Multi-Time Scale Probabilistic Production Simulation of Wind-Solar Hydrogen Integrated Energy System Considering Hydrogen Storage

doi:10.16183/j.cnki.jsjtu.2022.379

Abstract

Abstract:

The uncertainty of wind power output and the difficulty of power storage restrict the development of new energy. As a high-quality secondary energy, hydrogen energy is green and pollution-free and has a high energy density. In order to cope with the volatility and randomness of new energy output, this paper proposes a multi-time scale probabilistic production simulation method for wind-solar hydrogen integrated energy system considering hydrogen storage. First, thermal energy recovery is considered in the hydrogen storage system model, and the wind-solar hydrogen integrated energy system model including electrothermal hydrogen multiple energy storage is constructed. Then, multi-time scale probabilistic production simulation is conducted for the wind-solar hydrogen integrated energy system, and the system maintenance arrangement and hydrogen storage seasonal distribution scheme are obtained through medium and long-term production simulation. The simulation results are taken as the boundary for short-term production simulation to achieve the cooperation of electrothermal hydrogen multiple energy storage and to smooth the random fluctuation of wind and solar power. Finally, an IEEE-RTS79 node example is given to verify that the proposed method can improve the reliability, flexibility and low-carbon feature of system operation.

Key words: new energy, hydrogen storage, electrothermal hydrogen multiple energy storage, wind-solar hydrogen integrated energy system, multi-time scale production simulation

CLC Number:

TM732

FAN Hong, XING Mengqing, WANG Lankun, TIAN Shuxin. Multi-Time Scale Probabilistic Production Simulation of Wind-Solar Hydrogen Integrated Energy System Considering Hydrogen Storage[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 881-892.

