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1. Solar Energy 208 (2020) 1058–1067
Available online 3 September 2020
0038-092X/© 2020 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
A cost-effective power ramp rate control strategy based on flexible power
point tracking for photovoltaic system
Xingshuo Li a
, Huiqing Wen b,*
, Bingqing Chen b
, Shuye Ding a
, Weidong Xiao c
a
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
b
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
c
School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006, Australia
A R T I C L E I N F O
Keywords:
Active power control
Power ramp-rate control (PRRC)
Flexible power point tracking (FPPT)
Photovoltaic (PV) energy
A B S T R A C T
Due to the intermittent nature of renewable power generation, the power ramp-rate control (PRRC) strategy
becomes essential for Photovoltaic (PV) systems with the increased penetration ratio recently. However, most of
the PRRC strategies are based on energy storage systems, which require high installation cost. Therefore, it is
important to find alternative technology to avoid the additional hardware components and make the PV system
more cost-effective. In this paper, a novel PRRC strategy is proposed, which is based on a flexible power point
tracking (FPPT) strategy without the additional hardware cost. Besides, a ramp-rate measurement (RRM) method
is proposed to detect the power ramp-rate event. The proposed PRRC algorithm is suitable for both of the ramp-
up and ramp-down cases. Furthermore, it is simple and effective in regulating the PV power fluctuations under
the framework of grid code. The effectiveness of the proposed PRRC strategy is validated through simulation and
experimental evaluation under various scenarios. The experimental results based on the real-filed meteorological
profile validate that the proposed PRRC strategy can effectively regulate the ramp rate under 3 W/s, which is
corresponded to 5% of the rated PV power/min.
Grid-connected Photovoltaic (PV) systems have been increasingly
and globally installed in recent years (EPIA, 2018; Tan et al., 2018; Chen
et al., 2019). Due to the stochastic nature of the solar energy, the great
power fluctuation and high ramp-rate are bringing new challenges into
the stability of power grid (Omran et al., 2011; Sukumar et al., 2018;
Wang et al., 2019). Thus, the power ramp-rate control (PRRC) is
required by many electric power regulators for large-scale PV power
systems to minimize the negative impact (Dreidy et al., 2017; Liu et al.,
2018; Beltran et al., 2019). The PRRC aims to curtain any unpredictable
and sudden power fluctuation that impacts on power grid.
The PRRC operation requires reserved power, such as rechargeable
battery packs, to deal with unpredictable and fast change of power
generation. As a result, significant researches and publications have
been based on the additional energy storage strategy (Kakimoto et al.,
2009; Alam et al., 2014; Alam et al., 2015; Ai et al., 2018; Sangwong­
wanich et al., 2018; Tran et al., 2019; Martins et al., 2019; Atif and
Khalid, 2020; Patel et al., 2020). As illustrated in Fig. 1 (a), the energy
storage system (ESS) can be connected with the PV system. As a
consequence, the PV power fluctuations can be smoothed by absorbing
or injecting power from the ESS, as shown in Fig. 1 (b).
In general, using the ESS is a straightforward way to achieve PRRC.
The main concern of this approach is the high initial cost and limited life
time of current batteries. It is also predicted that the price can be even
higher due to the shortage of the material such as Lithium. Thus, it is
essential to find alternative method for the PRRC.
One technology, namely, flexible power point tracking (FPPT) has
attracted recent attention to minimize the heavy reliance on recharge­
able battery technology (Yang et al., 2019; Tafti et al., 2019b,a).
Different from the traditional PV system operation that based on
maximum power point tracking (MPPT) (Bhattacharyya et al., 2020;
Veerapen et al., 2019; Guo et al., 2020), the optimal operating point can
be flexibly and adaptively selected to maximize the benefit of grid
support. The system structure of FPPT is demonstrated in Fig. 2 (a),
which allows the operating point to be away from the maximum power
point (MPP) to a Non-MPP (Sangwongwanich et al., 2017a). Considering
the software-based strategy without additional hardware components,
the approach exhibits advantage for the practical application, which
makes the PV system simple and cost-effective. As shown in Fig. 2 (b)
and (c), the grid-injected power can be regulated at a curtailed level to
realize the PRRC for large-scale PV systems.
* Corresponding author.
E-mail addresses: Xingshuo.li@njnu.edu.cn (X. Li), Huiqing.Wen@xjtlu.edu.cn (H. Wen).
