Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage

LI Qi; ZOU Xueli; PU Yuchen; CHEN Weirong; ZHAO Shudan

doi:10.3969/j.issn.0258-2724.20210348

Volume 58 Issue 1

Jan. 2023

Turn off MathJax

Article Contents

Journal of Southwest Jiaotong University > 2023 > 58(1): 9-21.

LI Qi, ZOU Xueli, PU Yuchen, CHEN Weirong, ZHAO Shudan. Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 9-21. doi: 10.3969/j.issn.0258-2724.20210348

Citation:

LI Qi, ZOU Xueli, PU Yuchen, CHEN Weirong, ZHAO Shudan. Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 9-21. doi: 10.3969/j.issn.0258-2724.20210348

Citation:

PDF( 2157 KB)

Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage

doi: 10.3969/j.issn.0258-2724.20210348

1.
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.
Linfen Power Supply Company, State Grid Shanxi Electric Power Company, Linfen 041000, China

Received Date: 29 Apr 2021
Rev Recd Date: 05 Sep 2021

Available Online: 29 Nov 2022

Publish Date: 28 Oct 2021

Abstract

Abstract

According to the cogeneration characteristics of proton exchange membrane fuel cell and electrolyzer, in order to avoid the waste of heat energy in the hydrogen energy system and further improve the system efficiency, a combined heat–power microgrid system for photovoltaic, wind turbines, fuel cells, batteries, electric boilers, and gas boilers is built by incorporating hydrogen energy system, and a two-stage optimal dispathing method is proposed, including day-ahead scheduling and real-time optimization. The proposed system takes into account the waste heat recovery during the electricity-to-hydrogen conversion, and uses the hydrogen energy system as a thermal-electricity-hydrogen coupling equipment to realize the coordinated utilization and mutual conversion of electricity, heat, and hydrogen energy, and effectively improves the energy utilization rate. In the first stage of scheduling, according to the forecast of the wind-solar power system output and load demand in the day before, the mixed integer linear programming method is used to achieve the day-ahead optimal global schedule with the goal of minimizing the total operation cost of the microgrid. In the second stage of scheduling, based on the results of ultra-short-term predictions, the mixed integer quadratic programming algorithm is embedded in the model predictive control to lessen the economic influence from the prediction errors. Finally, through calculation examples of typical days in winter, summer and transitional seasons, compared with the day-ahead global optimal scheduling, the total cost of the two-stage scheduling method is reduced by 3.24%, 0.76% and 1.66%, respectively, in three types of seasonal days. Through the proposed method are simulated and verified in different scenarios, compared with the basic scenario without energy coupling, in the cases of involving the thermoelectric hydrogen coupling system and thermoelectric coupling system, the total cost and cost of pollution gas treatment with optimal scheduling are reduced by 15.58% and 24.93% respectively. The results show that the proposed method has a real-time and universal quality, which can meet the thermal and electrical load demand in the microgrid, realize stable and independent operation, and improve the system economy and environmental protection.
- hydrogen energy,
- combined heat-power microgrid,
- optimal scheduling

FullText(HTML)

References(27)

