基于概率可靠度的槽式太阳能电站优化设计

doi:10.12096/j.2096-4528.pgt.19143

摘要/Abstract

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。

关键词: 槽式太阳能电站, 概率可靠度, 优化设计, 不确定性, 神经网络

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.

Key words: trough solar power plant, probabilistic reliability, optimization design, uncertainty, neural network

中图分类号:

TK519

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.

图/表 17

图1 槽式太阳能热发电系统

Fig.1 Trough solar thermal power generation system

表1 槽式太阳能热发电系统设计参数

Tab. 1 Design parameters of trough solar thermal power system

项目	参数	数值
气象数据	经度/(°)	91.15
	纬度/(°)	29.65
	太阳直接辐射量/[(kW∙h)·m⁻²·d⁻¹]	7.12
集热系统	太阳倍数	1~6
	集热槽行间距/m	15~31
	集热器类型	Sloargenix SGX-1
	镜面反射率	0.935
	镜面清洁度	0.97
	集热管类型	2008 Schott PTR70
	集热管吸收率	0.96
	集热管透过率	0.963
	导热工质	Therminol VP-1
	集热场进(出)口温度/℃	293(391)
发电系统	机组净输出功率/MW	50
	机组总输出功率/MW	55
	汽轮机组发电效率	0.373 6
蓄热系统	全负荷当量时间/h	0~16
	最大蓄热量/(MW∙h)	0~2 376.88
	热损失/MW	0.97

表2 各性能评价指标的允许极限

Tab. 2 Allowable limits for each key performance indicator

性能评价指标	允许下限	允许上限
容量因子/%	54.60	—
LCOE/[美分/(kW∙h)]	—	17.12^[14]
总发电效率/%	13.5	—

图2 神经网络拓扑结构图

Fig.2 Neural network topology diagram

表3 各设计参数的分布参数

Tab. 3 Distribution parameters of each parameter

设计参数	均值	方差
太阳倍数	1~6	0.50
蓄热系统蓄热时长/h	0~16	0.90
集热槽行间距/m	15~31	2.25

图3 可靠性计算流程图

Fig.3 Reliability calculation flow chart

表4 基于不同性能评价指标的最优配置及其可靠性计算结果

Tab. 4 Optimal configuration based on different key performance indicators and its reliability calculation results

最优指标	X_SM	T/h	D/m	β₁	β₂	β₃
LCOE	3.84	13.57	19.28	2.31	8.37	−0.26
CF	6.00	15.97	29.12	10.81	2.34	−3.57
η	2.45	15.91	30.32	−0.34	−0.80	6.24

图4 基于LCOE最优的设计参数配置下LCOE的累积概率与发生概率分布

Fig.4 Cumulative probability and occurrence probability distribution for LCOE of optimal parameter configuration based on LCOE

图5 基于LCOE最优的设计参数配置下容量因子的累积概率与发生概率分布

Fig.5 Cumulative probability and occurrence probability distribution for capacity factor of optimal parameter configuration based on LCOE

图6 基于LCOE最优的设计参数配置下总发电效率的累积概率与发生概率分布

Fig.6 Cumulative probability and occurrence probability distribution for total generation efficiency of optimal parameter configuration based on LCOE

图7 基于容量因子最优的设计参数配置下容量因子的累积概率与发生概率分布

Fig.7 Cumulative probability and occurrence probability distribution for capacity factor of optimal parameter configuration based on capacity factor

图8 基于容量因子最优的设计参数配置下LCOE的累积概率与发生概率分布

Fig.8 Cumulative probability and occurrence probability distribution for LCOE of optimal parameter configuration based on capacity factor

图9 基于容量因子最优的设计参数配置下总发电效率的累积概率与发生概率分布

Fig.9 Cumulative probability and occurrence probability distribution for total generation efficiency of optimal parameter configuration based on capacity factor

图10 基于总发电效率最优的设计参数配置下总发电效率的累积概率与发生概率分布

Fig.10 Cumulative and occurrence probability distribution for total generation efficiency of optimal parameter configuration based on total generation efficiency

图11 基于总效率最优的设计参数配置下LCOE的累积概率与发生概率分布

Fig.11 Cumulative and occurrence probability distribution for LCOE of optimal parameter configuration based on total generation efficiency

图12 基于总发电效率最优的设计参数配置下容量因子的累积概率与发生概率分布

Fig.12 Cumulative and occurrence probability distribution for capacity factor of optimal parameter configuration based on total generation efficiency

表5 确定性与不确定性模型优化结果对比

Tab. 5 Comparison of deterministic and uncertainty model optimization results

模型	优化结果	太阳倍数	蓄热时长/h	行间距/m	CF/%	LCOE/[美分/(kW∙h)]	η/%
确定性模型	LCOE最优	3.88	13.58	18.50	69.36	14.05	13.90
	CF最优	6.00	16.00	26.71	82.95	15.54	10.75
	η最优	2.56	16.00	27.50	51.70	16.78	15.70
不确定性模型	LCOE最优	3.84	13.57	19.28	65.28	14.33	13.26
	CF最优	6.00	15.97	29.12	79.28	15.67	10.50
	η最优	2.45	15.91	30.32	46.91	18.17	15.00

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