Contents lists available at ScienceDirect
Solar Energy
journal homepage: www.elsevier.com/locate/solener
https://doi.org/10.1016/j.solener.2020.08.044
Received 6 November 2019; Received in revised form 29 May 2020; Accepted 15 August 2020

2. Solar Energy 208 (2020) 1058–1067
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The concept of FPPT is not completely new since the traditional
power generator in the utility scale commonly reserve power capacity,
which is called as power limiting control (PLC) (Sangwongwanich et al.,
2016, 2018; Tafti et al., 2018), and power reserve control (PRC) (Hoke
et al., 2017; Sangwongwanich et al., 2017c,b; Batzelis et al., 2017;
Batzelis et al., 2018; Li et al., 2019). However, the immigration from the
conventional power system concept to the late PV power generation
makes the FPPT new to achieve the PRRC with consideration of the
unique feature of solar power output (Chen et al., 2019; Craciun et al.,
2017; Sangwongwanich et al., 2016; Chen et al., 2020).
A ground-based sensor forecasting system (GBSFS) is created to
predict the cloud shadow arrival time so that the PV output power can be
gradually reduced to achieve the required ramp rate, as claimed in Chen
et al. (2019). As shown in Fig. 2 (c), the method has been proved to be
effective to achieve ramp-up rate but not ramp-down rate control.
Therefore, the ramp-down rate control becomes a challenge for the
PRRC due to the limited energy reservation. Even though the prediction
of the GBSFS is capable for the ramp-up rat control, the solution requires
additional outdoor hardware, which increases the system cost. Mean­
while, the accurate prediction of cloud movement and solar irradiation
change can not be easily performed.
Another study in Sangwongwanich et al. (2016) combines the well-
known MPPT algorithm with the PRRC strategy. The recent study also
points out the difficulty of ramp-rate measurement to follow the grid
regulation accurately due to the perturbation and observation (P&O)
operation of MPPT (Sangwongwanich et al., 2016; Yang et al., 2019).
Based on the grid codes from different countries, three commonly-used
methods for ramp-rate measurement have been reported, as demon­
strated in Fig. 3 (Martins et al., 2019). This is still the initial stage, since
it is still not clearly from the grid codes on how to calculate ramp-rate
measurements in practice (Martins et al., 2019).
In this paper, a PRRC strategy based on the FPPT is proposed. Both of
Fig. 1. Power ramp-rate control strategies for PV system with energy storage
system. (a) Schematic diagram; (b) Demonstration of the PV power smoothing.
Fig. 2. Power ramp-rate control strategies for PV system with flexible power
point tracking. (a) Schematic diagram; (b) Operational principle; (c) Demon­
stration of the PV power smoothing.
Fig. 3. Different ramp-rate calculation methods.
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3. Solar Energy 208 (2020) 1058–1067
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the ramp-up and ramp-down rates are effectively controlled by the
proposed PRRC strategy. Moreover, a new ramp-rate measurement is
also proposed, which is simpler and more effective. A fast changing
profile and a real-field meteorological profile are used in simulation and
experiment evaluation. Finally, both of the simulation and experimental
results validate the effectiveness of proposed PRRC strategy.
1. Ramp-rate measurement method
1.1. Conventional ramp-rate measurement
In order to achieve the PRRC, the power ramp-rate Rr should be
continuously measured, which can be calculate as (Yang et al., 2019):
Rr(t) =
ΔP
Δt
(1)
where ΔP refers to PV output power difference between a certain time
period Δt, t refers to the time instance.
As illustrated from Fig. 3, the Rr calculation is directly related to
selection of Δt (Martins et al., 2019). For example, Δt can be set as 60 s
and Rr is calculated
Rr(t) =
P1 − P2
Δt1
=
P(t) − P(t − 60)
t(t) − t(t − 60)
(2)
Alternatively, Rr can be also calculated by the power difference between
the maximum and minimum values of a certain time interval as:
Rr(t) =
P3 − P4
Δt2
=
Pmax − Pmin
Δt2
(3)
Generally, two ramp-rate measurement (RRM) methods in above are
used in the power system simulation. They are not suitable for the real-
time experiment. In Sangwongwanich et al. (2016), a straightforward
way to calculate Rr is used
Rr(t) =
P5 − P6
Δt3
=
P(t) − P(t − nTP)
nTp
(4)
where n is an integer and Tp refers to algorithm period (i.e., MPPT
perturbation). According to Li et al. (2019), Kivimäki et al. (2017), Tp
can be derived by:
Tp⩾Tε ≅ −
1
ζ⋅ωn
⋅ln
(
ε
̅̅̅̅̅̅̅̅̅̅̅̅̅
1 − ζ2
√ )
(5)
where ωn = 1/
̅̅̅̅̅̅̅̅̅̅
L⋅Cin
√
,ζ = 1/(2⋅Rpv)⋅
̅̅̅̅̅̅̅̅̅̅̅
L/Cin
√
, and ε = 0.1. In this paper,
it is set as 0.1s. For n, it is required to be properly tuned to achieve the
good performance of the PRRC strategy (Sangwongwanich et al., 2016).
