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Research on the Internal Flow
Characteristics of Pump Turbines for
Smoothing the Output Fluctuation of
the Wind–Photovoltaic
Complementary System
Yan Ren
1
,
2
*, Ruoyu Qiao
1
, Daohong Wei
1
* and Shangchen Hou
1
1
School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou, China,
2
Yellow River
Engineering Consulting Co., Ltd., Zhengzhou, China
Wind and photovoltaic (PV) power generation and other distributed energy sources are
developing rapidly. But due to the influence of the environment and climate, the output is very
unstable, which affects the power quality and power system stability. Pumped hydroelectric
energystorage(PHES)systemsaresuitableaspeakingpowersourcesforwindand
photovoltaic (Wind–PV) complementary systems because of their fast start–stop and long
life. The mathematical models and operational characteristics of the three subsystems in the
wind–PV–PHES complementary system are analyzed to improve the generation efficiency and
access capacity of wind and PV power. The peaking characteristics of the PHES system are
used to balance the maximum benefitandminimumoutputfluctuation of the wind–PV
complementary system. The stable operation of the pump turbine is an important guarantee
for the smooth output of the wind–PV complementary system. Three operating points are
selected from the net load curve and converted to the pump turbine model parameters. The
internal flow characteristics and laws of the pump turbine under different guide vane opening
conditions are summarized through the analysis of the computational fluid dynamics (CFD)
numerical simulation post-processing results. The study shows that the output of wind and PV
power generation varies with the changes in wind speed and solar radiation, respectively. The
output of the wind–PV complementary system still has large fluctuations, and the PHES
system can effectively suppress the power fluctuation of the wind–PV complementary system
and reduce the abandoned wind and light rate. CFD technology can accurately and efficiently
characterize the internal flow characteristics of the pump turbine, which provides a basis for the
design, optimization, and transformation of the pump turbine.
Keywords: wind–PV complementary system, output characteristics, CFD numerical simulation, pump turbine, flow
characteristics
Edited by:
Xiaoshun Zhang,
Northeastern University, China
Reviewed by:
Wenlong Fu,
China Three Gorges University, China
Lefeng Cheng,
Guangzhou University, China
*Correspondence:
Yan Ren
renyan@ncwu.edu.cn
Daohong Wei
hssgjl@163.com
Specialty section:
This article was submitted to
Sustainable Energy Systems and
Policies,
a section of the journal
Frontiers in Energy Research
Received: 07 April 2022
Accepted: 10 May 2022
Published: 21 June 2022
Citation:
Ren Y, Qiao R, Wei D and Hou S (2022)
Research on the Internal Flow
Characteristics of Pump Turbines for
Smoothing the Output Fluctuation of
the Wind–Photovoltaic
Complementary System.
Front. Energy Res. 10:914680.
doi: 10.3389/fenrg.2022.914680
Abbreviations: CFD, computational fluid dynamics; PV, photovoltaic; PHES, pumped hydroelectric energy storage; SD, sunny
day; CD, cloudy day; RD, rainy day; SWD, strong windy day; WWD, weak windy day; NWD, normal windy day. Symbols: P,
output; PT, prototype output; P11, model unit output; Q, flow rate; n, rotational speed; nr, rated rotational speed; n11, unit
rotational speed; ω, angular velocity; Vin, inlet velocity of volute; a0, guide vane opening; D1, diameter of the inlet side of the
runner; D2, diameter of the outlet side of the runner; D3, diameter of the inlet side of the volute; b0, height of vane; Z, number of
runner blades; ZS, number of stay vane; ZG, number of guide vane.
Frontiers in Energy Research | www.frontiersin.org June 2022 | Volume 10 | Article 9146801
ORIGINAL RESEARCH
published: 21 June 2022
doi: 10.3389/fenrg.2022.914680
1 INTRODUCTION
Currently, the world’s energy landscape is undergoing profound
changes; in this context, countries around the world are
scrambling to find ways to transform energy, and actively
develop clean energy sources such as solar energy, hydro
energy, nuclear energy, wind energy, and tidal energy to
gradually replace the fossil energy. However, with rapid
economic development, energy demand is increasing,
environmental issues are becoming increasingly prominent,
and the traditional energy structure is difficult to support
sustainable development. Therefore, it is necessary to take a
green and low-carbon path to ensure energy security. With the
development and utilization of wind power (Dowds et al., 2015),
PV power generation can greatly alleviate the problem of
electricity tension and energy depletion.
Wind power (Zhang and Qi, 2011) and PV power (Zhang
et al., 2021) generation has the advantages of good environmental
benefits, and clean and renewable energy. At the same time, wind
power and PV power generation is volatile, random (Abadi and
El-Saadany, 2010), and intermittent (Ren et al., 2017). In
addition, it is difficult to store them, which results in high
operation and maintenance costs. Electric energy can be
converted into many other forms of energy for storage
(Koohi-Fayegh and Rosen, 2020), such as kinetic energy in the
impeller, electric field in the capacitor, gravitational potential
energy in the reservoir, electrochemical energy, and chemical
energy in the fuel cell. Barra et al. (2021) discussed and compared
energy storage technologies and provided a comprehensive
review of important research on wind power smoothing using
these storage systems. Wu et al. (2014) and Acu ñ a et al. (2018)
connected the battery storage device to the wind–PV power
system and studied the optimal scheduling and capacity
allocation of the wind–PV–battery system to maximize the use
of renewable energy. Conventional battery storage cannot be used
in large-scale practical scenarios due to cost, environmental
requirements, safety, and lifetime limitations. Pumped storage
energy plants can be used as storage power sources because of
their stability and large capacity as physical energy storage.
Considering the intermittent nature of solar and wind energy
and the variation in load demand, Kusakana (2016) created an
energy dispatch model to meet the load demand; the main
objective of the model was to provide a multi-energy
complementary system composed of PV, wind (Parastegari
et al., 2015), PHES, and diesel generators. The main goal was
to reduce the operating costs of the system within the operating
limits of the different components and optimize the stability of
the system. When designing a multi-energy complementary
system, cost and reliability are prerequisites and foundations,
while also considering the demand-side response. Xu et al. (2020)
designed a PV–wind–PHES complementary system from an
investor’s perspective using various algorithms to find the
optimal configuration with maximum power supply reliability
and minimum investment cost. Jurasz (2017) combined a mixed-
integer model with an artificial neural network prediction method
to predict the amount of energy flow between a local balancing
area using a PV–wind–PHES complementary system and the
national power system. Rathore and Patidar (2019) evaluated the
reliability of a PHES system under a wind–PV–PHES
complementary system based on metrics such as load
expectation and expected energy not served. After comparison,
the PHES system was found to be more reliable and
environmentally friendly than battery storage systems. PHES
systems are being widely used to overcome the economic and
environmental disadvantages of electrochemical energy storage
devices.
