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Low-voltage and high-penetration distributed
photovoltaic access control technology based on
intelligent measurement
To cite this article: Yinglong Wang and Yongjiang Guo 2024
J. Phys.: Conf. Ser.
2770 012029
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2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
1
Low-voltage and high-penetration distributed photovoltaic
access control technology based on intelligent measurement
Yinglong Wang 1, 3 and Yo ngjiang Guo 2
1State Grid Guyuan Electric Power Supply Company, Guyuan 756000, Ningxia,
China
2State Grid Zhongwei Electric Power Supply Company, Zhongwei 755000, Ningxia,
China
3Corresponding author’s e-mail: www8825252023@163.com
Abstract: This project takes high-penetration distributed photovoltaic access as the
background and studies the control method for low voltage and high penetration distributed
photovoltaic access. Firstly, by analyzing the measured data of the low-voltage distribution
network, the daily load and measured values of the low-voltage distribution network were
obtained. On this basis, the topology structure of a high-penetration distributed photovoltaic
grid-connected system is studied, and the voltage, current, power, and other parameters of the
grid-connected system are obtained. A low-voltage and high-penetration distributed
photovoltaic grid-connected control method based on a genetic algorithm is adopted.
Experiments have shown that the maximum allowable capacity of this scheme is smaller than
the standard value and can effectively suppress voltage fluctuations. The experimental results
show that the system has good grid connection performance, which is of great significance for
improving the operational stability and efficiency of low-voltage distribution systems.
1. Introduction
The integration of high-penetration distributed photovoltaics increases the complexity and uncertainty
of the power grid, posing greater challenges to its stable operation [1]. At the same time, due to its
intermittent and fluctuating characteristics, the scheduling of the power system has become more
difficult. The power generation of photovoltaic power generation systems is greatly affected by the
lighting environment, and changes in the lighting environment are difficult to predict. Therefore, it
must be taken into account when operating the power grid [2]. This requires the power grid to be more
precise and flexible in scheduling and distributing electricity to adapt to changes in photovoltaic power
sources. To address these challenges, research on low-voltage and high-penetration distributed
photovoltaic access control technology has emerged. Scholars in related fields have researched low-
voltage and high-penetration distributed photovoltaic access control technology.
In [3], a maximum power point tracking control algorithm suitable for photovoltaic power
generation systems is established, utilizing adaptive backstepping neural networks to achieve
maximum output of photovoltaic modules. In [4], a distribution network scheduling method that
combines new energy vehicles with solar cells is studied. On this basis, a distributed robust
optimization model based on constraint generation is established, which comprehensively considers
the worst-case probability distribution, available energy of electric vehicles, arrival and departure time
of vehicles, and vehicle capabilities. Although the above methods have certain practical value, their
2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
2
voltage control effect is not good for low-voltage and high-penetration distributed photovoltaic power
generation systems connected to the distribution network.
To address the above issues, a low-voltage and high-penetration distributed photovoltaic access
control technology based on intelligent measurement is proposed.
2. Processing of measurement data for low-voltage distribution networks
Due to differences in the timing of data collection by load terminals for low-voltage distribution
networks, real-time measurement data is considered quasi real-time measurement data, and there are
also certain differences in the collection time of this measurement data, which will affect the real-time
performance of the measurement data to a certain extent [5-6]. For real-time measurement data, we use
the difference algorithm to process the measurement data collected by each measurement terminal, so
that they are all collected at the same time to form real-time data. The ratio between this result and the
daily electricity consumption mentioned above is called the power coefficient, which can describe the
load state representing the entire day. The calculation formula is:
1
1
P
PN
i
P
i
Q
QN
i
Q
i
A
K
AN
A
K
AN
<
<
<
<
(1)
where
P
K
represents active power;
Q
K
represents reactive power; Both
P
A
and
Q
A
represent
electricity, with the former corresponding to active power and the latter corresponding to reactive
power; Both
i
P
A
and
i
Q
A
represent electricity consumption, with the former corresponding to active
power and the latter corresponding to reactive power, representing the
i
day of the month in which the
day is located;
N
represents the number of days, corresponding to the month in which the day is
located [7].
