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Optimization of solar thermal power station LCOE based on NSGA-II algorithm

LU Xiao-juan1, LI Xin-yang1, DONG Hai-ying2

 

1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. School of New Energy and Power Engineering, Lanzhou Jiaotong University,  Lanzhou 730070, China)

 

Abstract: In view of the high cost of solar thermal power generation in China, it is difficult to realize large-scale production in engineering and industrialization. Non-dominated sorting genetic algorithm II (NSGA-II) is applied to optimize the levelling cost of energy (LCOE) of the solar thermal power generation system in this paper. Firstly, the capacity and generation cost of the solar thermal power generation system are modeled according to the data of several sets of solar thermal power stations which have been put into production abroad. Secondly, the NSGA-II genetic algorithm and particle swarm algorithm are applied to the optimization of the solar thermal power station LCOE respectively. Finally, for the linear Fresnel solar thermal power system, the simulation experiments are conducted to analyze the effects of different solar energy generation capacities, different heat transfer mediums and loan interest rates on the generation price. The results show that due to the existence of scale effect, the greater the capacity of the power station, the lower the cost of leveling and electricity, and the influence of the types of heat storage medium and the loan on the cost of leveling electricity are relatively high.

 

Key words: solar thermal power generation; levelling cost of energy (LCOE); linear Fresnel; non-dominated sorting genetic algorithm II (NSGA-II)

 

CLD number: TM76   Document code: A

 

Article ID: 1674-8042(2018)01-0001-08     doi:10.3969/j.issn.1674-8042.2018.01.001

 

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基于NSGA-Ⅱ算法的太阳能热发电站平准化度电成本优化

 

 路小娟1, 李欣阳1, 董海鹰2

 

1. 兰州交通大学 自动化与电气工程学院, 甘肃 兰州 730070; 2. 兰州交通大学 新能源与动力工程学院, 甘肃 兰州 730070)

 

 :  针对目前中国太阳能热发电成本高, 难以实现工程化和产业化大规模生产的问题, 提出了应用NSGA-II遗传算法对太阳能热发电系统的平准化度电成本的优化研究。 首先, 根据国外已经投入生产的多组太阳能热发电站数据对太阳能热发电系统的容量和发电成本进行建模; 其次, 将NSGA-II遗传算法与粒子群算法分别应用到太阳能热发电系统的平准化度电成本进行优化分析; 最后, 针对线性菲涅尔热发电系统, 在分别考虑不同的太阳能发电容量、 不同的导热介质以及贷款利率对发电价格影响的情况下, 进行了仿真实验分析。 结果表明, 由于规模效应的存在, 电站容量越大, 其平准化度电成本越低, 并且储热介质类型与贷款情况对平准化度电成本的影响较大。

 

关键词:  太阳能热发电; 平准化度电成本; 线性菲涅尔; NSGA-II算法

 

引用格式:  LU Xiao-juan, LI Xin-yang, DONG Hai-ying. Optimization of solar thermal power station LCOE based on NSGA-II algorithm. Journal of Measurement Science and Instrumentation, 2018, 9(1): 1-8. [doi: 10.3969/j.issn.1674-8042.2018.01.001]


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