If n is too large, it will cause significant delays in Rr calculation.
Therefore, the selection of n is very essential.
1.2. Proposed ramp-rate measurement
Unlike (2)–(4), ΔP rather than Δt is tuned by the proposed RRM
method. Here, Rr is re-defined as:
Rr(t) =
ΔP
Δt
=
P(t) − P10
Δt
(6)
where P10 is updated according to
{
P10 = P(t), Rr,max < ∣Rr(t)∣ ∧ after Δt (a)
P(t), Rr,max⩾∣Rr(t)∣ (b)
(7)
where Rr,max refers to the maximum allowable ramp-rate.
From (6), it can be seen that how to update P10 is crucial to Rr
calculation. In order to understand how P10 is updated, the operational
principle of is illustrated in Fig. 4.
Assuming that a MPPT method (i.e., P&O) is used and Δt is set to
10Tp, namely 1s. If the measured Rr(t) is smaller than the absolute value
of Rr,max, P10 is regularly updated to P(t) after every 10Tp. For instance,
P10 is regularly updated according to (7a) at time 0 s, 1 s, and 2 s. If the
measured Rr(t) is higher than the absolute value of Rr,max, no matter
whether 10Tp is passed, P10 is updated according to (7b), such at time
2.5 s.
Fig. 5 demonstrates the performances of conventional and proposed
RRM methods. Rr(t) is calculated by the proposed RRM method in the
real time without any delay. It can be also seen that a larger value of n
causes a longer delay in the conventional RRM method.
2. Power ramp-rate control
2.1. Conventional power ramp-rate control
The operational principle of the conventional PRRC strategy in
Sangwongwanich et al. (2016) is demonstrated in Fig. 6. According to
Fig. 4. Operational principle of the proposed ramp-rate measurement method. Fig. 5. Performances of different RRM methods.
Fig. 6. Operational principle of the conventional PRRC strategy in Sang­
wongwanich et al. (2016).
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4. Solar Energy 208 (2020) 1058–1067
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Sangwongwanich et al. (2016), (4) is used to calculate Rr. This con­
ventional PRRC strategy is very straightforward. If the measured Rr is
smaller than Rr,max, the MPPT method (i.e., P&O) is used, which is
denoted by ①. Otherwise, the operating point will perturb to the left
side from the MPP, which is denoted by ②. Here, the PV reference
voltage can be summarized as
{
Vref = Vmpp, Rr(t) < Rr,max (a)
V − Vstep, Rr(t)⩾Rr,max (b)
(8)
where Vmpp refers to the reference voltage by using an MPPT method (i.
e., P&O) and Vstep is perturbation step size.
From (8), it can be seen that only the positive ramp-rate is controlled
when the solar irradiance is increased. When the solar irradiance is
decreased, the P&O method is used no matter what value of the
measured Rr is. Therefore, the ramp-down rate is not controlled, which
is demonstrated by the operating trajectory from MPP to A in Fig. 6.
2.2. Proposed power ramp-rate control
Unlike the conventional PRRC strategy, the proposed method can
both of ramp-up and ramp-down rate. Fig. 7 shows the system config­
uration and control structure with the proposed PRRC strategy. Gener­
ally, a two-stage grid-connected PV inverter is used to validate the FPPT-
based control strategies, such as the PRC (Sangwongwanich et al.,
2017c,b), the PLC (Sangwongwanich et al., 2016, 2018; Tafti et al.,
2018) and the PRRC (Sangwongwanich et al., 2016). Actually, these
FPPT-based control strategies are realize at the first stage, namely the
PV-side DC-DC converter. Therefore, a simplified PV system with a DC-
DC converter can be also used to validate the effectiveness of these
control strategies, like (Batzelis et al., 2017; Batzelis et al., 2018; Li
et al., 2019).
As demonstrated in Fig. 7, PV-side voltage Vpv and current Ipv are
measured to feed the block of proposed PRRC strategy. Then, the voltage
command Vref is generated and compared with Vpv. The error between
Vref and Vpv is then calculated to feed the PI controller to regulate Vpv
towards Vref . Finally, the PWM is generated by the PI controller, which is
used to control the DC-DC converter. It should be noted that the sub-
optimal voltage Vopt and Rr,max are two external signals, which are pro­
vided by the system operator.
