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| Multi-Time-Scale Robust Optimization Strategy for Integrated Energy System Considering the Refinement of Hydrogen Energy Use |
| Hu Junjie1, Tong Yuxuan1, Liu Xuetao1, Wang Jianxiao2, Xu Yanhui1 |
1. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing 102206 China;
2. National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100871 China |
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Abstract The local flexibility of the high percentage of new energy power system is seriously insufficient, and it is urgent to establish a new energy structure system that is clean, efficient and flexible. Hydrogen energy as a secondary energy source with diverse and efficient conversion forms. Refined modeling of hydrogen energy utilization is a key issue to study the flexibility value of electric-hydrogen coupled units. At the same time, the energy system has uncertainty and fluctuating variability on multiple time scales, and the existing studies are too adventurous or conservative in considering the uncertainty at the day-ahead stage. In response to the above problems, a robust optimization strategy is proposed for the multi-timescale distribution of the integrated energy system taking into account the refined hydrogen energy utilization.
Firstly, the two-stage operation process of P2H is considered, and the refinement modeling of hydrogen energy use process and equipment is carried out by taking into account the operating characteristics of electrolyzer, hydrogen fuel cell and other equipment. The operating states of PEM are divided into shutdown, cold standby, overload, variable load and low load states, and a mixed integer linear mathematical model of the electrolyzer with variable load start-stop characteristics is established, taking into account the loss of hydrogen output during the cold start of PEM. In order to improve the operational flexibility of the cogeneration unit, the adjustable thermoelectric ratio of CHP and HFC is considered to decouple the thermoelectric linkage, and an adjustable thermoelectric ratio heat model of the cogeneration unit is established. Secondly, to reduce the power fluctuation caused by the deviation of wind power and multi-energy load forecasts in the day-ahead and intra-day, a two-stage optimization model of day-ahead scheduling and intra-day rolling is established. In the day-ahead stage, a data-driven distribution robust optimization model is established, and the probability distribution is constrained by the composite norm to adjust the conservativeness of the model; in the intra-day stage, the differences in the time scales of flexibility regulation of multi-energy flows are considered, and the impact of power fluctuations is reduced by rolling optimization on multiple time scales.
In the case simulation, five scenarios are set up for comparative analysis in the day-ahead phase, and the proposed multi-timescale model is compared with day-ahead programming (DA-P) in the intra-day phase, and the conservativeness of the data-driven DRO model is investigated, leading to the following conclusions: (1) The intermediate energy ladder losses are avoided after refining the two-stage operation process of P2H. The efficiency and flexibility of hydrogen energy utilization are fully exploited, and the comprehensive energy utilization of IES is significantly improved. (2) The proposed PEM mixed integer linear model and adjustable cogeneration model can adjust the equipment output in real time according to the load, which promotes the wind power consumption and improves the operating economy. (3) The data-driven DRO model proposed in the day-ahead stage fully takes into account the uncertainty of the energy system based on historical data samples, and its conservativeness is influenced by the number of reduction scenarios and sample size. It has a better ability to resist the fluctuation of uncertainty forecast error in the intra-day correction phase. (4) The intra-day phase takes into account the differences in the forecast characteristics of different energy sources, and smoothes out power fluctuations by regulating different energy coupling devices on a sub-time scale, effectively reducing wind power volatility and operating costs.
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Received: 21 December 2022
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[1] 葛苏南. 电能替代与综合能源服务加速融合[N]. 中国能源报, 2022-04-18.
[2] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516.
Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516.
[3] 陈瑜玮, 孙宏斌, 郭庆来. 综合能源系统分析的统一能路理论(五):电-热-气耦合系统优化调度[J]. 中国电机工程学报, 2020, 40(24): 7928-7937, 8230.
Chen Yuwei, Sun Hongbin, Guo Qinglai.Energy circuit theory of integrated energy system analysis (Ⅴ): integrated electricity-heat-gas dispatch[J]. Proceedings of the CSEE, 2020, 40(24): 7928-7937, 8230.
[4] 刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543.
Diao Hanbin, Li Peiqiang, Wang Jifei, et al.Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4532-4543.
[5] 陈锦鹏, 胡志坚, 陈颖光, 等. 考虑阶梯式碳交易机制与电制氢的综合能源系统热电优化[J]. 电力自动化设备, 2021, 41(9): 48-55.
Chen Jinpeng, Hu Zhijian, Chen Yingguang, et al.Thermoelectric optimization of integrated energy system considering ladder-type carbon trading mechanism and electric hydrogen production[J]. Electric Power Automation Equipment, 2021, 41(9): 48-55.
[6] 刘继春, 周春燕, 高红均, 等. 考虑氢能-天然气混合储能的电-气综合能源微网日前经济调度优化[J]. 电网技术, 2018, 42(1): 170-179.
Liu Jichun, Zhou Chunyan, Gao Hongjun, et al.A day-ahead economic dispatch optimization model of integrated electricity-natural gas system considering hydrogen-gas energy storage system in microgrid[J]. Power System Technology, 2018, 42(1): 170-179.
[7] 崔杨, 闫石, 仲悟之, 等. 含电转气的区域综合能源系统热电优化调度[J]. 电网技术, 2020, 44(11): 4254-4264.
Cui Yang, Yan Shi, Zhong Wuzhi, et al.Optimal thermoelectric dispatching of regional integrated energy system with power-to-gas[J]. Power System Technology, 2020, 44(11): 4254-4264.
[8] 邓杰, 姜飞, 王文烨, 等. 考虑电热柔性负荷与氢能精细化建模的综合能源系统低碳运行[J]. 电网技术, 2022, 46(5): 1692-1704.