Figures/Tables 9

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References 34

[1]	李政, 陈思源, 董文娟, 等. 碳约束条件下电力行业低碳转型路径研究[J]. 中国电机工程学报, 2021, 41(12): 3987-4001.
	LI Zheng, CHEN Siyuan, DONG Wenjuan, et al. Low carbon transition pathway of power sector under carbon emission constraints[J]. Proceedings of the CSEE, 2021, 41(12): 3987-4001.
[2]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.
[3]	高林, 郑雅文, 杨东泰, 等. 构建碳中和电力系统:碳中和公式[J]. 科学通报, 2021, 66(31): 3932-3936.
	GAO Lin, ZHENG Yawen, YANG Dongtai, et al. Criterial equation of carbon neutrality for power systems[J]. Chinese Science Bulletin, 2021, 66(31): 3932-3936.
[4]	国网能源研究院有限公司. 中国能源电力发展展望-2020[M]. 北京: 中国电力出版社, 2020.
	State Grid Corporation of China. China energy & electricity outlook[M]. Beijing: China Electric Power Press, 2020.
[5]	刘永前, 王函, 韩爽, 等. 考虑风光出力波动性的实时互补性评价方法[J]. 电网技术, 2020, 44(9): 3211-3220.
	LIU Yongqian, WANG Han, HAN Shuang, et al. Real-time complementarity evaluation method for real-time complementarity of wind and solar power considering their volatility[J]. Power System Technology, 2020, 44(9): 3211-3220.
[6]	ZHANG H X, CAO Y J, ZHANG Y, et al. Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data[J]. Applied Energy, 2018, 216: 172-182.
[7]	李海, 张宁, 康重庆, 等. 可再生能源消纳影响因素的贡献度分析方法[J]. 中国电机工程学报, 2019, 39(4): 1009-1018.
	LI Hai, ZHANG Ning, KANG Chongqing, et al. Analytics of contribution degree for renewable energy accommodation factors[J]. Proceedings of the CSEE, 2019, 39(4): 1009-1018.
[8]	WU X, LI H Y, WANG X L, et al. Cooperative operation for wind turbines and hydrogen fueling stations with on-site hydrogen production[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2775-2789.
[9]	张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46(8): 189-207.
	ZHANG Shenxi, WANG Danyang, CHENG Hao-zhong, et al. Key technologies and challenges of low-carbon integrated energy system planning for carbon emission peak and carbon neutrality[J]. Automation of Electric Power Systems, 2022, 46(8): 189-207.
[10]	刘俊峰, 罗燕, 侯媛媛, 等. 考虑广义储能的微电网主动能量管理优化算法研究[J]. 电网技术, 2023, 47(1): 245-255.
	LIU Junfeng, LUO Yan, HOU Yuanyuan, et al. Research on optimization algorithm of active microgrid energy management considering generalized energy storage[J]. Power System Technology, 2023, 47(1): 245-255.
[11]	陈艳波, 武超, 焦洋, 等. 考虑需求响应与储能寿命模型的火储协调优化运行策略[J]. 电力自动化设备, 2022, 42(2): 16-24.
	CHEN Yanbo, WU Chao, JIAO Yang, et al. Coordinated optimal operation strategy of thermal power-energy storage considering demand response and life model of energy storage[J]. Electric Power Automation Equipment, 2022, 42(2): 16-24.
[12]	孙鹤旭, 李争, 陈爱兵, 等. 风电制氢技术现状及发展趋势[J]. 电工技术学报, 2019, 34(19): 4071-4083.
	SUN Hexu, LI Zheng, CHEN Aibing, et al. Current status and development trend of hydrogen production technology by wind power[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4071-4083.
[13]	李梓丘, 乔颖, 鲁宗相. 海上风电-氢能系统运行模式分析及配置优化[J]. 电力系统自动化, 2022, 46(8): 104-112.
	LI Ziqiu, QIAO Ying, LU Zongxiang. Operation mode analysis and configuration optimization of offshore wind-hydrogen system[J]. Automation of Electric Power Systems, 2022, 46(8): 104-112.
[14]	滕云, 王泽镝, 金红洋, 等. 用于电网调节能力提升的电热氢多源协调储能系统模型[J]. 中国电机工程学报, 2019, 39(24): 7209-7217.
	TENG Yun, WANG Zedi, JIN Hongyang, et al. A model and coordinated optimization for the multi-energy storage system of electricity heat hydrogen to regulation enhancement of power grid[J]. Proceedings of the CSEE, 2019, 39(24): 7209-7217.
[15]	初壮, 赵蕾, 孙健浩, 等. 考虑热能动态平衡的含氢储能的综合能源系统热电优化[J]. 电力系统保护与控制, 2023, 51(3): 1-12.
	CHU Zhuang, ZHAO Lei, SUN Jianhao, et al. Thermoelectric optimization of an integrated energy system with hydrogen energy storage considering thermal energy dynamic balance[J]. Power System Protection & Control, 2023, 51(3): 1-12.
[16]	高源, 刘学智. 基于氢储能的可再生能源系统协同规划方法[J]. 电力需求侧管理, 2023, 25(1): 59-66.
	GAO Yuan, LIU Xuezhi. Collaborative planning method of renewable energy system based on hydrogen storage[J]. Power Demand Side Management, 2023, 25(1): 59-66.
[17]	李欣, 黄婧琪, 刘立, 等. 考虑氢储供能时序的综合能源系统三阶段调度[J]. 电力系统及其自动化学报, 2023, 35(8): 71-81.
	LI Xin, HUANG Jingqi, LIU Li, et al. Three-stage scheduling of integrated energy system considering hydrogen storage and energy supply timing[J]. Journal of Power Systems & Automation, 2023, 35(8): 71-81.
[18]	林俐, 郑馨姚, 周龙文. 基于燃氢燃气轮机的风光火储多能互补优化调度[J]. 电网技术, 2022, 46(8): 3007-3022.
	LIN Li, ZHENG Xinyao, ZHOU Longwen. Wind-PV-thermal-storage multi-energy complementary optimal dispatching based on hydrogen gas turbine[J]. Power System Technology, 2022, 46(8): 3007-3022.