The flowchart of proposed PRRC strategy is illustrated in Fig. 8. I(t)
and V(t) are instant values of the measured Ipv and Vpv, respectively.
Then, the instant value of Rr(t) is continuously calculated according to
(6). After that, P10 will be updated according to (7).
When P10 is updated, the power ramp-rate will be achieved by
controlling Vref . As shown in Fig. 8, it can be divided into two parts to
update Vref , namely ① and ②. In order to demonstrate the operational
principle, Fig. 9 is used.
Assuming that the operating point is initially at the curtailed level
Vopt, namely point A. When the solar irradiance is slightly increased, the
operating point moves from point A to point B, as shown in Fig. 9 (a) and
(b). At this time, it is found that the measured Rr(t) is smaller than Rr,max.
It indicates that the PV power is not suddenly increased or decreased.
Fig. 7. System configuration and control structure with the proposed
PRRC strategy.
Fig. 8. Flowchart of the proposed PRRC strategy.
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5. Solar Energy 208 (2020) 1058–1067
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Consequently, the operating point will be maintained at Vopt, namely the
point B.
When the solar irradiance is significantly increased, the operating
point moves from point B to point C. At this time, it is found that the
measured Rr(t) is larger than Rr,max. It indicates that the PV power is
suddenly increased or decreased and the PV power should be smoothed.
Then, the operating point will move to point D by tuning Vref as
Vref = PB
/
IC = P(t − 1)
/
I(t) (9)
where this process is marked as ①, PB refers to the power value at point
B and IC refers to the current value at point C. After ①, the operating
point will gradually move towards the point C by updating Vref as
Vref = V(t) ± Vstep (10)
where this process is marked as ②, Vstep refers to voltage step. Here, the
power changes ΔP in every Tp should not be exceeded Rr,max, so we have
ΔP = Vstep × I(t)⩽Rr,max × Tp (11)
Rearrange (11), we have
Vstep =
Rr,max × Tp
I(t)
(12)
Finally, combine the process ① and ②, Vref is calculated by
{
Vref = P(t − 1)
/
I(t), ∣Rr(t)∣⩾Rr,max (a)
V(t) ± Vstep, ∣Rr(t)∣ < Rr,max (b)
(13)
When the solar irradiance is decreased, a similar process is illustrated
in Fig. 9 (c) and (d). The only different thing is that the PV voltage will
be immediately perturbed to the right side if Rr(t) is larger than Rr,max.
Finally, Fig. 10 is used to demonstrate the proposed FPPT-based PRRC
strategy. It can be seen clearly that the operating point is flexibly
changed at the curtailed power level to limit the PV power fluctuations.
Fig. 9. Operational principle of the proposed PRRC strategy. (a)(b) Solar
irradiance is increased; (c)(d) solar irradiance is decreased.
Fig. 10. Demonstration of the proposed FPPT-based PRRC strategy.
Table 1
Corresponding specifications of DC-DC converter and PV module for the tested
PV system.
Parameter Symbol Value
Input capacitor Cpv 470 μF
Output voltage Vdc 24 V
Inductance L 1 mH
Switching frequency f 10 kHz
Maximum power Pmpp 60 W
Voltage at maximum power Vmpp 17.1 V
Current at maximum power Impp 3.5 A
Open-circuit voltage Voc 21.1 V
Short-circuit current Isc 3.8 A
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6. Solar Energy 208 (2020) 1058–1067
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3. Simulation results
In order to validate the effectiveness of proposed PRRC strategy,
simulation have been carried referring to Fig. 7. The corresponding
parameters of tested PV system are given in Table 1.
The conventional PRRC strategy in Sangwongwanich et al. (2016) is
used to compare with the proposed PRRC strategy. n is set to 10 for the
RRM in the conventional PRRC strategy. Besides, the P&O method is
also used for the MPPT control. The time interval of perturbation Tp is
set to 0.1 s for all of these control strategies.
The simulation results with three different cases are shown in
Figs. 11–13. In the case I, the ramp-rate changes in solar irradiance is 6
W/s and Rr,max is set to 3 W/s. As shown in Fig. 11, the maximum PV
output power is increased from 36 W to 60 W during the time from 1s to
5 s. Initially, the RRM with the conventional PRRC strategy has a delay
during the time from 1 s to 2 s, as show in Fig. 11 (a). As a consequence,
the power changes in this time period is not limited under Rr,max. After 2
s, the power changes is controlled and limited under Rr,max. During the
time from 6 s to 10 s, the maximum PV output power is decreased from
60 W to 36 W. In this time period, the ramp-down rate in PV output
Fig. 11. Simulation results for case I. (a) Conventional PRRC strategy; (b)
Proposed PRRC strategy.
