Robust optimal design method for photovoltaic cells

Patent metadata

Field	Value
Publication number	US-11630936-B2
Application number	US-202117369105-A
Country	US
Kind code	B2
Filing date	Jul 7, 2021
Priority date	Aug 26, 2020
Publication date	Apr 18, 2023
Grant date	Apr 18, 2023

Abstract

This invention relates to a robust optimal design method for photovoltaic cells. Firstly, the deterministic optimal model is established, which is solved by Monte Carlo method to obtain the maximum output power value of optimization objective and its corresponding design variable value, and then the design variable value obtained from deterministic optimization is deemed as the initial point of the mean value of the robust optimal design variable. Later, the robust optimal model is solved by Monte Carlo method in order to obtain the mean value of design variable, and then appropriate materials and manufacturing techniques are selected for corresponding photovoltaic components according to the design variable obtained, so as to achieve the robust optimal design of photovoltaic cells. In fact, this invention improves the output stability and reliability of photovoltaic cells.

First claim

What is claimed is:

1. A robust optimal design method for photovoltaic cells, which is characterized that it comprises the following steps:
Step 1: Take material parameters of photovoltaic components as design variable x and temperature and radiation intensity in an actual working environment as design parameter z, and a specific distribution is as shown in Table 1:

TABLE 1

Distribution of Design Variables and Parameters of Photovoltaic Cells

Parameter
Identi-

Distribution

Standard

Name
fication
Unit
Type
Mean Value
Deviation

  R x
x 1
Ω
Normal
[0.286, 0.656]
9.596 × 10 −3

Distribution

   R sh
x 2
Ω
Normal
[802.24, 1602.2]
22.2298

Distribution

C
x 3
mA/C
Normal
[0.0059, 0.0061]
  1.2 × 10 −3

Distribution

n
x 4
/
Normal
[1.18, 1.6] 
0.003

Distribution

S
z 1
W/m 2
Normal
600
8

Distribution

T
z 2
K
Normal
303.15
6.063

Distribution

wherein R s is equivalent series resistance of photovoltaic cells, R SH is equivalent shunt resistance of photovoltaic cells, C is temperature coefficient of short circuit current, n is diode ideality factor, S is radiation intensity, and T is surface temperature of photovoltaic cells,
Take a maximum output power of photovoltaic cells as optimization objective and theoretical efficiency of a studied cell as constrained performance function to establish a deterministic optimal model as follows:
Find x1, x2, x3, x4, and Max P(x,z), wherein Max P(x,z) is maximum output power of photovoltaic cells, given:
s.t.g(x,z)=η(x,z)−0.159≤0, wherein s.t.g is objective function corresponding to inequality constraints about conversion efficiency and η is conversion efficiency of photovoltaic cells;
0.286≤x1≤0.656, 802.24≤x2≤1602.24;
0.0059≤x3≤0.0061, 1.18≤x4≤1.6; and
Solve the deterministic optimal model by Monte Carlo method to obtain the maximum output power value Max P(x,z) of the optimization objective and its corresponding design variable value x={x1, x2, x3, x4};
Step 2: Take the design variable value obtained from deterministic optimization as an initial point of a mean value of robust optimal design variable (μx′={x 1 , x 2 , x 3 , x 4 }), and then obtain a robust optimal model based on the mean value and standard deviation after conversion as listed in Table 1, which is expressed as follows:
Find μ x 1 , μ x 2 , μ x 3 , μ x 4 ,

Min
⁢

σ
p

(

x
,
z

)

μ
p

(

x
,
z

)

,

wherein σ p is mean value of output power and μ p is standard deviation of output power, given:
s.t.G(x,z)=μ g(x,z) ≤0, wherein s.t.G is objective function corresponding to inequality constraints on mean value of the conversion efficiency and g is objective function corresponding to inequality constraints about conversion efficiency,
0.286≤μ x 1 ≤0.656, 802.24≤μ x 2 ≤1602.24
0.0059≤μ x 3 ≤0.0061, 1.18≤μ x 4 ≤1.6; and
Step 3: Solve the robust optimal model by Monte Carlo method to obtain a mean value μ x {μ x 1 , μ x 2 , μ x 3 , μ x 4 }, of design variables, and then select corresponding materials and manufacturing techniques of photovoltaic components according to the obtained μx, so as to achieve robust optimal design for photovoltaic cells.

Assignees

• Univ Northwestern Polytechnical

Inventors

• Zhang Feng
• Wu Mingying
• Zhang Xu
• Wang Dongyue
• Xu Xiayu
• Cheng Lei

Classifications

• G06F2119/06

  Power analysis or power optimisation · CPC title

• H02J7/00

  Circuit arrangements for charging or discharging batteries or for supplying loads from batteries · CPC title

• Y02E10/50

  Photovoltaic [PV] energy · CPC title

• G06F30/373 Primary

  Design optimisation · CPC title

• G06F17/15

  Correlation function computation {including computation of convolution operations (arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00)} · CPC title