Optimization Design of Trough Solar Power Plant Based on Probabilistic Reliability

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

Abstract

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

CLC Number:

TK519

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.

Figures/Tables 17

Fig.1 Trough solar thermal power generation system

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

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	—

Fig.2 Neural network topology diagram

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

Fig.3 Reliability calculation flow chart

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

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

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

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

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

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

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

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

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

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

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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