The PHES (Gao et al., 2018;Wu et al., 2020) technology is
mature, with the advantages of flexible start-up, fast climbing and
unloading, peak and valley regulation, frequency regulation, etc.It
can well mitigate the adverse effects of distributed energy sources
such as PV power generation and wind power generation on the
power system and maintain the stable operation of the power
system. Archimedes pump (Waters and Aggidis, 2015) is one of
the oldest engineering masterpieces, with a history of more than
2000 years, and is still in use today. Since the last century, it has
been significantly developed in modern engineering and can be
reversed to be used as a turbine, known as a pump turbine
(Bogdanovic-Jovanovic et al., 2014;Li et al., 2019) used to explore
the internal flow pattern and equipment performance of the
pump turbine in a cost-effective manner. Reversible pump
turbines are widely used in the power market because they can
switch between pump and turbine operation in a matter of
minutes. The operating characteristics of a pump turbine are
related to the geometry of the flow path. Olimstad et al. (2012)
composed several scenarios for computational fluid dynamics
(CFD) numerical simulation by varying the angle and radius of
curvature of the blade inlet, and found that the unstable pump
turbine characteristics are the result of vortex formation in the
runner and guide vane channels, with leading edges having a
longer radius of curvature that is more prone to vortex formation.
The stability of the pump turbine (Zuo and Liu, 2017) is very
important for the operation of the PHES system, and unstable
characteristics can lead to hazards for the operation of the unit.
Widmer et al. (2011) used a combination of CFD numerical
simulations and test bench measurements to analyze the
mechanisms of vortex formation and rotational stall caused by
the turbine model of the pump turbine. Barrio et al. (2011)
performed a series of complementary experimental
measurements from the test bench to obtain the general
characteristics of the pump condition and turbine condition to
verify the correctness of the numerical simulation results. The
post-processing results were used to investigate the flow regime at
some important locations by utilizing pressure and velocity
contours and vector plots.
In summary, the current research on wind–PV–PHES
complementary systems (Dianellou et al., 2021) only involves
capacity allocation (Xu et al., 2019) and scheduling optimization
(Zare Oskouei and Sadeghi Yazdankhah, 2015). Most of the
studies on the characteristics of pumped turbines have been
carried out in the context of traditional PHES technology. In
this article, the research is divided into two parts: one is to study
the output characteristics of the complementary system and its
two subsystems with a given capacity configuration and the other
is to conduct CFD numerical simulations of the typical working
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Ren et al. Pump Turbines and Wind–PV System
conditions of the pump turbine in the complementary system
with different guide vane openings to investigate its flow
characteristics. The main contributions are as follows:
1) analyze operational characteristics of wind and PV power
systems of defined capacity;
2) analyze the wind–PV complementary system (Sinha and
Chandel, 2015) and the operating characteristics of the
wind–PV–PHES complementary system after adding
typical loads to obtain the net load curve; and
3) select three prototype operating points from the net load curve
and convert them to the relevant parameters of the model
pump turbine. The CFD numerical simulation is carried out to
analyze the flow characteristics inside the pump turbine under
the wind–PV–PHES complementary system.
The remaining sections of this article are organized as follows.
In Chapter 2, a three-dimensional model of the pump turbine is
established, and the meshing and irrelevance are verified. The
output mathematical models of wind power, PV power, and
PHES systems are established. In Chapter 3, the output
characteristics of the aforementioned systems are analyzed,
and on this basis, the operating characteristics of the wind–PV
system and the wind–PV–PHES system with three typical loads
are analyzed and the net load curves are drawn. In Chapter 4,
three prototype operating points are selected from the net load
curves in Chapter 3 and reflected from the comprehensive
characteristic curves of the pump turbine model after
conversion. CFD numerical simulations are performed for the
three selected operating points with different guide vane
openings. The laws and characteristics of the internal flow
state of the pump turbine are summarized after analysis of the
post-processing results. Chapter 5 is a summary of the research
findings. The workflow of this article is shown in Figure 1.
2 METHODS AND MATHEMATICAL
MODELS
2.1 Methods
2.1.1 CFD Numerical Simulation Method
CFD technology can be understood as a numerical simulation
method of flow controlled by the fundamental flow equations
(conservation of mass, conservation of momentum, and
conservation of energy). The distribution of basic physical
quantities, such as velocity, pressure, temperature, and
concentration, at different locations in the flow field and the
variation in these physical quantities with time can be obtained by
numerical simulation. Then the vortex distribution
characteristics, cavitation characteristics, and delocalization
zone can be observed. Therefore, CFD can be used not only
for flow field analysis but also for further study of the fluid flow
mechanism inside the pump turbine (Tao and Wang, 2021) and
the transition process (Wang et al., 2011). At the same time, it can
be used as a tool to calculate the external performance parameters
such as the head and shaft power of the pump turbine. In
addition, combined with CAD software, it can also optimize
the design of the structure.
However, the flow characteristics inside most hydraulic
machines are difficult to be studied by building test benches
due to the limitations of funding, space, accuracy, and time and
labor costs. Therefore, the use of CFD numerical simulation to
study fluid problems is a proven method. However, the
traditional calculation method has many drawbacks: it requires
FIGURE 1 | Workflow diagram.
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Ren et al. Pump Turbines and Wind–PV System
a lot of mathematical derivation, and the solution process is
complicated and time-consuming. Therefore, it is difficult to meet
the requirements of engineering practice. The aforementioned
disadvantages of the CFD numerical simulation method can be
overcome by exploring the internal flow characteristics of the
hydraulic machinery with the help of computer resources. For
example, the CFD numerical simulation method can be used to
understand in detail the flow in the pump turbine pipeline, the
formation and propagation of vortices, pressure distribution, flow
velocity distribution, force magnitude, and its change with time.
2.1.2 Flow Control Equation
The flow in a pump turbine, regardless of the flow rate, is always
governed by the basic physical conservation laws. The
corresponding control equations are as follows.
Continuous Equation:
zρ
zt+zρui
zxi
0(1)
where uiis the fluid velocity component in the direction of
coordinate xi,ρis the bulk density, and tis the time.
Momentum Equation:
zρui
zt+
zρuiuj
zxj
−
zp
zxi
+zτij
zxj
+Smi (2)
where pis the static pressure, Smi is the generalized source term of
the momentum equation, τij is the stress tensor, and ujis the fluid
velocity component in the direction of coordinate xj.
Energy Equation:
zρT
zt+zTρui
ztλ
cp
z2T
zt+ST(3)
where Tis the temperature, uis the velocity, STis the viscous
dissipation term, and cpis the specific heat capacity.
2.1.3 Establishment of the Three-Dimensional Model
of the Pump Turbine
The pump turbine of a PHES system is chosen as the object of
calculation, and the main basic parameters are shown in Table 1.
The overflow components such as the stay vane, guide vane,
volute, runner, and draft tube are modeled in the fluid domain
using Unigraphics NX software, and then assembled as shown in
Figure 2.
Since the modeling idea of the full flow channel 3D hydraulic
model of the pump turbine is to build each overflow component
separately and then assemble it, the internal flow field between
two adjacent components cannot be connected when using
Fluent for CFD numerical simulation. Therefore, to realize the
connection of the whole flow field, the common practice is to
create interface surfaces between the components. The Shared
Topology function in ANSYS SpaceClaim can automatically
identify the contacting or intersecting bodies and share their
topology, replacing the complicated setting of interface surfaces.
The schematic diagram is shown in Figure 3.