References

[1]	ZHU D F, YANG B, LIU Q, et al. Energy trading in microgrids for synergies among electricity, hydrogen and heat networks[J]. Applied Energy, 2020, 272: 115225. doi: 10.1016/j.apenergy.2020.115225
[2]	李奇,蒲雨辰,韩莹,等. 电-氢孤岛直流微电网的分层能量管理[J]. 西南交通大学学报,2020,55(5): 912-919. LI Qi, PU Yuchen, HAN Ying, et al. Hierarchical energy management of electricity-hydrogen island DC microgrid[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 912-919.
[3]	李奇,赵淑丹,蒲雨辰,等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报,2021,36(3): 486-495. LIQi, ZHAO Shudan, PU Yuchen, et al. Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 486-495.
[4]	NADERIPOUR A, ABDUL-MALEK Z, NOWDEH S A, et al. Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condition[J]. Energy, 2020, 196: 117124.1-117124.13.
[5]	王丹,黄德裕,胡庆娥,等. 基于电-热联合市场出清的综合需求响应建模及策略[J]. 电力系统自动化,2020,44(12): 13-21. doi: 10.7500/AEPS20191220007 WANG Dan, HUANG Deyu, HU Qing ’e, et al. Modeling and strategy of integrated demand response based on joint electricity-heat clearing market[J]. Automation of Electric Power Systems, 2020, 44(12): 13-21. doi: 10.7500/AEPS20191220007
[6]	杨冬锋,姜超,蔡国伟,等. 考虑电热耦合的交直流微电网多目标优化配置[J]. 电力系统自动化,2020,44(8): 124-132. YANG Dongfeng, JIANG Chao, CAI Guowei, et al. Multi-objective optimization configuration of AC/DC microgrid considering electro-thermal coupling[J]. Automation of Electric Power Systems, 2020, 44(8): 124-132.
[7]	BORNAPOUR M, HOOSHMAND R A, KHODABAKHSHIAN A, et al. Optimal stochastic coordinated scheduling of proton exchange membrane fuel cell-combined heat and power, wind and photovoltaic units in micro grids considering hydrogen storage[J]. Applied Energy, 2017, 202: 308-322. doi: 10.1016/j.apenergy.2017.05.133
[8]	王丹,智云强,贾宏杰,等. 基于多能源站协调的区域电力-热力系统日前经济调度[J]. 电力系统自动化,2018,42(13): 59-67. doi: 10.7500/AEPS20171129003 WANG Dan, ZHI Yunqiang, JIA Hongjie, et al. Day-ahead economic dispatch strategy of regional electricity-heating integrated energy system based on multiple energy stations[J]. Automation of Electric Power Systems, 2018, 42(13): 59-67. doi: 10.7500/AEPS20171129003
[9]	靳小龙,穆云飞,贾宏杰,等. 集成智能楼宇的微网系统多时间尺度模型预测调度方法[J]. 电力系统自动化,2019,43(16): 25-33. doi: 10.7500/AEPS20180629016 JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Model predictive control based multiple-time-scheduling method for microgrid system with smart buildings integrated[J]. Automation of Electric Power Systems, 2019, 43(16): 25-33. doi: 10.7500/AEPS20180629016
[10]	JIN X L, WU J Z, MU Y F, et al. Hierarchical microgrid energy management in an office building[J]. Applied Energy, 2017, 208: 480-494. doi: 10.1016/j.apenergy.2017.10.002
[11]	AGHAEI J, ALIZADEH M I. Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)[J]. Energy, 2013, 55: 1044-1054. doi: 10.1016/j.energy.2013.04.048
[12]	GUO X G, BAO Z J, LAI H J, et al. Model predictive control considering scenario optimisation for microgrid dispatching with wind power and electric vehicle[J]. The Journal of Engineering, 2017, 2017(13): 2539-2543. doi: 10.1049/joe.2017.0785
[13]	YANG H M, PAN H, LUO F J, et al. Operational planning of electric vehicles for balancing wind power and load fluctuations in a microgrid[J]. IEEE Transactions on Sustainable Energy, 2017, 8(2): 592-604. doi: 10.1109/TSTE.2016.2613941
[14]	吴鸣,骆钊,季宇,等. 基于模型预测控制的冷热电联供型微网动态优化调度[J]. 中国电机工程学报,2017,37(24): 7174-7184. WU Ming, LUO Zhao, JI Yu, et al. Model predictive control based dynamic optimal scheduling for cooling, heating and power microgrid[J]. Proceedings of the CSEE, 2017, 37(24): 7174-7184.
[15]	GU W, WANG Z H, WU Z, et al. An online optimal dispatch schedule for CCHP microgrids based on model predictive control[J]. 2017 IEEE Power & Energy Society General Meeting, 2017, 8(5): 2332-2342.
[16]	陈维荣,于瑾,李奇,等. 电-氢多能互补型微电网的VSG平衡电流控制方法[J]. 西南交通大学学报,2019,54(6): 1323-1331. doi: 10.3969/j.issn.0258-2724.20180860 CHEN Weirong, YU Jin, LI Qi, et al. Balanced current control method for virtual synchronous generator in electro-hydrogen multi-energy complementary microgrid[J]. Journal of Southwest Jiaotong University, 2019, 54(6): 1323-1331. doi: 10.3969/j.issn.0258-2724.20180860
