基于火用经济分析的电氢能源系统区间鲁棒优化调度

doi:10.19595/j.cnki.1000-6753.tces.240619

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Abstract

Hydrogen, as a clean, efficient, and high-quality energy source, is recognized as a crucial solution for decarbonizing the energy system and mitigating climate change. The electricity and hydrogen energy system, which uses electricity and hydrogen as energy carriers, represents a key pathway for integrating power systems with hydrogen energy. It helps overcome the developmental limitations of renewable energy, fosters the interconnection and complementarity of multiple energy modes, and promotes deep integration across generation, grid, load, and storage. The electro-hydrogen coupling process, central to this system, can lower operating costs through peak shaving and valley filling. However, the efficiency of electrolyzers and fuel cell remain suboptimal, resulting in significant exergy losses alongside economic benefits during the coupling process. Striking a balance between economic viability and energy saving continues to be a challenging task. Moreover, the substantial forecasting errors caused by the uncertainty of renewable energy outputs can negatively impact the supply-demand balance and the operating conditions of electrolyzers. Therefore, the uncertainty risks associated with renewable energy must be thoroughly considered in optimal scheduling. In response to the above problems, a robust optimal scheduling model based on exergoeconomic analysis is proposed, with the uncertainty set defined by the confidence interval to reduce the conservatism of robust optimization.
Firstly, considering the dynamic efficiency characteristics of the electrolyzer, piecewise linearization was applied to handle the non-convex terms introduced by this relationship. The operation model of the electrolyzer including hydrogen production power allocation and operation models of fuel cell and energy storage equipment were constructed. Secondly, the energy quality coefficients were employed to analyze the exergy loss distribution based on the equipment operation model. A cost accounting method for exergy losses, including both internal and external factors, was proposed. Internally, the cost allocation method was used to price unit exergy losses, enabling the calculation of operational loss costs based on the distribution of exergy losses. Externally, the cost of transmission line losses and penalties of wind curtailment were calculated according to current electricity prices and relevant policies. Thirdly, taking into account constraints such as electrolyzer start-stop cycles, ramping power, and energy balance, an optimal scheduling model was developed with the goal of minimizing total exergy loss costs in the electricity and hydrogen energy system. Then, the model was reformulated into a robust optimization problem based on the uncertainty set of the confidence interval,and a dual transformation method for solving the model was proposed.
In the case simulation, four cases are set up for comparative analysis, leading to the following conclusions: (1) By setting the wind curtailment penalty coefficient appropriately, with the goal of minimizing exergy loss costs, a balance can be achieved between the economic benefits and the exergy losses associated with the electricity-hydrogen coupling process, while ensuring the efficient absorption of wind power. (2) The proposed model can further improve the overall hydrogen production efficiency of the electrolyzer array by taking advantage of the flexibility of hydrogen production power allocation. (3) The confidence interval is used as the uncertainty set of robust optimization, which can take into account the probability characteristics of random variables, and reduce the conservative degree of system operation under the premise of ensuring robustness.

Key words： Electricity and hydrogen energy system exergoeconomic analysis exergy loss cost confidence interval robust optimizatio

Received: 22 April 2024

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	Li Zhiwei
	Zhao Yuze
	Wu Pei
	Zhang Hao
	Zhao Shuqiang

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Li Zhiwei,Zhao Yuze,Wu Pei等. Interval Robust Optimal Scheduling of Electricity and Hydrogen Energy System Based on Exergoeconomic Analysis[J]. Transactions of China Electrotechnical Society, 0, (): 619-2.