2.1.4 Pump Turbine Meshing and Mesh Irrelevance
Verification
To ensure the solution accuracy and time, and to take into
account the computer resources, Poly-Hexcore mesh, which
consists of laminated polyhedra mesh, pure polyhedra mesh,
TABLE 1 | Basic parameters of the pump turbine of a pumped storage power
station.
Parameter Parameter value
n(rpm) 250
n
r
(rpm) 1,500
Z9
Z
S
20
Z
G
20
D
1
(mm) 300
D
2
(mm) 441.9
D
3
(mm) 315.1
b
0
(mm) 66.7
FIGURE 2 | Full flow channel three-dimensional hydraulic model of the
pump turbine.
FIGURE 3 | Shared topology schematic.
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Ren et al. Pump Turbines and Wind–PV System
and hexahedra mesh (Hexcore), is used for meshing the pump
turbine 3D model.
The grid-independence test requires that the sparsity of the
grid has as little effect as possible on the results of CFD numerical
simulations. But the number of grids can be increased to improve
the computational accuracy under the condition that sufficient
computational resources are available. The former is widely used
because of its high efficiency, while the latter is difficult to be
widely used because of its inherent large errors and difficulties.
Since the grid has many influencing factors besides model
accuracy, such as model boundary conditions and residual
criteria, increasing the number of grids may not necessarily
improve the accuracy of the calculation. But if the number of
grids is so small that it cannot represent the complete details of
the model, it will be impossible to obtain more accurate
calculation results.
In general, the larger the number of grids, the higher the
computational accuracy, and the larger the number of grids, the
greater the pressure on the computational resources. Therefore,
while ensuring computational accuracy, the pressure on
computational resources should be taken into account. For the
verification of the grid-independence under optimal operating
conditions in the hydraulic turbine mode of the pump turbine,
five groups of different numbers of grids are divided in the range
of 1–4.5 million for numerical calculation. The relative errors of
the head and efficiency calculated by numerical calculation and
the experimental values are shown in Figure 4. As can be seen
from Figure 4, the two curves represent the relative errors
between the numerical simulation results and the experimental
values for different grid number scenarios for the head and
efficiency, respectively. The relative error reaches the
minimum when the number of grid cells is 2,797,995, and the
error is maintained within 2% to meet the calculation
requirements, after which the relative error increases slightly
again with the increase in the number of grid cells.
Taking into account the computational resources and
computational accuracy, the final number of grid cells is
determined to be 2,797,995. The number of grid cells of each
component is shown in Table 2,andtheschematicdiagramof
grid division is shown in Figure 5. As can be seen from Figure 5,the
surface of the mesh is roughly honeycomb-shaped, and the interior is
filled with positive hexahedra. The regularity of each cell is high, and
the connectivity between cells is excellent. There will be different
degrees of encryption in the transition area between the lower
curvature, complex structure, and adjacent two parts.
2.2 Mathematical Models
The wind–PV–PHES complementary system is an important
type of multi-energy complementary power generation system
that can well integrate distributed energy sources, reduce the
abandoned wind and light rate, and improve power quality. The
wind–PV–PHES complementary system consists of three
subsystems: PHES system, wind, and PV power system.
2.2.1 Wind Power Generation
For wind turbines, their output is strongly related to the local
wind speed; especially the wind speed at the height of the rotor
determines the output. But since the wind speed at the height of
the rotor is difficult to measure, the wind speed at the ground level
needs to be converted according to the empirical formula.
V
V0H
H0α
(4)
where Vis the wind speed at the height of H(m), m/s; V0is the
wind speed at the height of H0(m), m/s (usually taken as a
reference height of 10 m); and αis the friction factor, also known
as the wind speed conversion factor and is related to the ground
topography.
For a specific wind turbine, the power curve shows the
relationship between the wind speed and the generator output.
Figure 6 shows the power curve of a typical wind turbine with a
quadratic approximation. The corresponding output expression
is given by the following equation.
P
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
0Vw≤Vc
PR
V2
w−V2
c
V2
R−V2
c
Vc<Vw≤VR
PRVR<Vw≤VF
0VF<Vw
⎫
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎭
(5)
where PRis the rated power of the wind turbine, kW; VRis the
rated speed of the wind turbine, m/s; Vwis the cut-in speed of the
wind turbine, m/s; and VFis the cut-out speed of the wind
turbine, m/s.
Vwis the minimum speed required to produce power. VRis
the speed at which the generator delivers its design power. If the
wind speed exceeds the rating, an active pitch control system, a
passive stall control design, or a combination of both must be
used to throw off some of the wind resistance or risk damaging
the generator. Wind speed above the VFcan potentially damage
the generator.
FIGURE 4 | Schematic diagram of grid independence verification.
Frontiers in Energy Research | www.frontiersin.org June 2022 | Volume 10 | Article 9146805
Ren et al. Pump Turbines and Wind–PV System
2.2.2 Mathematical Model of PV Power Generation
Solar radiation and ambient temperature will have a direct impact
on the output of PV power generation, and its output
mathematical model is shown as follows:
PPV(t)APV G(t)ηPV(t)ηinv (6)
where APV is the area of the PV panel exposed to solar radiation,
m
2
.G(t)is the solar radiation value, W/m
2
.ηPV is the energy
conversion efficiency of the PV module. ηinv is the conversion
efficiency of the inverter. ηPV (t) is influenced by the ambient
temperature.
ηPV(t)ηref 1−βTC(t)−TCrf (7)
where ηref is the reference energy conversion efficiency of the PV
module at standard temperature. βis the coefficient of
temperature effect on the energy conversion efficiency. TC(t)
is the temperature of the PV module at time t. TCis the reference
standard temperature of the PV module.
Under the influence of solar radiation absorption and ambient
temperature, the temperature of the PV module changes as
follows:
TC(t)−Tambient Trated
800 G(t)(8)
where Tambient is the ambient temperature and Trated is the rated
temperature of the PV module.
2.2.3 Mathematical Model of the Pumped Storage Unit
The PHES system mainly consists of an upper reservoir, lower
reservoir, transmission pipeline, and plant. The reversible pump
turbine is the core component of the PHES system, which realizes
pumping and power generation functions through working
condition conversion.
When the pump turbine is in stable operation, for an ideal
viscosity-free fluid, the work carried out by the water flow on the
runner is as follows:
NρgQH (9)
where Nis the output of the pump turbine, kW. Qis the flow rate
of the pump turbine, m
3
/s. His the head of the water pump
turbine, m.
Under the turbine model, the output transmitted from the
shaft end of the main shaft to the generator is called the output of
the turbine.
TABLE 2 | Number of grid cells for each component.
Component Volute Stay vane Guide vane Runner Draft tube
Number of grid cell 718,948 476,279 453,344 747,143 402,281
FIGURE 5 | Schematic diagram of grid division.
FIGURE 6 | Power curve of the wind turbine.
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Ren et al. Pump Turbines and Wind–PV System
PTρgQTHTηT(10)
where ηTis the efficiency of the pump turbine under the
turbine model.
Under the pump model, the energy of the water flow through
the pump per unit time is called the effective power.
PeρgQPHP(11)
Then the power transmitted by the motor to the shaft end of
the pump, that is, the shaft power, is as follows:
PPρgQPHPηP(12)
where ηPis the efficiency under the pump model.