3. Access control strategy for low-voltage and high-penetration distributed photovoltaics
3.1. Analysis of high-permeability distributed photovoltaic access distribution network structure
Inverters are one of the key components in the control process of distribution networks [8], and the
prerequisite for achieving control is to establish a mathematical model of the inverter and analyze its
characteristics [9]. The Parker transform method can transform information in different coordinate
systems and effectively simplify the structure and content of infor mation. Therefore, this study applies
the Parker transform method to process inverter output information, and the expression is:
a
d
b
e
c
a
d
b
e
c
u
u
Tu
u
u
i
i
Ti
i
i
<
<
(2)
where
a
u
,
b
u
,
c
u
and
a
i
,
b
i
,
c
i
represent the voltage a nd current of the inverter in a stationar y
coordinate system;
d
u
,
e
u
and
d
i
,
e
i
represent the voltage and current in the rotating coordinates after
Parker transformation;
T
represents the Parker transformation matrix;
ϖ
represents angular velocity;
2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
3
t
represents the period. Corresponding settings need to be made according to the specific situation of
the inverter circuit.
Under coordinate system transformation, the inverter power will also undergo corresponding
transformation, and the calculation formula is:
de ed
Quiui
< ),)
(3)
where
Q
refers to the reactive power of the inverter;
d
u
and
e
u
respectively represent the voltage
components of the direct and cross axes;
d
i
and
e
i
respectively represent the current components of
the straight and cross axes.
In practical applications, after Parker transformation, if the value of the AC axis voltage component
e
u
of the inverter is 0, the formula for calculating the inverter power can be simplified as follows:
de
Qui
<)
(4)
3.2. Low-voltage and high-penetration distributed photovoltaic access control strategy
The robust range of power load uncertainty after photovoltaic integration into the distribution network
is as follows [10]:
∋
(
,, ,, ,, ,,
PV PV PV PV PV
ist ist ist ist i
P dP
φφ
< ∗Χ (5)
∋
(
,, ,, ,, ,,
el el el el el
ist ist i st i st i
P dP
φφ
< ∗Χ (6)
where
,,
PV
ist
P
,
,,
PV
ist
φ
, and
,,
PV
ist
φ
Χ are the real active power of distributed photovoltaics connected to the
node, the lower limit of the output coefficient, and the difference between the upper and lower limits
of the output coefficient respectively during the period of Scenario
s
;
PV
i
P
is the distributed
photovoltaic capacity connected to Node
i
of the distribution network;
,,
el
ist
P
,
,,
el
ist
φ
, and
,,
el
ist
φ
Χ are
the real active power of base load in the scenario period, the lower limit of load factor, and the
difference between the upper and lower limits of load factor;
el
i
P
is the standard value of the basic
load demand for Node
i
;
,,
PV
ist
d
and
,,
el
ist
d
are auxiliary parameters of source load uncertainty.
We set
g
and
h
as the photovoltaic capacity and number of energy storage modules to be
connected to the selected node.
,
st
m
,
,
st
o
, and
,
st
q
are the distribution network flow variables,
electricity management demand vectors, and source load auxiliary vectors for Scenario
s
during the
t
periods. After fully considering the impact of power uncertainty factors on the distribution network,
the objective function of the maximum allowable capacity of the distribution network is obtained.
∋ ( ∋ (
,
,,
,,
' max min max
st
st st
T
qU
ho G gm
W fg
⊆
⊆ ⊆Ε
<
(7)
where
G
is the feasible domain of the energy storage sector and the demand vector for electricity
management in a certain source load scenario,
U
is the feasible domain of the auxiliary vector
representing source load uncertainty,
Ε
is the feasible domain of photovoltaic capacity, and
f
represents a column vector with elements equal to 1.
Under the improved genetic algorithm, we calculate the fitness value of the function. The error
function at this point can be expressed as:
2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
4
∋( ∋(
1
1
n
i
rr n
k
i
f i outi
E
H
<
<
,
<
(8)
where
rr
E
refers to an error function (When the error is less than the preset value, it indicates that the
function has obtained a local optimal solution);
∋
(
fi
refers to the input value of the model;
∋
(
out i
represents the model output value;
k
H
represents the normalization result. If the current result is only
a local optimal solution, we need to re-select random numbers and perform crossover and mutation
operations. In this way, it can generate new offspring and calculate their fitness values. Through this
approach, it can continuously optimize the solution and gradually approach the overall opt imal
solution. If the current solution is not the overall optimal solution, we need to return to the step of
calculating the fitness value of the function and select a random number again. We repeat the above
process until we find the overall optimal solution. When the optimal solution is obtained, the low-
voltage and high-permeability distributed photovoltaic access control results can be output.