Deng Jie, Jiang Fei, Wang Wenye, et al.Low-carbon optimized operation of integrated energy system considering electric-heat flexible load and hydrogen energy refined modeling[J]. Power System Technology, 2022, 46(5): 1692-1704.
[9] 朱兰, 王吉, 唐陇军, 等. 计及电转气精细化模型的综合能源系统鲁棒随机优化调度[J]. 电网技术, 2019, 43(1): 116-126.
Zhu Lan, Wang Ji, Tang Longjun, et al.Robust stochastic optimal dispatching of integrated energy systems considering refined power-to-gas model[J]. Power System Technology, 2019, 43(1): 116-126.
[10] Varela C, Mostafa M, Zondervan E.Modeling alkaline water electrolysis for power-to-x applications: a scheduling approach[J]. International Journal of Hydrogen Energy, 2021, 46(14): 9303-9313.
[11] 林顺富, 曾旭文, 沈运帷, 等. 考虑灵活性需求的园区综合能源系统协同优化配置[J]. 电力自动化设备, 2022, 42(9): 9-17.
Lin Shunfu, Zeng Xuwen, Shen Yunwei, et al.Collaborative optimal configuration of park-level integrated energy system considering flexibility requirement[J]. Electric Power Automation Equipment, 2022, 42(9): 9-17.
[12] 马燕峰, 谢家荣, 赵书强, 等. 考虑园区综合能源系统接入的主动配电网多目标优化调度[J]. 电力系统自动化, 2022, 46(13): 53-61.
Ma Yanfeng, Xie Jiarong, Zhao Shuqiang, et al.Multi-objective optimal dispatching for active distribution network considering park-level integrated energy system[J]. Automation of Electric Power Systems, 2022, 46(13): 53-61.
[13] 张涛, 刘景, 杨晓雷, 等. 计及主/被动需求响应与条件风险价值的微网经济调度[J]. 高电压技术, 2021, 47(9): 3292-3304.
Zhang Tao, Liu Jing, Yang Xiaolei, et al.Economic dispatch of microgrid considering active/passive demand response and conditional value at risk[J]. High Voltage Engineering, 2021, 47(9): 3292-3304.
[14] 汤翔鹰, 胡炎, 耿琪, 等. 考虑多能灵活性的综合能源系统多时间尺度优化调度[J]. 电力系统自动化, 2021, 45(4): 81-90.
Tang Xiangying, Hu Yan, Geng Qi, et al.Multi-time-scale optimal scheduling of integrated energy system considering multi-energy flexibility[J]. Automation of Electric Power Systems, 2021, 45(4): 81-90.
[15] 何畅, 程杉, 徐建宇, 等. 基于多时间尺度和多源储能的综合能源系统能量协调优化调度[J]. 电力系统及其自动化学报, 2020, 32(2): 77-84, 97.
He Chang, Cheng Shan, Xu Jianyu, et al.Coordinated optimal scheduling of integrated energy system considering multi-time scale and hybrid energy storage system[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 77-84, 97.
[16] 张大海, 贠韫韵, 王小君, 等. 计及风光不确定性的新能源虚拟电厂多时间尺度优化调度[J]. 太阳能学报, 2022, 43(11): 529-537.
Zhang Dahai, Yun Yunyun, Wang Xiaojun, et al.Multi-time scale of new energy scheduling optimization for virtual power plant considering uncertainty of wind power and photovoltaic power[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 529-537.
[17] 谢鹏, 蔡泽祥, 刘平, 等. 考虑多时间尺度不确定性耦合影响的风光储微电网系统储能容量协同优化[J]. 中国电机工程学报, 2019, 39(24): 7126-7136, 7486.
Xie Peng, Cai Zexiang, Liu Ping, et al.Cooperative optimization of energy storage capacity for renewable and storage involved microgrids considering multi time scale uncertainty coupling influence[J]. Proceedings of the CSEE, 2019, 39(24): 7126-7136, 7486.
[18] 马紫嫣, 贾燕冰, 韩肖清, 等. 考虑动态时间间隔的综合能源系统双层优化调度[J]. 电网技术, 2022, 46(5): 1721-1730.
Ma Ziyan, Jia Yanbing, Han Xiaoqing, et al.Two-layer dispatch model of integrated energy system considering dynamic time-interval[J]. Power System Technology, 2022, 46(5): 1721-1730.
[19] 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.
[20] 荆有印, 白鹤, 张建良. 太阳能冷热电联供系统的多目标优化设计与运行策略分析[J]. 中国电机工程学报, 2012, 32(20): 82-87, 143.
Jing Youyin, Bai He, Zhang Jianliang.Multi-objective optimization design and operation strategy analysis of a solar combined cooling heating and power system[J]. Proceedings of the CSEE, 2012, 32(20): 82-87, 143.
[21] 程浩忠, 胡枭, 王莉, 等. 区域综合能源系统规划研究综述[J]. 电力系统自动化, 2019, 43(7): 2-13.
Cheng Haozhong, Hu Xiao, Wang Li, et al.Review on research of regional integrated energy system planning[J]. Automation of Electric Power Systems, 2019, 43(7): 2-13.
[22] 孔顺飞, 胡志坚, 谢仕炜, 等. 含电动汽车充电站的主动配电网二阶段鲁棒规划模型及其求解方法[J]. 电工技术学报, 2020, 35(5): 1093-1105.
Kong Shunfei, Hu Zhijian, Xie Shiwei, et al.Two-stage robust planning model and its solution algorithm of active distribution network containing electric vehicle charging stations[J]. Transactions of China Electrotechnical Society, 2020, 35(5): 1093-1105. |
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2024, Vol. 39 