3 RESULT AND DISCUSSION
3.1 Characterization
PV and wind power have some complementary characteristics.
When the sunshine is abundant during the day, the PV generator
sets have a high output and generation, while the wind generator
sets have a relatively low power generation due to the reduction of
wind speed on sunny days. When there is no light at night, there is
no PV power output and PV power generation is 0, while wind
power usually has a high wind speed at night, and the output
increases and the power generation increases. At this time, the
PHES system can be operated under pumping or power
generation conditions according to the size of the load in
the system and the real-time output of the wind–PV
complementary system, so as to play the role of peak and
valley regulation and suppress the fluctuation of output in the
whole system, and then improve the power quality and the
energy utilization rate.
The PHES system runs pumping conditions when the grid
load is low to pump water to the reservoir, and then converts
under turbine condition when the grid load is high to generate
electricity to meet the demand of the grid. It can play a multi-role
function of source, load, and storage in ensuring the balance of
supply and demand of the grid operation, with the characteristics
of fast start-up, fast response, and outstanding peak and valley
regulation. This can effectively improve the intermittent,
fluctuating, and random nature of wind and PV power
generation, maintain the balance of supply and demand in the
grid, and enhance the resilience of power security.
The installed capacity of each subsystem in this article is set as
follows: 300 MW for the PHES subsystem with two units,
600 MW for the PV power subsystem, and 800 MW for the
wind power subsystem, which is based on the operational
characteristics of each subsystem and the principle of not
wasting installed resources. Taking into account the actual
power generation characteristics of wind power and PV
power, the wind, PV, and PHES subsystems and the power
load in a 24-h cycle are studied, and the power generation
characteristics of the subsystems themselves, the wind–PV
complementary system, and the multi-energy complementary
systems after adding the PHES system are analyzed.
3.1.1 Analysis of Wind Power System Characteristics
The fundamental reason for the large fluctuation of wind power is
the natural intermittency and random fluctuation of wind energy
resources. The wind speed is affected by a variety of complex
natural factors such as climate, weather, and terrain. The wind
power system output characteristics of a typical year are shown in
Figure 7. It can be seen from the figure that the wind power
system output is random and irregular during 8,760 h. It is known
that the total installed capacity of wind power is 800 MW. In this
typical year, the power generation hours with an output range of
0–300 MW, 300–600 MW, and 600–800 MW are 6781, 1069, and
910 h, respectively, accounting for 77.4, 12.2, and 10.4% of
8,760 h in a year after statistics.
From the wind power data of this typical year, three typical
days of a strong windy day (SWD), normal windy day (NWD),
and weak windy day (WWD) were selected. Their output and
wind speed values are listed in Supplementary Table S1, and the
output and wind speed distribution of each typical day are plotted
as shown in Figure 8.
From the wind power output and wind speed distribution on
SWD shown in Figure 8A, it can be seen that wind speed and
output fluctuate greatly during the day. The output of wind power
on SWD peaks at 21:00 with 672.37 MW, accounting for 84.05%
of the installed capacity of the system, and the trough of output is
34.9 MW around 12:00, accounting for 4.36% of the installed
capacity of the system. The overall wind speed from 0:00 to 24:00
shows a trend of decreasing first and then increasing. The wind
speed fluctuates with time, the minimum wind speed is 3.77 m/s
at noon, the wind speed is larger at night, and the output changes
with the change in wind speed.
As shown in Figure 8B, the wind power output and wind
speed distribution on NWD are similar to those on SWD but
fluctuate slightly less than those on SWD. The wind power output
peaks at 466.59 MW around 22:00, accounting for 58.32% of the
FIGURE 7 | Power output characteristics of a typical year wind power
system.
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Ren et al. Pump Turbines and Wind–PV System
installed capacity of the system and 69.39% of the peak output on
SWD. The overall wind speed from 0:00 to 24:00 shows a trend of
decreasing first and then increasing. The wind speed fluctuates
with time, the minimum wind speed is 3.40 m/s at 11:00, the wind
speed is larger at night, and the output changes with the change in
wind speed.
From Figure 8C, the wind power output and wind speed
distribution on WWD, it can be seen that the trend of wind power
output and wind speed is consistent with the other two typical
days as a whole. It fluctuates the most among the three typical
days and decreases sharply at 21:00–23:00. The overall output
value is smaller in a day, the wind power output on SWD peaks at
around 00:00 at 241.96 MW, accounting for 30.25% of the system
installed capacity and 35.99% of the peak output on SWD. The
trough of output is 6.53 MW around 13:00, which is 0.8% of the
installed capacity of the system and 18.7% of the trough on SWD.
The wind speed is small and fluctuates widely throughout the day,
and the minimum wind speed is 0.79 m/s at 11:00.
We analyzed the wind power system output characteristics on
three typical days and found that the size of the output varies with
the wind speed. The wind speed is low at noon because the
sunlight is sufficient at noon, so the output of all three typical days
reaches a low point at noon. The wind speed is higher at night, so
the output is also higher.
3.1.2 Analysis of PV Power System Characteristics
The difference between PV power and wind power is that it is
intermittent and regular. The output is somewhat random and
fluctuates due to the influence of weather, season, and sunlight.
The PV power system output characteristics of a typical year is
shown in Figure 9. From the figure, it can be seen that the PV
power system intermittents are greater at 8,760 h. Electricity is
generated only in the daytime when there is light; at night when
there is no light radiation, no electricity is generated. The output
in winter is higher, although the light is longer and irradiance is
higher in summer. But the temperature is also high, and high
temperature has a significant negative impact on the output of the
PV power system, making the overall output in summer lower.
The total installed capacity of the PV power system is known to be
600 MW. In this typical year, the number of power generation
hours with the output range of 0–200 MW is 6725 h, which is the
highest in the year, accounting for 76.8% of the year. The number
of power generation hours with the output range of 400–600 MW
is 375 h, which is the lowest in the year, accounting for 4.2% of
the year.
The output and solar irradiation intensity values are listed in
Supplementary Table 2 for the three typical days of sunny day
(SD), cloudy day (CD), and rainy day (RD) selected from the
typical annual PV power generation data. The output and solar
irradiation intensity distribution of each typical day are plotted as
shown in Figure 10.
From Figure 10A, the distribution of PV power output and
solar irradiation intensity on SD, it can be seen that the PV power
system is mainly affected by solar radiation. The solar irradiation
and output are approximately normally distributed on SD. As
time goes by, the system power gradually increases, reaching a
FIGURE 8 | Typical daily output and wind speed distribution. (A) SWD,
(B) NWD, and (C) WWD.
FIGURE 9 | Output characteristics of a typical year PV power system.
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Ren et al. Pump Turbines and Wind–PV System
peak at noon and decreasing from afternoon until the sun sets.
The solar irradiation appears at around 8:00 and disappears at
around 20:00. The solar irradiation reaches a peak of 973 W/m
2
at
14:00, and the output value reaches a peak of 496.23 MW at 14:00,
accounting for 82.71% of the installed capacity of the system.
