015, 39(9): 2472-2477.
[11]	夏沛, 邓长虹, 龙志君, 等. 含抽水蓄能机组的风电消纳鲁棒机组组合[J]. 电力系统自动化, 2018, 42(19): 41-49.
	XIA Pei, DENG Changhong, LONG Zhijun, et al. Robust unit commitment with pumped storage units for wind power accommodation[J]. Automation of Electric Power Systems, 2018, 42(19): 41-49.
[12]	李咸善, 杨宇翔. 基于双向电价补偿的含氢储能风电和梯级水电联合优化调度[J]. 电网技术, 2020, 44(9): 3297-3306.
	LI Xianshan, YANG Yuxiang. Optimization dispatching for joint operation of hydrogen storage-wind power and cascade hydropower station based on bidirectional electricity price compensation[J]. Power System Technology, 2020, 44(9): 3297-3306.
[13]	蔡国伟, 孔令国, 薛宇, 等. 风氢耦合发电技术研究综述[J]. 电力系统自动化, 2014, 38(21): 127-135.
	CAI Guowei, KONG Lingguo, XUE Yu, et al. Overview of research on wind power coupled with hydrogen production technology[J]. Automation of Electric Power Systems, 2014, 38(21): 127-135.
[14]	WANG J, BOTTERUD A, BESSA R, et al. Wind power forecasting uncertainty and unit commitment[J]. Applied Energy, 2011, 88(11): 4014-4023. doi: 10.1016/j.apenergy.2011.04.011 URL
[15]	BOUFFARD F, GALIANA F D. Stochastic security for operations planning with significant wind power generation[J]. IEEE Transactions on Power Systems, 2008, 23(2): 306-316. doi: 10.1109/TPWRS.2008.919318 URL
[16]	李湃, 王伟胜, 刘纯, 等. 张北柔性直流电网工程新能源与抽蓄电站配置方案运行经济性评估[J]. 中国电机工程学报, 2018, 38(24): 7206-7214.
	LI Pai, WANG Weisheng, LIU Chun, et al. Economic assessment of Zhangbei VSC-based DC grid planning scheme with integration of renewable energy and pumped-hydro storage power station[J]. Proceedings of the CSEE, 2018, 38(24): 7206-7214.
[17]	DING H J, HU Z C, SONG Y H. Stochastic optimization of the daily operation of wind farm and pumped-hydro-storage plant[J]. Renewable Energy, 2012, 48: 571-578. doi: 10.1016/j.renene.2012.06.008 URL

风电-光伏-电制氢-抽蓄零碳电力系统短期生产模拟模型

A Short-Term Production Simulation Model of Wind-PV-Hydrogen-Pumped Storage Zero Carbon Power System

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