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[1] 中华人民共和国国家发展和改革委员会. 《氢能产业发展中长期规划(2021-2035年)》[EB/OL]. [2022-03-23].https://zfxxgk.nea.gov.cn/2022-03/23/c_1310525630.htm.
[2] 程欢, 任洲洋, 孙志媛, 等. 电能-甲醇跨区协同输运下的电-氢耦合系统调度[J]. 电工技术学报, 2024, 39(3): 731-744.
Cheng Huan, Ren Zhouyang, Sun Zhiyuan, et al.A dispatching for the electricity-hydrogen coupling systems considering the coordinated inter-region transportation of electricity and methanol[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 731-744.
[3] 清华大学气候变化与可持续发展研究院. 《中国的氢能政策和技术现状及发展建议》 [EB/OL].[2023-11-27].https://lce.tsinghua.edu.cn/info/1042/1457.htm.
[4] 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.
[5] 郜捷, 宋洁, 王剑晓, 等. 支撑中国能源安全的电氢耦合系统形态与关键技术[J]. 电力系统自动化, 2023, 47(19): 1-15.
Gao Jie, Song Jie, Wang Jianxiao, et al.Form and key technologies of integrated electricity-hydrogen system supporting energy security in China[J]. Automation of Electric Power Systems, 2023, 47(19): 1-15.
[6] Zhong Zhiyao, Fang Jiakun, Hu Kewei, et al.Power-to-hydrogen by electrolysis in carbon neutrality: technology overview and future development[J]. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1266-1283.
[7] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485.
Wu Mengxue, Fang Fang.Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.
[8] 罗潇, 任洲洋, 温紫豪, 等. 考虑氢能系统热回收的电氢区域综合能源系统日前优化运行[J]. 电工技术学报, 2023, 38(23): 6359-6372.
Luo Xiao, Ren Zhouyang, Wen Zihao, et al.A day-ahead dispatching method of regional integrated electric-hydrogen energy systems considering the heat recycle of hydrogen systems[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6359-6372.
[9] Feng Chenjia, Shao Chengcheng, Xiao Yunpeng, et al.Day-ahead strategic operation of hydrogen energy service providers[J]. IEEE Transactions on Smart Grid, 2022, 13(5): 3493-3507.
[10] Hernández-Gómez Á, Ramirez V, Guilbert D.Investigation of PEM electrolyzer modeling: electrical domain, efficiency, and specific energy consumption[J]. International Journal of Hydrogen Energy, 2020, 45(29): 14625-14639.
[11] 李志伟, 赵雨泽, 吴培, 等. 基于制氢设备精细建模的综合能源系统绿氢蓝氢协调低碳优化策略[J]. 电网技术, 2024, 48(6): 2317-2326.
Li Zhiwei, Zhao Yuze, Wu Pei, et al.Low-carbon dispatching strategy of integrated energy system with coordination of green hydrogen and blue hydrogen based on fine modeling of hydrogen production equipment[J]. Power System Technology, 2024, 48(6): 2317-2326.
[12] 胡俊杰, 童宇轩, 刘雪涛, 等. 计及精细化氢能利用的综合能源系统多时间尺度鲁棒优化策略[J]. 电工技术学报, 2024, 39(5): 1419-1435.
Hu Junjie, Tong Yuxuan, Liu Xuetao, et al.Multi-time-scale robust optimization strategy for integrated energy system considering the refinement of hydrogen energy use[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1419-1435.
[13] 李建林, 赵文鼎, 梁忠豪, 等. 基于混合电解槽制氢系统的功率分配技术[J]. 电力系统自动化, 2024, 48(13): 9-18.
Li Jianlin, Zhao Wending, Liang Zhonghao, et al.Power distribution technology based on hybrid-electrolyzer hydrogen production system[J]. Automation of Electric Power Systems, 2024, 48(13): 9-18.
[14] 沈小军, 聂聪颖, 吕洪. 计及电热特性的离网型风电制氢碱性电解槽阵列优化控制策略[J]. 电工技术学报, 2021, 36(3): 463-472.
Shen Xiaojun, Nie Congying, Lü Hong.Coordination control strategy of wind power-hydrogen alkaline electrolyzer bank considering electrothermal characteristics[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 463-472.
[15] Ding Hongbing, Dong Yuanyuan, Zhang Yu, et al.Energy efficiency assessment of hydrogen recirculation ejectors for proton exchange membrane fuel cell (PEMFC) system[J]. Applied Energy, 2023, 346: 121357.
[16] 中华人民共和国中央人民政府. 《2024—2025年节能降碳行动方案》[EB/OL].[2024-05-29]. https://www.gov.cn/zhengce/content/202405/content_6954322.htm.
[17] Hu Zhengbiao, He Dongfeng, Zhao Hongbo.Multi-objective optimization of energy distribution in steel enterprises considering both exergy efficiency and energy cost[J]. Energy, 2023, 263: 125623.
