EN基于概率可靠度的槽式太阳能电站优化设计
Optimization Design of Trough Solar Power Plant Based on Probabilistic Reliability
- 发电技术 2020年41卷第6期 页码：590-598
- 作者机构：
1.华中科技大学能源与动力工程学院, 湖北省 武汉市 430074
2.华中科技大学中欧清洁与可再生能源学院, 湖北省 武汉市 430074
- 作者简介：
[ "张燕平(1971), 女, 博士, 副教授, 主要研究方向为太阳能热发电系统优化, zyp2817@hust.edu.cn" ]
[ "张宇超(1994), 男, 硕士研究生, 主要研究方向为新能源科学与工程, 15927687652@163.com" ]
- 基金信息：湖北省技术创新专项(2019AAA017)
DOI：10.12096/j.2096-4528.pgt.19143
中图分类号： TK519收稿：2020-03-07，
纸质出版：2020-12-31
- 稿件说明：
移动端阅览
张燕平, 张宇超, 刘易飞. 基于概率可靠度的槽式太阳能电站优化设计[J]. 发电技术, 2020,41(6):590-598.
Yanping ZHANG, Yuchao ZHANG, Yifei LIU. Optimization Design of Trough Solar Power Plant Based on Probabilistic Reliability[J]. Power Generation Technology, 2020, 41(6): 590-598.
张燕平, 张宇超, 刘易飞. 基于概率可靠度的槽式太阳能电站优化设计[J]. 发电技术, 2020,41(6):590-598. DOI： 10.12096/j.2096-4528.pgt.19143.
Yanping ZHANG, Yuchao ZHANG, Yifei LIU. Optimization Design of Trough Solar Power Plant Based on Probabilistic Reliability[J]. Power Generation Technology, 2020, 41(6): 590-598. DOI： 10.12096/j.2096-4528.pgt.19143.
摘要
基于概率可靠度对槽式太阳能电站系统配置进行了优化设计。首先，通过SAM软件对建设50 MW槽式太阳能电站进行了模拟，并考虑不确定性因素带来的随机性，结合神经网络-蒙特卡罗法，建立了槽式太阳能电站基于其太阳倍数、蓄热系统蓄热时长以及集热槽行间距3个设计参数的不确定性模型。其次，建立了可靠度计算模型，选取平准化度电成本（levelized cost of energy，LCOE）、容量因子（capacity factor，CF）和总发电效率作为性能评价指标，以各性能评价指标的可靠性指标最优为优化目标，进行了可靠性计算与分析。最后，将不确定性模型下得出的各设计参数的最优配置与确定性模型下的优化结果进行了对比。由于考虑了不确定性因素的影响，不确定性模型下的计算结果更接近实际情况。
Abstract
Based on the probabilistic reliability
the configuration of the trough solar power plant was optimized. Firstly
the SAM software was used to simulate the 50 MW trough solar power plant. Then
by considering the randomness caused by the uncertainty factor
the uncertainty model of the trough solar power plant based on its solar multiple
the full load hours of storage system and the row spacing distance of the collector was established by neural network-Monte Carlo method. Secondly
the reliability calculation model was established
levelized cost of energy (LCOE)
capacity factor (CF) and total generation efficiency were selected as key performance indicators (KPI)
then the KPI were optimized based on the reliability indexes of each KPI. Finally
the optimal configuration of each design parameter obtained under the uncertainty model was compared with the optimization result under the deterministic model. Since the influence of the uncertainty factor is considered
the calculation result under the uncertainty model is closer to the actual situation.
关键词
Keywords
references
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