Fig. 12. Simulation results for case II. (a) Conventional PRRC strategy; (b)
Proposed PRRC strategy.
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7. Solar Energy 208 (2020) 1058–1067
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power is not controlled by the conventional PRRC strategy. By contrast,
both of the ramp-down and ramp-up rates in PV output power are
properly controlled by the proposed PRRC strategy during the same
time, as shown in Fig. 11 (b). It is also clearly seen that there is no delay
in the RRM for the proposed PRRC strategy.
Same to the case I, the ramp-rate changes in solar irradiance in case II
is set to 6 W/s. However, Rr,max is set to 2 W/s, which has a higher
requirement on the PRRC strategy. From Fig. 12 (a), it can be seen that
the lower Rr,max is more challenging to the conventional PRRC strategy.
However, it does not affect the performance of the proposed method.
Both of the ramp-down and ramp-up rates in PV output power are still
Fig. 13. Simulation results for case III. (a) Conventional PRRC strategy; (b)
Proposed PRRC strategy.
Fig. 14. Movements of the operating trajectory for the case I. (a) Solar irra­
diance is increased; (b) solar irradiance is decreased.
Fig. 15. Movements of the operating trajectory for the case III. (a) Solar irra­
diance is increased; (b) solar irradiance is decreased.
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8. Solar Energy 208 (2020) 1058–1067
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controlled properly, as shown in Fig. 12 (b).
In the case III, both of the ramp-rate changes in solar irradiance and
Rr,max are set to 3 W/s, as shown in Fig. 13. The RRM with the con­
ventional PRRC strategy still has an initial delay, as shown in Fig. 13 (a).
However, it dose not affect the ramp-up rate is controlled under Rr,max by
the conventional PRRC strategy. Besides, the PV output power is not
smoothed when the solar irradiance is decreased. It should be noted that
the Vopt is set to 14 V for the proposed PRRC strategy, as shown in Fig. 13
(b). The positive and negative ramp rates are also controlled properly.
Compared to case I, the power loss caused by proposed PRRC strategy is
much less.
It should be noted that both the conventional and proposed PRRC
strategies have power loss to achieve the ramp-rate control. Here, the
power efficiency caused by the PRRC strategies ηprrc is defined
ηprrc =
Ppo(t)
Pprrc(t)
(14)
where Ppo(t) and Pprrc(t) refers to the instantaneous power extracted by
the P&O method and the PRRC strategies. The average values of ηprrc for
the conventional and proposed PRRC strategies in each case are marked
in Figs. 11–13.
In order to compare the performance of conventional and propose
PRRC strategies, the movements of operating trajectory for the case I
and case III are demonstrated in Figs. 14 and 15, respectively. When the
solar irradiance is increasing fast, the operating point for the conven­
tional PRRC strategy firstly is perturbed towards the right side of MPP,
as shown in Fig. 14 (a). It is caused by the P&O method due to the fast
increase in solar irradiance, which is famous as “drift” condition (Killi
et al., 2015; Li et al., 2016). Once the measured Rr is larger than Rr,max,
the operating point is perturbed towards the left side and the changes in
PV output power is gradually reduced to Rr,max. When the solar irradi­
ance is increasing slow, there is no drift happened for the conventional
PRRC strategy, as shown in Fig. 15 (a). The operating point is perturbed
around 16 V to maintain the measured Rr is below Rr,max. It should be
noted that the ramp-down rate is not controlled by the conventional
PRRC strategy when the solar irradiance is decreased. As shown in
Figs. 14 and 15 (b), the operating point is perturbed towards the MPP.
For the proposed PRRC strategy, the operating point is firstly per­
turbed by using the process ① when the solar irradiance is suddenly
changed. Then, the operating point is perturbed towards Vopt by using
the process ②. When the solar irradiance is continuously changing fast,
the operating point may not able to reach Vopt, which causes more power
loss. However, the less power loss can be achieved by tuning a higher
value of Vopt if the solar irradiance is changing slow. Whatever the power
loss is high or low, both of ramp-up and ramp-down rates are controlled
by the proposed PRRC strategy.
4. Experimental results
Experimental results have been also carried out and the system set-
up is shown in Fig. 16, which is referred to Fig. 7. A PV emulator is
used to emulate solar irradiance profiles and the proposed PRRC strat­
egy is implemented dSPACE DS1104. The main components and control
variables are shown into Table 2.