4. Experimental analysis
4.1. Construction of low-voltage and high-penetration distributed photovoltaics
PV
PV
1 2 34 5 6 7 8 9 10 11 12
21 22 23
13 14 15
16 17 18 19 20
21 22
C1
C2
C3
C4
PV
PV
Figure 1. Node network structure.
To test the effectiveness of low-voltage and high-penetration distributed photovoltaic access
control technology based on intelligent measurement, the following experiments are designed. The
node network structure is shown in Figure 1, and there are a total of 23 nodes, with the active load of
the system being 2.578 MW and the total reactive load being 2.665 MW.
4.2. Experimental results
Under uniform load distribution, the photovoltaic power supply is distributed and connected to Nodes
3, 6, 8, and 22. Using the methods in [3] and [4] as comparison methods, the comparison results of the
maximum allowable capacity output are shown in Figure 2.
2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
5
Reference [3] Method
Maximuma llowable power/kW
100
200
300
400
36822
500
700
600
Node number
Reference [4] Method
Proposed method
Real maximum admission capacity
Figure 2.Calculation results of maximum admission capacity.
From Figure 2, it can observe the maximum acceptance capacity of each node, and the capacity
values between nodes in this method have a small difference from the actual maximum admission
capacity. This indicates that after the low-voltage high-penetration distributed photovoltaic access, the
access capacity value of this method has significantly increased, further confirming the superiority of
low-voltage and high-penetration distributed photovoltaic access in the control effect.
The maximum voltage fluctuation is used as the evaluation index for photovoltaic access control
results, and its calculation formula is:
∋
(
∋
(
ζ
|
max max max max min
UU
mm
V VV
<,
(9)
where
max
V
indicates the maximum voltage fluctuation of the fully distributed power grid;
U
m
V
represents the column vector of the voltage sample at Node
m
.
Based on actual cases, we set the rated active capacity S1=2.1 MVA and verified the maximum
voltage fluctuation control results of three methods in different scenarios after low-voltage and high-
penetration distributed photovoltaic access. Among them, Scenarios 1-3 are the first-end access,
Scenarios 4-6 are the middle-end access, and Scenarios 7-9 are the end-end access. The maximum
voltage fluctuation control results are shown in Figure 3.
Scene
0
Maximum voltage f luctuation/V
4
3
1
2
5
12345 6 7 8 9
Referenc e [3] Method
Referenc e [4] MethodProposed m ethod
Figure 3.Control results of maximum voltage fluctuation.
As shown in Figure 3, when testing the maximum voltage fluctuations of the four algorithms, the
maximum voltage fluctuations of our method under the condition of first-end connection are 1.7 V, 1.3
V, and 1.5 V, respectively. Under the condition of middle-end connection, the maximum voltage
fluctuations are 0.7 V, 0.6 V, and 0.6 V, respectively. Under the condition of terminal connection, the
maximum voltage fluctuations are 1.5 V, 1.3 V, and 1.4 V, respectively. Compared to the other three
comparative algorithms, under the same conditions, the maximum voltage fluctuation of the method
described in the article is the smallest, indicating that it is relatively stable. The low-voltage and high-
2023 3rd International Conference on Detection Technology and Intelligence System
IOP Conf. Series: Journal of Physics: Conf. Series 2770 (2024) 012029
IOP Publishing
doi:10.1088/1742-6596/2770/1/012029
6
penetration distributed photovoltaic access control technology based on intelligent measurement
designed in this article has better performance.
5. Conclusion
To address the energy crisis and environmental issues, and promote the development of renewable
energy, a low-voltage and high-penetration distributed photovoltaic access control technology based
on intelligent measurement is proposed. This project proposes a safe and reliable operation of the
power grid based on intelligent detection and advanced control strategies, improving the power quality
of the power grid, and promoting energy structure optimization and new energy development.
Experiments have shown that this scheme can effectively suppress the maximum allowable capacity of
the power grid, reduce voltage fluctuations, and improve the stability of power grid operation.
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