家用熔融碳酸盐燃料电池发电系统日常运行的遗传算法优化

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家用熔融碳酸盐燃料电池发电系统日常运行的遗传算法优化

李勇; 曹广益; 余晴春

Institute of Fuel Cell, Shanghai Jiaotong University, Shanghai 200030, China

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-06-28 发布日期:2006-06-28
通讯作者: 李勇

Daily Operation Optimization of a Residential Molten Carbonate Fuel Cell Power System Using Genetic Algorithm

LIYong; CAOGuangyi; YUQingchun

Institute of Fuel Cell, Shanghai Jiaotong University, Shanghai 200030, China

Received:1900-01-01 Revised:1900-01-01 Online:2006-06-28 Published:2006-06-28
Contact: LI Yong

摘要/Abstract

摘要： To decrease the cost of electricity generation of a residential molten carbonate fuel cell (MCFC) power system, multi-crossover genetic algorithm (MCGA), which is based on “multi-crossover” and “usefulness-based selection rule”, is presented to minimize the daily fuel consumption of an experimental 10kW MCFC power system for residential application. Under the operating conditions obtained by MCGA, the operation constraints are satisfied and fuel consumption is minimized. Simulation and experimental results indicate that MCGA is efficient for the operation optimization of MCFC power systems.

关键词: molten carbonate fuel cell power system;fuel consumption;operation optimization;multi-crossover;residential fuel cell;genetic algorithm

Abstract: To decrease the cost of electricity generation of a residential molten carbonate fuel cell (MCFC) power system, multi-crossover genetic algorithm (MCGA), which is based on “multi-crossover” and “usefulness-based selection rule”, is presented to minimize the daily fuel consumption of an experimental 10kW MCFC power system for residential application. Under the operating conditions obtained by MCGA, the operation constraints are satisfied and fuel consumption is minimized. Simulation and experimental results indicate that MCGA is efficient for the operation optimization of MCFC power systems.

Key words: molten carbonate fuel cell power system, fuel consumption, operation optimization, multi-crossover, residential fuel cell, genetic algorithm

李勇, 曹广益, 余晴春. 家用熔融碳酸盐燃料电池发电系统日常运行的遗传算法优化[J]. CIESC Journal.

LIYong, CAOGuangyi, YUQingchun. Daily Operation Optimization of a Residential Molten Carbonate Fuel Cell Power System Using Genetic Algorithm[J]. .

参考文献

1    Tsunefumi, I., Yasue, H., “Start-up, testing and operation of 1000kW class MCFC power plant”, J. Power Sources, 86(1—2), 145—150(2000).
2    Barbir, F., Gomez, T., “Efficiency and economics of proton exchange membrane (PEM) fuel cells”, Int. J. of Hydrogen Energy, 2(10—11), 1027—1037(1997).
3    Azmy, A.M., Erlich, I., “Online optimal management of PEM fuel cells using neural networks”, IEEE Trans. Power Delivery, 20(2), 1051—1058(2005).
4    Kang, B.S., Koh, J.H., Lim, H.C., “Effects of system con-figuration and operating condition on MCFC system effi-ciency”, J. Power Sources, 108(1—2), 232—238(2002).
5    Au, S.F., McPhail, S.J., Woudstra, N., Hemmes, K., “The influence of operating temperature on the efficiency of a combined heat and power fuel cell plant”, J. Power Sources, 122(1), 37—46(2003).
6    Luh, G.C., Chueh C.H., “Multi-objective optimal design of truss structure with immune algorithm”, Comput. Struct., 82(11—12), 829—844(2004).
7    Jae, K.K,, Dong, H.C., Hyun, K.J., “Niching genetic al-gorithm adopting restricted competition selection com-bined with pattern search method”, IEEE Trans. Magnet., 38(2), 1001—1004(2002).
8    Cochran, J.K., Horng, S.M., Fowler, J.W., “A multi-population genetic algorithm to resolve multi-objective scheduling problems for parallel ma-chines”, Comput. Oper. Res., 30(7), 1087—1102(2003).
9    Yu, L.J., Yuan, J.Q., Cao, G.Y., “Numerical simulation of dynamic performance of the molten carbonate fuel cell”, Chin. J. Chem. Eng., 12(2), 272—276(2004).
10    Lukas, M.D., Lee, K.Y., Ghezel-Ayagh, H., “An explicit dynamic model for direct reforming carbonate fuel cell stack”, IEEE Trans. Energy Conv., 16(3), 289—295(2001).

家用熔融碳酸盐燃料电池发电系统日常运行的遗传算法优化

Daily Operation Optimization of a Residential Molten Carbonate Fuel Cell Power System Using Genetic Algorithm

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