Firstly, the PV emulator is adopted with the fast changing profile of
solar irradiance, which is shown in Fig. 17. The solar irradiance is
changed between 600 W/m2
and 1000 W/m2
by 100 W/m2
in every 1s,
which is same to the case I in the simulation. From Fig. 17, it can been
seen that the proposed PRRC strategy can successfully limit the power
ramp-rate under the fast changing rate in PV power. Both of the ramp-up
and ramp-down rates are successfully limited under Rr,max.
In order to further verify the effectiveness of proposed PRRC strategy
in real life, the PV emulator is adopted with a real-field meteorological
profile in Humboldt State University (HSU), California, which is shown
in Fig. 18. It should be also noted that the resolution of real-field
meteorological profile is 1 min, which takes around 8 h to carry out.
In order to save the experimental time, two accelerated tests are carried
Fig. 16. Experimental set-up of the simplified PV system.
Table 2
Main components for the prototype.
Parameter Value
Electrolytic capacitor Cin(PV side) 470 μF
Electrolytic capacitor Cout(Load side) 47 μF
Inductor L 1 mH
IGBT IRG4PH50U
Diode RHRG30120
Current transducer LA25-NP
Voltage transducer LV25-P
Switching frequency 10 kHz
Fig. 17. Experimental results under a fast changing rate in PV power with 1 s
time interval.
Fig. 18. Real-field meteorological profile in Humboldt State University (HSU),
California, 31th Jul. 2015.
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9. Solar Energy 208 (2020) 1058–1067
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out (i.e., 60 and 30 times faster than the real-field meteorological pro­
file). Besides, a period of time from 8:13:00 to 16:03:00 is adopted to
further accelerate the experiment. It should be noted that Rr,max = 3 W/s
corresponds to 5% of the rated PV power/min, which is much lower than
some grid codes like 10%/min in Germany (Sangwongwanich et al.,
2016).
The experimental results of the two accelerated tests are shown in
Figs. 19 (a) and 20 (a), respectively. Although some power is lost due to
the proposed PRRC strategy, the PV output power is much smoother
compared to that with the MPPT control. The large and frequent changes
of PV voltage indicate the proposed PRRC strategy is active (i.e., 130s-
190s in Fig. 19 (a)). These changes in PV voltage are resulted by using
the process ① and ② in succession.
To be more specific, zoom-up experimental results during 255 s–295
s and 500 s–580 s are given in Figs. 19 (b) and 20 (b), respectively. It can
been seen clearly that the proposed PRRC strategy can successfully limit
the power ramp-rate under the rapid changing periods. Both of the
ramp-up and ramp-down rates are successfully limited under Rr,max.
5. Conclusion
In this paper, a cost-effective FPPT-based PRRC strategy is proposed
for the PV system. Two challenging issues related to the ramp-rate
measurement and the ramp-down rate limitation have been addressed
by using the proposed PRRC strategy. The proposed PRRC strategy is
compared with the conventional PRRC strategy through both simulation
and experiments under various scenarios. Main simulation results show
that the proposed PRRC strategy can effectively control both of the ramp
up rate and ramp down rate, which are below 2 W/s and 3 W/s.
Furthermore, the experimental results which are based on the real-filed
meteorological profile are provided, which validate that the proposed
PRRC strategy can effectively regulate the ramp rate under 3 W/s, which
is corresponded to 5% of the rated PV power/min.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Fig. 19. Experimental results under the profile in HSU with 1 s resolution. (a)
Full scope; (b) zoom-up scope during 255 s–295.
Fig. 20. Experimental results under the profile in HSU with 2 s resolution. (a)
Full scope; (b) zoom-up scope during 500 s–580 s.
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10. Solar Energy 208 (2020) 1058–1067
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Acknowledgement
This work was supported by the Research development fund of
XJTLU (RDF-16-01-10, RDF-17-01-28), the Research Enhancement fund
of XJTLU (REF-17-01-02), the Suzhou Prospective Application pro­
gramme (SYG201723), and the XJTLU Key Programme Special Fund
(KSF-A-08, KSF-E-13, KSF-T-04).
References
Ai, X., Li, J., Fang, J., Yao, W., Xie, H., Cai, R., Wen, J., 2018. Multi-time-scale
coordinated ramp-rate control for photovoltaic plants and battery energy storage.
IET Renew. Power Gen. 12 (12), 1390–1397.
Alam, M.J.E., Muttaqi, K.M., Sutanto, D., 2014. A novel approach for ramp-rate control
of solar pv using energy storage to mitigate output fluctuations caused by cloud
passing. IEEE Trans. Energy Convers. 29 (2), 507–518.