From Figure 10B, the distribution of PV power output and
solar irradiation intensity on CD, it can be seen that the solar
irradiance curve of the PV power system fluctuates more
drastically. This leads to higher output volatility and lower
output throughout the day, with a large gap between each
hour. The solar irradiation appears at around 7:00 and
disappears at around 20:00. The solar irradiation reaches a
peak of 517 W/m
2
at 17:00, and the output value reaches a
peak of 415.5 MW at around 17:00, accounting for 69.25% of the
installed capacity of the system. The discontinuities are more
obvious, with no similar characteristics or clear rules.
From Figure 10C, the distribution of PV power output and
solar irradiation intensity on RD, it can be seen that the solar
irradiance curve of the PV power system fluctuates more
drastically. The output has the characteristics of small output
and great fluctuation. The output of the whole day is obviously
reduced and is the lowest among the three typical days. The solar
irradiance appears at around 7:00 and disappears at around 20:00.
The solar irradiation reaches a peak of 250 W/m
2
at 11:00, and the
output value reaches a peak of 211.25 MW at around 11:00,
accounting for 35.21% of the installed capacity of the system.
This section analyzes the output characteristics of the PV
power system on three typical days and finds that the output
varies with solar irradiance. The output of the PV power system is
cyclical 24 h a day, and the output on SD is approximately
normally distributed, with a relatively smooth curve. However,
the output on RD and CD fluctuates greatly, showing multi-peak
characteristics, and there is no specific rule to follow.
3.1.3 Analysis of PHES System Characteristics
Due to the randomness and instability of wind and PV power
systems, the PHES system plays the role of peak and valley
regulation in the wind–PV complementary system. By working
reciprocally to balance the output of wind–PV complementary
systems and reduce the rate of wind and light abandonment
through the conversion of pumping and power generation
conditions. The daily operation diagram of a PHES system for
regulating the output of the wind–PV complementary system is
shown in Figure 11. From the figure, it can be seen that in the
complementary power system containing wind and PV
generating units, the net load in the system fluctuates greatly
(the size of the net load is the difference between the load in the
system and the output of the wind–PV system), and the output of
the PHES system changes with the fluctuation of the net load.
During 00:00–7:00, the local wind speed is high, and the wind
power system output is also high, while the power system is in the
low peak period, so the load is small. To reduce the abandoned
wind rate, the PHES system uses the surplus electricity in the
complementary system to pump the water to the upper reservoir
under the pump mode to store the potential energy for peak
regulation. During 9:00–18:00, with the significant increase in the
complementary system load, the PHES system will release the
water stored in the upper reservoir to the lower reservoir and
FIGURE 10 | Typical daily output and solar irradiation intensity
distribution. (A) SD, (B) CD, and (C) RD.
FIGURE 11 | Daily operation diagram of a pumped storage power
station for regulating the output of the wind–PV complementary system.
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Ren et al. Pump Turbines and Wind–PV System
generate electricity under the turbine mode to supply the load to
fill the valley.
3.1.4 Analysis of Wind–PV Complementary System
Characteristics
The output of the wind–PV power system has certain
complementary characteristics. So in order to reduce the rate
of wind and light abandonment and the fluctuation of system
output, the wind and PV power systems are coupled by using the
natural spatial and temporal complementary characteristics of
solar and wind power. The SWD, WWD, and NWD are
combined with SD, CD, and RD to form the nine typical days
1–9, and the output distribution of the wind–PV complementary
power system on the nine typical days is shown in Figure 12.
The fluctuation rule of PV and wind power output under each
weather type varies depending on the degree of influence of
different meteorological factors, but there is a certain degree of
similarity. Since the PV power generates electricity only during
the daytime and generally produces more power near noon, wind
power normally produces more power at night and reaches an
underestimation at noon. As can be seen from the figure, overall,
the volatility of the system output after wind and PV power
complementation is improved to different degrees in the
following nine typical days.
Under different weather conditions, the complementary
relationship between PV and wind power systems has obvious
differences. On typical “day 1”which combines SD with SWD,
and typical “day 6”which combines RD with NWD, the output of
the complementary system is significantly improved. The output
fluctuation is greatly reduced, the multi-peak characteristic
becomes less obvious, and the peak-to-valley difference is also
greatly reduced. While the output fluctuation of typical days of
most weather types is improved, the effect is not very obvious,
such as typical “day 8”which combines RD with WWD.
3.1.5 Analysis of Wind–PV–PHES Complementary
System Net Load Characteristics
The random fluctuation of solar and wind energy can have a
significant impact on the stability of power systems. Uncontrolled
absorption of large amounts of solar and wind energy may lead to
drastic power fluctuations in the entire power system over a
certain period. Therefore, it is very important to maximize the
consumption of PV and wind power without affecting the system,
taking into account the operational economics of PV and wind
FIGURE 12 | Distribution of nine kinds of typical daily wind–PV complementary output.
Frontiers in Energy Research | www.frontiersin.org June 2022 | Volume 10 | Article 91468010
Ren et al. Pump Turbines and Wind–PV System
power. An effective way to overcome this problem is to combine
the PHES system with intermittent renewable energy sources.
The reason is that the PHES system is a relatively mature form of
energy storage with low investment costs that can make full use of
excess energy and ensure smooth operation of the system.
Solar and wind energy have the characteristics of natural
space–time complementarity. The PHES system has the
characteristics of energy storage, peak regulation, and
frequency regulation, forming a wind–PV–PHES
complementary system to make the power system run
smoothly. If the PV and wind power generation cannot output
at full capacity, the excess solar and wind energy will be available
for pumping the stored gravitational potential energy of the lower
reservoir. If the PV and wind power generation cannot generate
enough power to meet the load requirements, water from the
upper reservoir will be used to drive the turbines to generate
power. The principle of complementary operation is that the
wind–PV complementary system operates at full load according
to the day-ahead power forecast, with fluctuations and intervals
in output regulated mainly by the PHES system. In other words,
while the PHES system is added to the system, the output
fluctuations of the complementary operation system will be
smoothed out as PV and wind power are fully utilized and the
operational efficiency is improved.
To improve the generation efficiency and access capacity of PV
and wind power, the energy storage function of the PHES system
is used to balance the maximum benefit and minimum output
fluctuation of the wind–PV–PHES complementary system. The
net load curve of the wind–PV–PHES complementary system
after adding the daily load of three typical systems is shown in
Figures 13–15.
As can be seen from the figure, the daily load of all three typical
systems fluctuates and is smaller at night and larger during the
day. The three typical daily load curves with different peaks and
trough periods are representative and can accurately and
comprehensively verify the regulation ability of the PHES
system. The net load is the difference between the sum of the
wind power generation and PV power generation system output
and the load of that period, which is the function of the PHES
system to adjust the peak and fill the valley of the power system.
When the value of a point in the net load curve is less than 0, it
means that the wind and PV power output is less than the load
value, so the PHES system converts to the turbine condition and
releases water to the lower reservoir to make up for the lack of
output and ensure the stability of the system load. When the value
of a point in the net load curve is greater than 0, it means that the
wind and PV power output is greater than the load value, so the
PHES system converts to the pump condition and pumps water
FIGURE 13 | Distribution of wind and PV power output and net load curve after adding load one.
