Optimal Schedule of Combined Heat-Power Microgrid Based on Hydrogen Energy Storage
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摘要:
针对质子交换膜燃料电池和电解槽的热电联供特性，为避免氢能系统的热能浪费并进一步提高氢能系统的效率，搭建了一种考虑氢能系统的热电联供型光伏/风机/燃料电池/蓄电池/电锅炉/燃气锅炉微电网系统，提出一种包括日前调度与实时优化的两阶段优化调度方法. 所建系统考虑了电氢转换时的余热回收，将氢能系统作为热电氢耦合设备，实现了电、热、氢能的协调利用与相互转换，有效提高了能量利用率. 在第一阶段调度中，根据日前的风光发电出力及负荷需求预测，以微电网整体运行成本最小为目标，采用混合整数线性规划方法实现日前最优全局调度；在第二阶段调度中，根据超短期预测结果，使用模型预测控制嵌入混合整数二次规划算法，减小预测误差带来的经济性影响. 最后，通过冬、夏及过渡季典型日算例可知，本文所提出的两阶段调度方法在3种季节典型日的总成本较日前全局最优调度分别降低了3.24%、0.76%、1.66%；通过在不同场景下对本文所提方法进行仿真验证，相较于不考虑能量耦合的基础场景，考虑热电耦合系统和热电氢耦合系统时，优化调度的总成本和污染气体治理成本分别降低了15.58%、24.93%. 结果表明：本文所提方法具有一定的实时性及通用性，能够满足微网内热电负荷需求，实现稳定独立运行，改善系统的运行经济性与环保性.
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.-
Key words:
- hydrogen energy /
- combined heat-power microgrid /
- optimal scheduling
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图 1 热电氢联供型微电网系统结构
Figure 1. Structure of combined heat-power-hydrogen microgrid system
表 1 日前优化成本和日内优化成本
Table 1. Day-ahead and real-time optimized cost
元 成本项目 冬季典型日 夏季典型日 过渡季典型日 调度
成本优化
成本调度
成本优化
成本调度
成本优化
成本运维 436.78 435.00 226.02 221.54 317.08 312.65 燃料 623.59 597.28 162.67 164.51 504.94 496.62 污染气体治理 169.67 157.87 44.21 43.58 136.82 133.69 总成本 1230.04 1190.15 432.90 429.63 958.85 942.96 
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表 2 微电网各能量占比
Table 2. Energy proportion in microgrid
% 季节 电能 热能 总能量利用率 冬季 71.97 28.03 89.43 夏季 76.13 23.87 91.16 过渡季 63.87 36.13 93.13
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表 3 不同场景下的调度成本
Table 3. Scheduling cost in different scenarios
元 场景 运维成本 燃料成本 污染气体
治理成本总成本 1 235.58 214.81 58.53 508.92 2 198.76 200.47 56.14 455.37 3 229.61 160.33 44.83 434.37 4 221.54 164.51 43.58 429.63
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表 4 优化调度的计算时间
Table 4. Calculation time for optimal scheduling
s 计算时间 冬季典型日 夏季典型日 过渡季典型日 调度 5.59 6.37 6.15 优化 87.76 85.56 86.92 总时间 93.36 91.93 93.08
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表 5 不同权重系数下的优化成本
Table 5. Optimal cost under different weight coefficients
季节 组编号 λ1 λ2 λ3 λ4 λ5 λ6 λ7 运维成本/元 燃料成本/元 污染气体治理成本/元 总成本/元 冬季 1 0.3 0.5 0.1 0.21 0.2 0.3 0.1 435.00 597.28 157.87 1190.15 2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 438.33 602.02 158.66 1199.00 3 0.4 0.3 0.2 0.1 0.3 0.2 0.3 439.09 595.60 157.64 1192.33 4 0.5 0.4 0.3 0.2 0.4 0.5 0.2 438.73 597.92 158.09 1194.74 5 0.3 0.3 0.1 0.21 0.2 0.2 0.2 438.94 597.63 157.96 1194.53 夏季 1 0.3 0.5 0.1 0.21 0.2 0.3 0.1 221.54 164.51 43.58 429.63 2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 222.67 165.18 44.28 432.12 3 0.4 0.3 0.2 0.1 0.3 0.2 0.3 222.31 165.36 44.25 431.92 4 0.5 0.4 0.3 0.2 0.4 0.5 0.2 222.43 165.26 44.24 431.94 5 0.3 0.3 0.1 0.21 0.2 0.2 0.2 222.77 164.99 44.45 432.21 过渡季 1 0.3 0.5 0.1 0.21 0.2 0.3 0.1 312.65 496.62 133.69 942.96 2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 314.01 496.54 134.21 944.76 3 0.4 0.3 0.2 0.1 0.3 0.2 0.3 313.56 496.02 134.02 943.59 4 0.5 0.4 0.3 0.2 0.4 0.5 0.2 313.69 496.16 134.07 943.91 5 0.3 0.3 0.1 0.21 0.2 0.2 0.2 313.58 496.77 134.22 944.57
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[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/AEPS20191220007WANG 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/AEPS20171129003WANG 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/AEPS20180629016JIN 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.0258-2724.20180860CHEN 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.170597KONG 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.2018.1528PU 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.2013.03.002DING 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. -
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