[17]	孔令国,蔡国伟,李龙飞,等. 风光氢综合能源系统在线能量调控策略与实验平台搭建[J]. 电工技术学报,2018,33(14): 3371-3384. doi: 10.19595/j.cnki.1000-6753.tces.170597 KONG Lingguo, CAI Guowei, LI Longfei, et al. Online energy control strategy and experimental platform of integrated energy system of wind, photovoltaic and hydrogen[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3371-3384. doi: 10.19595/j.cnki.1000-6753.tces.170597
[18]	蒲雨辰,李奇,陈维荣,等. 计及最小使用成本及储能状态平衡的电-氢混合储能孤岛直流微电网能量管理[J]. 电网技术,2019,43(3): 918-927. doi: 10.13335/j.1000-3673.pst.2018.1528 PU Yuchen, LI Qi, CHEN Weirong, et al. Energy management for islanded DC microgrid with hybrid electric-hydrogen energy storage system based on minimum utilization cost and energy storage state balance[J]. Power System Technology, 2019, 43(3): 918-927. doi: 10.13335/j.1000-3673.pst.2018.1528
[19]	LI J R, LIN J, SONG Y H, et al. Operation optimization of power to hydrogen and heat (P2HH) in ADN coordinated with the district heating network[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1672-1683. doi: 10.1109/TSTE.2018.2868827
[20]	PASHAEI-DIDANI H, NOJAVAN S, NOUROLLAHI R, et al. Optimal economic-emission performance of fuel cell/CHP/storage based microgrid[J]. International Journal of Hydrogen Energy, 2019, 44(13): 6896-6908. doi: 10.1016/j.ijhydene.2019.01.201
[21]	随权,马啸,魏繁荣,等. 计及燃料电池热-电综合利用的能源网日前调度优化策略[J]. 中国电机工程学报,2019,39(6): 1603-1613, 1857. SUI Quan, MA Xiao, WEI Fanrong, et al. Day-ahead dispatching optimization strategy for energy network considering fuel cell thermal-electric comprehensive utilization[J]. Proceedings of the CSEE, 2019, 39(6): 1603-1613, 1857.
[22]	HU Q, LIN J, ZENG Q, et al. Optimal control of a hydrogen microgrid based on an experiment validated P2HH model[J]. IET Renewable Power Generation, 2020, 14(3): 364-371. doi: 10.1049/iet-rpg.2019.0544
[23]	丁明,王波,赵波,等. 独立风光柴储微网系统容量优化配置[J]. 电网技术,2013,37(3): 575-581. doi: 10.13335/j.1000-3673.pst.2013.03.002 DING Ming, WANG Bo, ZHAO Bo, et al. Configuration optimization of capacity of standalone PV-wind-diesel-battery hybrid microgrid[J]. Power System Technology, 2013, 37(3): 575-581. doi: 10.13335/j.1000-3673.pst.2013.03.002
[24]	熊焰,吴杰康,王强,等. 风光气储互补发电的冷热电联供优化协调模型及求解方法[J]. 中国电机工程学报,2015,35(14): 3616-3625. XIONG Yan, WU Jiekang, WANG Qiang, et al. An optimization coordination model and solution for combined cooling, heating and electric power systems with complimentary generation of wind, PV, gas and energy storage[J]. Proceedings of the CSEE, 2015, 35(14): 3616-3625.
[25]	LI Q, CHEN W R, LIU Z X, et al. Development of energy management system based on a power sharing strategy for a fuel cell-battery-supercapacitor hybrid tramway[J]. Journal of Power Sources, 2015, 279: 267-280. doi: 10.1016/j.jpowsour.2014.12.042
[26]	ERGEN T, KOZAT S S. Online training of LSTM networks in distributed systems for variable length data sequences[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 5159-5165. doi: 10.1109/TNNLS.2017.2770179
[27]	MA H W, ZHANG D Q. A grey forecasting model for coal production and consumption[C]//2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). Nanjing: IEEE, 2009: 512-516.

Relative Articles

Supplements(1)

Supplements
- 2023年第1期李奇附加材料
  .docx
  
  Download

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(7) / Tables(5)

Get Citation

PDF

XML

Article views(701) PDF downloads(110)

Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage

doi: 10.3969/j.issn.0258-2724.20210348

Abstract

References

Supplements

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Related

Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage

doi: 10.3969/j.issn.0258-2724.20210348

Abstract

References

Supplements

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Related

Export File

Citation

Format

Content