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Ren et al. Pump Turbines and Wind–PV System
to the upper reservoir to store energy, reduce the rate of wind and
light abandonment, and improve energy utilization.
As shown in Figures 13–15, the maximum net load is
436.1 MW and the minimum value is -513.2 MW, which
meets the demand for peak regulation. The PHES system has
the functions of quick action, flexibility, fast climbing and
unloading, and peak and valley regulation. So they are capable
of improving the load fluctuation of the power system and
ensuring smooth operation.
3.2 Analysis of the Flow Characteristics of
the Pump Turbine to Smooth out the
Fluctuation of Wind and Solar Energy
Output
3.2.1 Selection of Working Condition Points
As shown in Figure 16, a point from each of the three typical
daily net load curves after adding load three is taken as the
prototype working condition point for CFD numerical
simulation. The output is converted to unit power output
P
11
by the formula and marked on the P
11
–n
11
curve of the
pump turbine model one by one. The P
11
–n
11
curve shows the
various main performances of the pump turbine by plotting
the iso-efficiency line, the iso-openness line, and the iso-
cavitation coefficient line with n
11
and P
11
as horizontal
and vertical coordinates. The main information and the
correspondence can be obtained by taking any point of the
curve. The three working condition points of the pump turbine
model under different openings are as shown in Figure 17,and
the conversion formula is as follows.
PmP11D2
1mH3
2
m(13)
PT
Pm
D1p
D1m2
·HT
Hm3
2
(14)
where Pmis the model output of the pump turbine, kW; PTis the
prototype output, kW; P11 is the unit output, kW; D1mis the
model runner diameter, 0.3 m; D1pis the prototype runner
diameter, 3.57 m; Hmis the model constant net head, 50 m;
and HTis the prototype constant net head, 177.4 m.
The relevant parameters of the three working condition points
after conversion are listed in Table 3.
CFD numerical simulations were performed for the
aforementioned three working condition points, and the same
pressure-based solver settings were used before performing the
calculations.
FIGURE 14 | Distribution of wind and PV power output and net load curve after adding load two.
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Ren et al. Pump Turbines and Wind–PV System
1) Adopting speed inlet.
2) Free outflow (Outflow) is used at the outlet.
3) Dynamic and static interference of runner (Song et al., 2020),
using the multi-reference system model (MRF).
4) Coupled calculation of velocity and pressure using the coupled
algorithm.
5) Turbulence model using the SST k-ωmodel and second-order
windward discretization.
The parameters required for the three working condition
points of the pump turbine before conducting CFD numerical
simulations are listed in Table 4.
3.2.2 Flow Characteristics Analysis
3.2.2.1 Water Guide Mechanism
Since both the volute and vane are designed to guide the water
flow smoothly into the runner and to form and change the
FIGURE 15 | Distribution of wind and PV power output and net load curve after adding load three.
FIGURE 16 | Prototype working points in the daily net load curve.
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Ren et al. Pump Turbines and Wind–PV System
velocity loop of the water flow, the volute and vane are analyzed
together. From the pressure distribution diagram of the volute
and vane in Figure 18A, it can be seen that the pressure change of
the water flow in the volute is basically the same along the
centripetal direction under the three working conditions, and
only the magnitude of the pressure value is different. The pressure
in the circumferential direction is distributed in a band from the
outside to the inside, and the pressure gradually decreases along
the center of the circle. The pressure size is in the order of the
volute, stay vane, and guide vane. Since the volute is segmented,
the fluid domain is not smooth at the weld seam, and because the
flow rate is larger at the opening of the guide vane a0 = 47.41 mm,
there is a slight sudden change in pressure at the weld seam of the
volute.
The pressure variation of the water flow between the walls of
the vane is approximately the same for the three operating
conditions, with a general tendency to decrease. The pressure
at the outer edge of the seat ring, the inlet of the stay vane, and the
guide vane is higher. Due to the high-speed flow of water, the
water flow in the tip and tail of the vane experiences an impact.
The pressure will suddenly reduce, at a0 = 16.1 mm more obvious.
At a0 =47.41 mm, the pressure at the back of the vane blade is
significantly smaller than that at the waterward side. Due to the
dynamic and static interference of the runner, the pressure
magnitude changes dramatically in the area from the guide
vane to the inlet section of the runner.
From the velocity vector diagrams of the volute and vane in
Figure 18B, it can be seen that, on the whole, the water flow in the
volute has an equiangular spiral shape in all three operating
conditions, which is consistent with the flow characteristics of the
water in the volute. The symmetry in the circumferential
direction is good, the water flow is uniform and smooth, and
the volute can well guide the water flow into the stay vane in an
axisymmetric manner.
The velocity distribution of the water flow in the vane is
relatively uniform, the flow is smooth, the symmetry along the
circumferential direction is good, and there is no vortex. After the
water flows out of the guide vane and before entering the runner,
it can be observed that the velocity direction of a0 = 16.1, 22.47,
and 47.41 mm in the three working conditions, and the angle
FIGURE 17 | Working condition points in the P
11
–n
11
curve.
TABLE 3 | Relevant parameters of working condition points.
Working condition a
0
(mm) P
T
(MW) P
11
(kW) n
11
(rpm)
Point 1 47.41 271.03 4 56
Point 2 16.1 120.46 5.85 50
Point 3 22.47 176.17 9 50
TABLE 4 | Pump turbine pre-treatment parameters.
Parameter a
0
(mm) n
11
(rpm) n(rpm) Q(m
3
/s) V
in
(m/s)
Point 1 47.41 56 1,355.1 0.71 9.14
Point 2 16.1 56 1,355.1 0.3 3.86
Point 3 22.47 50 1,209.9 0.43 5.53
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Ren et al. Pump Turbines and Wind–PV System
between the guide vane increases in order, and it is close to
normal at a0 = 47.41 mm.
3.2.2.2 Runner
From the pressure distribution diagram of the runner in
Figure 19A, it can be seen that the pressure distribution of
the runner in the three working conditions is relatively
uniform and has a certain similar distribution law. The
pressure change in the wall of the runner is distributed in a
band, and it is symmetrical and decreases uniformly along the
circumferential direction, but the pressure magnitude is different.
The pressure from the inlet side to the outlet side of the runner
decreases gradually. There are different degrees of negative
pressure at the outlet and the lower ring of the runner, and
the negative pressure zone at a0 = 22.47 mm is relatively large;
therefore, the possibility of cavitation is also larger. Since there are
narrow gaps between the tip of the runner blade and the edge of
the upper crown and lower ring, high-pressure areas will be
generated at these gaps when the runner rotates at high speed,
which is also the area where cavitation is likely to occur.
The pressure distribution diagram of the runner blades in
Figure 19B shows that the pressure distribution law of the blades
in the three working conditions is similar. The pressure decreases
gradually along the direction of the water flow from the inlet side
to the outlet side of the blades, and the pressure distribution of the
nine runner blades has symmetry along the circumferential
direction, the overall force of the runner is great, and the flow
is smooth.