[19]	鲁明芳, 李咸善, 李飞, 等. 季节性氢储能-混氢燃气轮机系统两阶段随机规划[J]. 中国电机工程学报, 2023, 43(18): 6978-6991.
	LU Mingfang, LI Xianshan, LI Fei, et al. Two-stage stochastic programming of seasonal hydrogen energy storage and mixed hydrogen-fueled gas turbine system[J]. Proceedings of the CSEE, 2023, 43(18): 6978-6991.
[20]	邓浩, 陈洁, 焦东东, 等. 风氢耦合并网系统能量管理控制策略[J]. 高电压技术, 2020, 46(1): 99-106.
	DENG Hao, CHEN Jie, JIAO Dongdong, et al. Control strategy for energy management of hybrid grid-connected system of wind and hydrogen[J]. High Voltage Engineering, 2020, 46(1): 99-106.
[21]	邓浩, 陈洁, 腾扬新, 等. 风氢耦合系统能量管理策略研究[J]. 太阳能学报, 2021, 42(1): 256-263.
	DENG Hao, CHEN Jie, TENG Yangxin, et al. Energy management strategy of wind power coupled with hydrogen system[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 256-263.
[22]	邵成成, 冯陈佳, 傅旭, 等. 多能源电力系统生产模拟:现状与挑战[J]. 中国电机工程学报, 2021, 41 (6): 2029-2040.
	SHAO Chengcheng, FENG Chenjia, FU Xu, et al. Multi energy power system production simulation: State of arts and challenges[J]. Proceedings of the CSEE, 2021, 41(6): 2029-2040.
[23]	KIRYANOVA N G, MATRENIN P V, MITROFANOV S V, et al. Hydrogen energy storage systems to improve wind power plant efficiency considering electricity tariff dynamics[J]. International Journal of Hydrogen Energy, 2022, 47(18): 10156-10165.
[24]	邵成成, 王雅楠, 冯陈佳, 等. 考虑多能源电力特性的电力系统中长期生产模拟[J]. 中国电机工程学报, 2020, 40(13): 4072-4081.
	SHAO Chengcheng, WANG Yanan, FENG Chenjia, et al. Mid/long-term power system production simulation considering multi-energy generation[J]. Proceedings of the CSEE, 2020, 40(13): 4072-4081.
[25]	邵成成, 冯陈佳, 王秀丽, 等. 基于负荷状态转移曲线的中长期快速机组组合[J]. 中国电机工程学报, 2019, 39(Sup.1): 141-147.
	SHAO Chengcheng, FENG Chenjia, WANG Xiuli, et al. Mid-long term fast unit commitment based on load state transfer curve[J]. Proceedings of the CSEE, 2019, 39(Sup.1): 141-147.
[26]	李湃, 王伟胜, 黄越辉, 等. 大规模新能源基地经特高压直流送出系统中长期运行方式优化方法[J]. 电网技术, 2023, 47(1): 31-44.
	LI Pai, WANG Weisheng, HUANG Yuehui, et al. Method on optimization of medium and long term operation modes of large-scale renewable energy power base through UHVDC system[J]. Power System Technology, 2023, 47(1): 31-44.
[27]	朱睿, 胡博, 谢开贵, 等. 含风电-光伏-光热-水电-火电-储能的多能源电力系统时序随机生产模拟[J]. 电网技术, 2020, 44(9): 3246-3253.
	ZHU Rui, HU Bo, XIE Kaigui, et al. Sequential probabilistic production simulation of multi-energy power system with wind power, photovoltaics, concentrated solar power, cascading hydro power, thermal power and battery energy storage[J]. Power System Technology, 2020, 44(9): 3246-3253.
[28]	JING S B, YU Z Y, CHEN W W, et al. Research on power system operation simulation model considering energy storage and new energy generation[C]//2020 10th International Conference on Power and Energy Systems. Chengdu, China: IEEE, 2020: 129-133.
[29]	KAFFASH M, DECONINCK G. Comparison of statistical-based and data-driven-based scenario generation of PV power for stochastic day-ahead battery scheduling[C]//2020 IEEE PES Innovative Smart Grid Technologies Europe. The Hague, Netherlands: IEEE, 2020: 730-734.
[30]	李湃, 范越, 黄越辉, 等. 基于电源聚合-分解模型的新能源电力系统月度发电计划优化方法[J]. 电网技术, 2020, 44(9): 3281-3293.
	LI Pai, FAN Yue, HUANG Yuehui, et al. Monthly generation scheduling method of renewable energy power system based on power plant aggregation & decomposition models[J]. Power System Technology, 2020, 44(9): 3281-3293.
[31]	宋震, 张龙, 徐广强, 等. 耦合氢储能的火电机组在新型电力系统中的应用研究[J]. 热力发电, 2022, 51(11): 140-147.
	SONG Zhen, ZHANG Long, XU Guangqiang, et al. Application study of thermal power unit coupled with hydrogen energy storage in new type electric power system[J]. Thermal Power Generation, 2022, 51(11): 140-147.
[32]	何彬彬, 杨波, 潘军, 等. 碳中和视角下的氢储能电热气耦合系统优化配置[J]. 粘接, 2022, 49(11): 118-121.
	HE Binbin, YANG Bo, PAN Jun, et al. Optimal configuration of hydrogen energy storage electric thermal gas coupling system from the perspective of carbon neutralization[J]. Adhesion, 2022, 49(11): 118-121.
[33]	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.
[34]	赵伟, 熊正勇, 潘艳, 等. 计及碳排放流的电力系统低碳规划[J]. 电力系统自动化, 2023, 47(9):23-33.
	ZHAO Wei, XIONG Zhengyong, PAN Yan, et al. Low-carbon planning of electric power system considering carbon emission flow[J]. Automation of Electric Power System, 2023, 47(9): 23-33.

场景	总成本/万元	碳排放成本/ 万元	弃风弃光惩罚/ 万元	风光能源渗透/%	向上灵活性不足率/%	向下灵活性不足率/%	失负荷概率/%	电力不足期望值/(GW·h)
1	83 422	20 417	1 748	41.231	0.0116	10.562	1.119	0.484
2	84 013	20 563	1 756	36.513	0.0142	12.137	1.125	0.612
3	83 872	20 588	1 763	34.484	0.0151	13.096	1.145	0.798

场景	风光消纳电量/(GW·h)	弃风弃光率/%
1	1743.608	6.53
2	1665.342	9.75
3	1653.261	10.80