Alam, M.J.E., Muttaqi, K.M., Sutanto, D., 2015. A multi-mode control strategy for var
support by solar pv inverters in distribution networks. IEEE Trans. Power Syst. 30
(3), 1316–1326.
Atif, A., Khalid, M., 2020. Saviztky–golay filtering for solar power smoothing and ramp
rate reduction based on controlled battery energy storage. IEEE Access 8,
33806–33817.
Batzelis, E.I., Kampitsis, G.E., Papathanassiou, S.A., 2017. Power reserves control for pv
systems with real-time mpp estimation via curve fitting. IEEE Trans. Sustain. Energy
8 (3), 1269–1280.
Batzelis, E.I., Papathanassiou, S.A., Pal, B.C., 2018. Pv system control to provide active
power reserves under partial shading conditions. IEEE Trans. Power Electron. 33
(11), 9163–9175.
Beltran, H., Tomás García, I., Alfonso-Gil, J.C., Pérez, E., 2019. Levelized cost of storage
for li-ion batteries used in pv power plants for ramp-rate control. IEEE Trans. Energy
Convers. 34 (1), 554–561.
Bhattacharyya, S., Patnam, D.S.K., Samanta, S., Mishra, S., 2020. Steady output and fast
tracking mppt (soft mppt) for p o and inc algorithms. IEEE Trans. Sustain. Energy 1.
Chen, X., Du, Y., Lim, E., Wen, H., Jiang, L., 2019. Sensor network based pv power
nowcasting with spatio-temporal preselection for grid-friendly control. Appl. Energy
255, 113760.
Chen, X., Du, Y., Lim, E., Wen, H., Yan, K., Kirtley, J., 2020. Power ramp-rates of utility-
scale pv systems under passing clouds: Module-level emulation with cloud shadow
modeling. Appl. Energy 268, 114980.
Chen, X., Du, Y., Wen, H., Jiang, L., Xiao, W., 2019. Forecasting-based power ramp-rate
control strategies for utility-scale pv systems. IEEE Trans. Ind. Electron. 66 (3),
1862–1871.
Craciun, B., Kerekes, T., Séra, D., Teodorescu, R., Annakkage, U.D., 2017. Power ramp
limitation capabilities of large pv power plants with active power reserves. IEEE
Trans. Sustain. Energy 8 (2), 573–581.
Dreidy, M., Mokhlis, H., Mekhilef, S., 2017. Inertia response and frequency control
techniques for renewable energy sources: A review. Renew. Sust. Energ. Rev. 69,
144–155.
EPIA, 2018. Global market outlook 2018-2022. http://solarpowereurope.org/reports/g
lobal-market-outlook-2018/?L=994.
Guo, B., Su, M., Sun, Y., Wang, H., Liu, B., Zhang, X., Pou, J., Yang, Y., Davari, P., 2020.
Optimization design and control of single-stage single-phase pv inverters for mppt
improvement. IEEE Trans. Power Electron. 1.
Hoke, A.F., Shirazi, M., Chakraborty, S., Muljadi, E., Maksimovic, D., 2017. Rapid active
power control of photovoltaic systems for grid frequency support. IEEE J. Em. Sel.
Top. P. 5(3), 1154–1163.
Kakimoto, N., Satoh, H., Takayama, S., Nakamura, K., 2009. Ramp-rate control of
photovoltaic generator with electric double-layer capacitor. IEEE Trans. Energy
Convers. 24 (2), 465–473.
Killi, M., Samanta, S., 2015. An adaptive voltage-sensor-based mppt for photovoltaic
systems with sepic converter including steady-state and drift analysis. IEEE Trans.
Ind. Electron. 62 (12), 7609–7619.
Kivimäki, J., Kolesnik, S., Sitbon, M., Suntio, T., Kuperman, A., 2017. Revisited
perturbation frequency design guideline for direct fixed-step maximum power point
tracking algorithms. IEEE Trans. Ind. Electron 64 (6), 4601–4609.
Li, X., Wen, H., Jiang, L., Xiao, W., Du, Y., Zhao, C., 2016. An improved mppt method for
pv system with fast-converging speed and zero oscillation. IEEE Trans. Ind. Appl. 52
(6), 5051–5064.
Li, X., Wen, H., Zhu, Y., Jiang, L., Hu, Y., Xiao, W., 2019. A novel sensorless photovoltaic
power reserve control with simple real-time mpp estimation. IEEE Trans. Power
Electron. 34 (8), 7521–7531.
Liu, Y., You, S., Tan, J., Zhang, Y., Liu, Y., 2018. Frequency response assessment and
enhancement of the u.s. power grids toward extra-high photovoltaic generation
penetrations-an industry perspective. IEEE Trans. Power Syst. 33 (3), 3438–3449.