[18] Chen Jianrun, Chen Haoyong, Liang Zipeng, et al.An exergy analysis model for the optimal operation of integrated heat-and-electricity-based energy systems[J]. Protection and Control of Modern Power Systems, 2024, 9(1): 1-18.
[19] 何帅, 刘念, 盛超群, 等. 多能源枢纽联合运行的? 损最小化分布式优化调度[J]. 电力系统自动化, 2021, 45(9): 28-37.
He Shuai, Liu Nian, Sheng Chaoqun, et al.Distributed optimal scheduling for minimizing exergy loss based on joint operation of multiple energy hubs[J]. Automation of Electric Power Systems, 2021, 45(9): 28-37.
[20] 刘帅东, 韩松, 荣娜, 等. 计及火用效率的电-气-热综合能源系统多目标优化调度方法[J]. 电网技术, 2024, 48(7): 2715-2722.
Liu Shuaidong, Han Song, Rong Na, et al.A multi-objective optimal scheduling method for integrated electricity-gas-heat energy system taking into account the exergy efficiency of the integrated energy system[J]. Power System Technology, 2024, 48(7): 2715-2722.
[21] 亓海青, 韩巍, 张娜, 等. 基于能的品位概念的火用经济分析方法及其案例分析[J]. 中国电机工程学报, 2016, 36(12): 3223-3231.
Qi Haiqing, Han Wei, Zhang Na, et al.Exergoeconomic analysis methodology based on energy level and case study[J]. Proceedings of the CSEE, 2016, 36(12): 3223-3231.
[22] Fu Yidan, Cai Lei, Liu Chunming, et al.Thermodynamic and economic performance comparison of biomass gasification oxy-fuel combustion power plant in different gasifying atmospheres using advanced exergy and exergoeconomic approach[J]. Renewable Energy, 2024, 226: 120290.
[23] 杨俊兰, 高思雨, 李久东. CO₂跨临界制冷循环系统? 经济分析[J]. 太阳能学报, 2020, 41(1): 60-65.
Yang Junlan, Gao Siyu, Li Jiudong.Exergoeconomic analysis of CO₂ transcritical refrigeration cycle system[J]. Acta Energiae Solaris Sinica, 2020, 41(1): 60-65.
[24] Wang Xi, Henshaw P, Ting D S.Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO)[J]. Applied Energy, 2021, 294: 116952.
[25] Yang Kun, He Yiyun, Du Na, et al.Exergy, exergoeconomic, and exergoenvironmental analyses of novel solar- and biomass-driven trigeneration system integrated with organic Rankine cycle[J]. Energy, 2024, 301: 131605.
[26] 韩子娇, 那广宇, 董鹤楠, 等. 考虑灵活性供需平衡的含电转氢综合能源系统鲁棒优化调度[J]. 电力系统保护与控制, 2023, 51(6): 161-169.
Han Zijiao, Na Guangyu, Dong Henan, et al.Robust optimal operation of integrated energy system with P2H considering flexibility balance[J]. Power System Protection and Control, 2023, 51(6): 161-169.
[27] 王灿, 张雪菲, 凌凯, 等. 基于区间概率不确定集的微电网两阶段自适应鲁棒优化调度[J]. 中国电机工程学报, 2024, 44(5): 1750-1764.
Wang Can, Zhang Xuefei, Ling Kai, et al.Two-stage adaptive robust optimal scheduling based on the interval probability uncertainty set for microgrids[J]. Proceedings of the CSEE, 2024, 44(5): 1750-1764.
[28] 清华大学电机工程与应用电子技术系. 《构建新型电力系统的战略构想: 新能源为主、电网配送、电氢融合》[EB/OL]. [2021-07-10]. https://www.eea.tsinghua.edu.cn/info/1038/2092.htm
[29] 袁铁江, 万志, 王进君, 等. 考虑电解槽启停特性的制氢系统日前出力计划[J]. 中国电力, 2022, 55(1): 101-109.
Yuan Tiejiang, Wan Zhi, Wang Jinjun, et al.The day-ahead output plan of hydrogen production system considering the start-stop characteristics of electrolytic cell[J]. Electric Power, 2022, 55(1): 101-109.
[30] 张艺晨, 戈志华, 杨勇平, 等. 基于火用分析的热电联产能耗一致性评价方法[J]. 中国电机工程学报, 2024, 44(2): 642-651.
Zhang Yichen, Ge Zhihua, Yang Yongping, et al.Unified evaluation method of cogeneration energy consumption based on exergy analysis[J]. Proceedings of the CSEE, 2024, 44(2): 642-651.
[31] 国家市场监督管理总局, 国家标准化管理委员会. 能量系统分析技术导则: GB/T 14909—2021[S]. 北京: 中国标准出版社, 2021.
[32] 中华人民共和国国家发展和改革委员会. 《关于第三监管周期省级电网输配电价及有关事项的通知》[EB/OL].[2023-05-15]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202305/t20230515_1355747.html.
[33] 林雨眠, 熊厚博, 张笑演, 等. 计及新能源机会约束与虚拟储能的电-热系统分布式多目标优化调度[J]. 电工技术学报, 2024, 39(16): 5042-5059.
Lin Yumian, Xiong Houbo, Zhang Xiaoyan, et al.Distributed multi-objective optimal scheduling of integrated electric-heat system considering chance constraint of new energy and virtual storage[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5042-5059.
[34] Li Peng, Yang Ming, Wu Qiuwei.Confidence interval based distributionally robust real-time economic dispatch approach considering wind power accommodation risk[J]. IEEE Transactions on Sustainable Energy, 2021, 12(1): 58-69.