The overall pressure on the waterward side of the blade is
greater than that on the backwater side, so the blade forms a
certain pressure difference between the waterward side and the
backwater side, which drives the runner’s high-speed rotation of
the force from this pressure difference. When a0 = 47.41 mm, the
pressure difference of the blade between the waterward side and
the backwater side is the largest, with different degrees of negative
pressure separately. The possibility of these areas occurring in the
cavitation is larger, and these areas will cause damage to the
runner blade. The following measures can be taken to improve
the cavitation performance of the pump turbine.
1) Improving the hydraulic design of the pump turbine.
2) Improving the level of processing technology of the pump
turbine and adopting materials with strong corrosion
resistance.
3) Taking appropriate operational measures to improve
operating conditions.
4) Repairing the pump turbine parts damaged by cavitation.
3.2.2.3 Draft tube
As shown in the flow diagram of the draft tube in Figure 20A, the
water flows out of the runner and enters the straight cone section
first, and the water flow in the straight cone section under the
FIGURE 18 | Internal flow characteristics of volute and vane. (A) Pressure distribution. (B) Velocity vector.
Frontiers in Energy Research | www.frontiersin.org June 2022 | Volume 10 | Article 91468015
Ren et al. Pump Turbines and Wind–PV System
three working conditions has a more obvious velocity ring
volume, forming an eccentric vortex zone. Then, the water
enters the elbow pipe, and the flow direction changes here and
the flow field changes, forming different degrees of vortex
stagnation zone in the elbow section. At a0 = 16.1 mm, the
water flow at the elbow pipe is more turbulent, the vortex
phenomenon is more obvious, and there is a small backflow.
At a0 = 22.47 mm, the water flow at the elbow pipe has small
fluctuations. At a0 = 47.41 mm, the water flow at the elbow pipe is
relatively smooth, more close to the upper wall, and then enters
the diffusion section. As the centrifugal force gradually
disappears, the water flows into the horizontal diffusion
FIGURE 19 | Internal flow characteristics of runner. (A) Flow line. (B) Cross-sectional pressure distribution.
FIGURE 20 | Internal flow characteristics of the draft tube: (A),(B).
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Ren et al. Pump Turbines and Wind–PV System
section through the elbow tube, and the water flows smoothly in
the diffusion section under all three conditions.
As shown in the pressure distribution diagram of the draft
tube section in Figure 20B, the pressure distribution pattern of
the straight cone section under the three working conditions is
similar, showing the characteristics of small in the middle and
large around. The pressure gradually expands from the center to
the circumferential direction. At a0 = 16.1 mm, there are many
small pressure mutations at the side wall of the draft tube inlet
section. At a0 = 22.47 mm, there is negative pressure at the center
of the draft tube inlet section, and there is a certain eccentricity in
the pressure distribution at the end of the straight cone section.
The pressure in the bent elbow section in the three conditions
gradually expands from the center to the circumferential
direction. The pressure distribution has different degrees of
eccentricity and increases along the direction away from the
center of curvature. In the diffusion section of the three
conditions, the pressure distribution in each section is more
uniform because the water flow gradually tends to be smooth.
The pressure distribution in the whole overflow section is
basically the same, and the pressure gradually increases in the
direction of the draft tube outlet.
4 CONCLUSION
In this study, the output of wind and PV power systems
concerning their volatility was analyzed, CFD numerical
simulations of typical working conditions of the pump turbine
under wind–PV–PHES complementary systems were conducted,
and the following conclusions were drawn.
1) The output of wind and PV power systems varies with the
changes in wind speed and solar radiation, respectively, and
the output of both fluctuates greatly. The output of the wind
power system reaches a trough at noon, and the output at
night is larger. The output of the PV power system on SD is
approximately normally distributed, and the output reaches a
peak at noon. But there are still large fluctuations in the output
on RD and CD fluctuates, without obvious rule, and is multi-
peaked.
2) Using the natural spatial and temporal complementary
characteristics of solar and wind energy, we analyzed the
output characteristics of nine typical daily wind–PV
complementary systems formed by the arrangement and
combination, and found that the output of wind and PV
power systems have certain complementary characteristics.
But there are still large fluctuations in the output of the
complementary systems.
3) In order to improve the power generation efficiency and
access capacity of wind and PV power systems, the energy
storage function of the PHES system is used to balance the
maximum efficiency and minimum output fluctuation of
the wind–PV complementary system. After adding three
typical system daily loads, 27 net load curves are obtained
for the wind–PV–PHES complementary systems. The PHES
system pumps and generates electricity to reduce the
abandoned wind and light rates and smooth out system
fluctuations.
4) There are common points in the laws and characteristics of
the internal flow of the pump turbine in different guide vane
opening working conditions. At the same time, the different
guide vane openings cause the flow in each condition to have
its characteristics. CFD technology can well characterize the
internal flow characteristics of the pump turbine and provide
mechanism analysis for the design and optimization of the
pump turbine.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in
the article/Supplementary Material; further inquiries can be
directed to the corresponding authors.
AUTHOR CONTRIBUTIONS
YR: methodology, supervision, funding acquisition, and
resources. RQ: writing—original draft, formal analysis, and
project administration. DW: investigation, data curation,
writing—review and editing, and visualization. SH: software
model and validation.
FUNDING
The study was funded by the Henan Province Key R&D and
Promotion Project (Science and Technology Research) (Grant
No. 212102311054), the Training Program for Young Key
Teachers in Colleges and Universities of Henan Province
(Grant No. 2019GGJS097), and the Science and Technology
Collaborative Innovation Special Project in Zhengzhou city
(Research on water system connectivity in realizing rural
revitalization strategy of water conservancy) (Grant No. 19).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fenrg.2022.914680/
full#supplementary-material
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Ren et al. Pump Turbines and Wind–PV System
REFERENCES
Abadi, M. H., and El-Saadany, E. F. (2010). Overview of Wind Power Intermittency
Impacts on Power Systems [J]. Electr. Power Syst. Res. 80 (6), 627–632. doi:10.
1016/j.epsr.2009.10.035
Acuña, L. G., Lake, M., Padilla, R. V., Lim, N. Y., Ponzón, E. G., and Soo Too, Y. C.
(2018). Modelling Autonomous Hybrid Photovoltaic-Wind Energy Systems
under a New Reliability Approach [J]. Energy Convers. Manag. 172, 357–369.
doi:10.1016/j.enconman.2018.07.025
Barra, P. H. A., de Carvalho, W. C., Menezes, T. S., Fernandes, R. A. S., and Coury,
D. V. (2021). A Review on Wind Power Smoothing Using High-Power Energy
Storage Systems [J]. Renew. Sustain. Energy Rev. 137, 1–18. doi:10.1016/j.rser.
2020.110455
Barrio, R., Fernández, J., Blanco, E., Parrondo, J., and Marcos, A. (2011).
Performance Characteristics and Internal Flow Patterns in a Reverse-
Running Pump-Turbine. Proc. Institution Mech. Eng. Part C J. Mech. Eng.