Martins, J.P.R., Spataru, S., Sera, D., Stroe, D.I., Lashab, A., 2019. Comparative study of
ramp-rate control algorithms for pv with energy storage systems. Energies 12 (7),
1342.
Omran, W.A., Kazerani, M., Salama, M.M.A., 2011. Investigation of methods for
reduction of power fluctuations generated from large grid-connected photovoltaic
systems. IEEE Trans. Energy Convers. 26 (1), 318–327.
Patel, S., Ahmed, M., Kamalasadan, S., 2020. A novel energy storage-based net-load
smoothing and shifting architecture for high amount of photovoltaics integrated
power distribution system. IEEE Trans. Ind. Appl. 56 (3), 3090–3099.
Sangwongwanich, A., Angenendt, G., Zurmuhlen, S., Yang, Y., Sera, D., Sauer, D.U.,
Blaabjerg, F., 2018. Enhancing pv inverter reliability with battery system control
strategy. CPSS Trans. Power Electron. Appl. 3 (2), 93–101.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., 2016. A cost-effective power ramp-rate
control strategy for single-phase two-stage grid-connected photovoltaic systems. In:
2016 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1–7.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., 2016. High-performance constant power
generation in grid-connected pv systems. IEEE Trans. Power Electron. 31 (3),
1822–1825.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., 2017a. Development of flexible active
power control strategies for grid-connected photovoltaic inverters by modifying
mppt algorithms. In: 2017 IEEE 3rd International Future Energy Electronics
Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), pp. 87–92.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., 2017b. A sensorless power reserve control
strategy for two-stage grid-connected pv systems. IEEE Trans. Power Electron. 32
(11), 8559–8569.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., Sera, D., 2017c. Delta power control
strategy for multistring grid-connected pv inverters. IEEE Trans. Ind. Appl. 53 (4),
3862–3870.
Sangwongwanich, A., Yang, Y., Blaabjerg, F., Wang, H., 2018. Benchmarking of constant
power generation strategies for single-phase grid-connected photovoltaic systems.
IEEE Trans. Ind. Appl. 54 (1), 447–457.
Sukumar, S., Marsadek, M., Agileswari, K.R., Mokhlis, H., 2018. Ramp-rate control
smoothing methods to control output power fluctuations from solar photovoltaic
(pv) sources—a review. J. Energy Storage 20, 218–229.
Tafti, H.D., Maswood, A.I., Konstantinou, G., Pou, J., Blaabjerg, F., 2018. A general
constant power generation algorithm for photovoltaic systems. IEEE Trans. Power
Electron. 33 (5), 4088–4101.
Tafti, H.D., Sangwongwanich, A., Yang, Y., Pou, J., Konstantinou, G., Blaabjerg, F.,
2019a. An adaptive control scheme for flexible power point tracking in photovoltaic
systems. IEEE Trans. Power Electron. 34 (6), 5451–5463.
Tafti, H.D., Townsend, C.D., Konstantinou, G., Pou, J., 2019b. A multi-mode flexible
power point tracking algorithm for photovoltaic power plants. IEEE Trans. Power
Electron. 34 (6), 5038–5042.
Tan, Z., Tan, Q., Rong, M., 2018. Analysis on the financing status of pv industry in china
and the ways of improvement. Renew. Sustain. Energy Rev. 93, 409–420.
Tran, V.T., Islam, M.R., Sutanto, D., Muttaqi, K.M., 2019. Mitigation of solar pv
intermittency using ramp-rate control of energy buffer unit. IEEE Trans. Energy
Convers. 34 (1), 435–445.
Veerapen, S., Wen, H., Li, X., Du, Y., Yang, Y., Wang, Y., Xiao, W., 2019. A novel global
maximum power point tracking algorithm for photovoltaic system with variable
perturbation frequency and zero oscillation. Sol. Energy 181, 345–356.
Wang, X., Virguez, E., Xiao, W., Mei, Y., Patiño-Echeverri, D., Wang, H., 2019. Clustering
and dispatching hydro, wind, and photovoltaic power resources with multiobjective
optimization of power generation fluctuations: A case study in southwestern china.
Energy 189, 116250.
Yang, Y., Kim, K.A., Blaabjerg, F., Sangwongwanich, A., 2019. 6 - flexible active power
control of pv systems. In: Yang, Y., Kim, K.A., Blaabjerg, F., Sangwongwanich, A.
(Eds.), Advances in Grid-Connected Photovoltaic Power Conversion Systems.
Woodhead Publishing, pp. 153–185.
X. Li et al.