Sci. 226 (3), 695–708. doi:10.1177/0954406211416304
Bogdanovic-Jovanovic, J., Milenkovic, D., Svrkota, D., Bogdanovic, B., and Spasic,
Z. (2014). Pumps Used as Turbines Power Recovery, Energy Efficiency, CFD
Analysis. Therm. Sci. 18 (3), 1029–1040. doi:10.2298/tsci1403029b
Dianellou, A., Christakopoulos, T., Caralis, G., Kotroni, V., Lagouvardos, K., and
Zervos, A. (2021). Is the Large-Scale Development of Wind-PV with Hydro-
Pumped Storage Economically Feasible in Greece? [J]. Appl. Sci. 11 (5), 1–21.
doi:10.3390/app11052368
Dowds, J., Hines, P., Ryan, T., Buchanan, W., Kirby, E., Apt, J., et al. (2015). A
Review of Large-Scale Wind Integration Studies. Renew. Sustain. Energy Rev.
49, 768–794. doi:10.1016/j.rser.2015.04.134
Gao, J., Zheng, Y., Li, J., Zhu, X., and Kan, K. (2018). Optimal Model for
Complementary Operation of a Photovoltaic-Wind-Pumped Storage System.
Math. Problems Eng. 2018, 1–9. doi:10.1155/2018/5346253
Jurasz, J. (2017). Modeling and Forecasting Energy Flow between National Power
Grid and a Solar-Wind-Pumped-Hydroelectricity (PV-WT-PSH) Energy
Source. Energy Convers. Manag. 136, 382–394. doi:10.1016/j.enconman.2017.
01.032
Koohi-Fayegh, S., and Rosen, M. A. (2020). A Review of Energy Storage Types,
Applications and Recent Developments [J]. J. Energy Storage 27, 2–23. doi:10.
1016/j.est.2019.101047
Kusakana, K. (2016). Optimal Scheduling for Distributed Hybrid System with
Pumped Hydro Storage. Energy Convers. Manag. 111, 253–260. doi:10.1016/j.
enconman.2015.12.081
Li, D., Zuo, Z., Wang, H., Liu, S., Wei, X., and Qin, D. (2019). Review of Positive
Slopes on Pump Performance Characteristics of Pump-Turbines. Renew.
Sustain. Energy Rev. 112, 901–916. doi:10.1016/j.rser.2019.06.036
Olimstad, G., Nielsen, T., and Børresen, B. (2012). Dependency on Runner
Geometry for Reversible-Pump Turbine Characteristics in Turbine Mode of
Operation [J]. J. Fluids Eng. 134 (12). doi:10.1115/1.4007897
Parastegari, M., Hooshmand, R.-A., Khodabakhshian, A., and Zare, A.-H. (2015).
Joint Operation of Wind Farm, Photovoltaic, Pump-Storage and Energy
Storage Devices in Energy and Reserve Markets. Int. J. Electr. Power &
Energy Syst. 64, 275–284. doi:10.1016/j.ijepes.2014.06.074
Rathore, A., and Patidar, N. P. (2019). Reliability Assessment Using Probabilistic
Modelling of Pumped Storage Hydro Plant with PV-Wind Based Standalone
Microgrid. Int. J. Electr. Power & Energy Syst. 106, 17–32. doi:10.1016/j.ijepes.
2018.09.030
Ren, G., Liu, J., Wan, J., Guo, Y., and Yu, D. (2017). Overview of Wind Power
Intermittency: Impacts, Measurements, and Mitigation Solutions. Appl. Energy
204, 47–65. doi:10.1016/j.apenergy.2017.06.098
Sinha, S., and Chandel, S. S. (2015). Review of Recent Trends in Optimization
Techniques for Solar Photovoltaic-Wind Based Hybrid Energy Systems. Renew.
Sustain. Energy Rev. 50, 755–769. doi:10.1016/j.rser.2015.05.040
Song, H., Zhang, J., Huang, P., Cai, H., Cao, P., and Hu, B. (2020). Analysis of
Rotor-Stator Interaction of a Pump-Turbine with Splitter Blades in a Pump
Mode [J]. Mathematics 8 (9), 1465. doi:10.3390/math8091465
Tao, R., and Wang, Z. (2021). Comparative Numerical Studies for the Flow Energy
Dissipation Features in a Pump-Turbine in Pump Mode and Turbine Mode [J].
J. Energy Storage 41, 1–14. doi:10.1016/j.est.2021.102835
Wang, L., Yin, J., Jiao, L., Wu, D., and Qin, D. (2011). Numerical Investigation on
the "S" Characteristics of a Reduced Pump Turbine Model. Sci. China Technol.
Sci. 54 (5), 1259–1266. doi:10.1007/s11431-011-4295-2
Waters, S., and Aggidis, G. A. (2015). Over 2000 Years in Review: Revival of the
Archimedes Screw from Pump to Turbine. Renew. Sustain. Energy Rev. 51,
497–505. doi:10.1016/j.rser.2015.06.028
Widmer, C., Staubli, T., and Ledergerber, N. (2011). Unstable Characteristics and
Rotating Stall in Turbine Brake Operation of Pump-Turbines [J]. J. Fluids Eng.
133 (4). doi:10.1115/1.4003874
Wu, G., Shao, X., Jiang, H., Chen, S., Zhou, Y., and Xu, H (2020). Control Strategy
of the Pumped Storage Unit to Deal with the Fluctuation of Wind and
Photovoltaic Power in Microgrid [J]. Energies 13 (2), 1–23. doi:10.3390/
en13020415
Wu, K., Zhou, H., and Liu, J. (2014). Optimal Capacity Allocation of Large-Scale
Wind-PV-Battery Units. Int. J. Photoenergy 2014, 1–13. doi:10.1155/2014/
539414
Xu, X., Hu, W., Cao, D., Huang, Q., Chen, C., and Chen, Z. (2020). Optimized
Sizing of a Standalone PV-Wind-Hydropower Station with Pumped-Storage
Installation Hybrid Energy System. Renew. Energy 147, 1418–1431. doi:10.
1016/j.renene.2019.09.099
Xu, Y., Lang, Y., Wen, B., and Yang, X (2019). An Innovative Planning Method for
the Optimal Capacity Allocation of a Hybrid Wind–PV–Pumped Storage
Power System [J]. Energies 12 (14), 1–14. doi:10.3390/en12142809
Zare Oskouei, M., and Sadeghi Yazdankhah, A. (2015). Scenario-based Stochastic
Optimal Operation of Wind, Photovoltaic, Pump-Storage Hybrid System in
Frequency- Based Pricing. Energy Convers. Manag. 105, 1105–1114. doi:10.
1016/j.enconman.2015.08.062
Zhang, S., and Qi, J. (2011). Small Wind Power in China: Current Status and Future
Potentials. Renew. Sustain. Energy Rev. 15 (5), 2457–2460. doi:10.1016/j.rser.
2011.02.009
Zhang, X., Dong, X. J., and Li, X. Y. Study of China’s Optimal Concentrated Solar
Power Development Path to 2050[J]. Front. ENERGY Res., 2021, 9:doi:10.3389/
fenrg.2021.724021
Zuo, Z., and Liu, S. (2017). Flow-Induced Instabilities in Pump-Turbines in China.
Engineering 3 (4), 504–511. doi:10.1016/j.eng.2017.04.010
Conflict of Interest: YR was employed by Yellow River Engineering Consulting
Co., Ltd.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
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Frontiers in Energy Research | www.frontiersin.org June 2022 | Volume 10 | Article 91468018
Ren et al. Pump Turbines and Wind–PV System
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