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Transactions on Engineering and Computing Sciences - Vol. 13, No. 02
Publication Date: April 25, 2025
DOI:10.14738/tecs.1302.18485.
Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining
Frequency Stability During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
Services for Science and Education – United Kingdom
Investigating Droop Control Contribution to Grid Resilience by
Maintaining Frequency Stability During Grid Disturbances
C. N. Anyangwe
Africa Centre of Excellence for Sustainable Power and
Energy Development, University of Nigeria Nsukka
C. N. Anyanwu
Department of Agricultural and Bioresources Engineering,
University of Nigeria Nsukka
C. Udanor
Department of Computer Science,
University of Nigeria Nsukka
ABSTRACT
In contrast to conventional power plants, which are based on large synchronous
generators with large inertia capabilities to dampen sudden disturbances,
renewable energy sources, such as solar and wind, connected to the grid through
power electronics converters, display low system inertia and overload limiting
capabilities. Additionally, because they lack primary frequency regulation
capabilities, they are unable to actively respond to the frequency response of the
system. This research investigates the contribution of droop control strategies to
grid resilience by focusing on their ability to maintain frequency stability during
grid disturbances. The study employs a simulation-based approach using
MATLAB/Simulink to model the Djoum power plant in Cameroun and implement
droop control algorithm. The methodology involves designing and analyzing the
system's response under sudden load changes using droop and supervisory control
strategies. Parameters such as droop coefficients and control bandwidths were
systematically varied to analyze their impact on frequency regulation and grid
resilience. Results show that droop control maintains system operation by
adjusting frequency and voltage under disturbance, while supervisory control acts
as a secondary layer to fully restore parameters to their reference values thereby
ensuring reliable operation during grid disturbances.
Keywords: Renewable Energy Integration, Droop Control, Frequency Stability, Grid
Disturbances, MATLAB/Simulink.
INTRODUCTION
Renewable energy sources connected to the grid via power electronics interfaces, display low
system inertia and overload limiting capabilities as compared to conventional power plants
that are based on large synchronous generators with large inertia capabilities to dampen
sudden disturbances [1] [2]. They do not have the ability of primary frequency regulation, so
they cannot actively respond to the frequency response of the system. Connection of renewable
energy resources to the grid often cause random fluctuations in output power. If the frequency
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Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability
During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
URL: http://dx.doi.org/10.14738/tecs.1302.18485
regulation capacity is insufficient, low-frequency load shedding protection action will be
triggered, which would lead the system to disconnect in severe circumstances, compromising
the power grid’s safety. Additionally, the occurrence of unforeseen events such as sudden load
changes, generator outages, and faults further disrupts the balance between power supply and
demand, leading to frequency deviations and potential grid instability. Therefore, it is
absolutely imperative to improve on the frequency stability of such a system in order to make
the grid more resilient to disturbances. Traditional frequency control methods, such as
governor control and automatic generation control (AGC), rely on centralized control schemes
and may not adequately address the dynamic and decentralized nature of modern power
systems. In this context, droop control strategies [3] [4] [5] [6] offer decentralized and
responsive control mechanisms to maintain grid stability.
Despite the extensive application of droop control strategies in microgrids, the dynamic
interaction between primary (droop-based) and secondary (supervisory) control mechanisms
during significant grid disturbances, such as sudden load changes or generator outages,
remains underexplored [7] [8] [9]. Current literature largely addresses these controls
separately, with limited insights into their combined effectiveness in mitigating frequency and
voltage deviations in renewable-dominated grids, particularly under high penetration of PV
systems. Supervisory control plays a crucial role in addressing the steady-state restoration
issues left unresolved by droop control. This research employs the combined use of droop and
supervisory control strategies to address frequency and voltage deviations during sudden grid
disturbances. The case study here is Djoum solar PV plant where an anti-windup proportional
integral controller integrated in an inverter has been used to demonstrate their collective
effectiveness in enhancing grid stability and resilience. The objective is to investigate the
contribution of droop control strategy to grid resilience by focusing on their role in maintaining
frequency stability during grid disturbances by developing an algorithm for proportional
integral droop controller in a MATLAB/Simulink modeling environment [10] [11]. The
proportional integral droop controller designed experiment is expected to respond to
frequency droop control in the grid under various sudden grid disturbance scenarios. It is
hypothesized that proportional integral (PI) droop control mitigates frequency deviations
thereby minimizing the impact on grid stability by varying load disturbances. This study will
help in the understanding of methods to control or mitigate frequency drooping in grid- connected renewable energy resources [12] [13]. It offers opportunities to improve on the
performance reliability and efficiency of microgrid systems and contribute to the transition to
a more sustainable energy future.
PROBLEM DESCRIPTION
Renewable energy sources connected to the grid via power electronics interfaces display low
system inertia, output power fluctuations and overload limiting capabilities as compared to
conventional power plants that are based on large synchronous generators with large inertia
capabilities to dampen sudden disturbances. They do not have primary frequency regulation
ability and cannot actively respond to the system’s frequency response. If the frequency
regulation capacity is insufficient, low-frequency load shedding protection action will be
triggered leading to system disconnection in severe circumstances. Additionally, unforeseen
disturbances like sudden load changes disrupts the balance between power supply and demand
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leading to frequency deviations and potential grid instability. Therefore, it is imperative to
improve on the frequency stability of such a system in order to make the grid more resilient to
disturbances. Traditional frequency control methods, such as governor control and automatic
generation control (AGC) rely on centralized control schemes and do not adequately address
the dynamic and decentralized nature of modern power systems. Droop control has become an
alternative for frequency regulation.
RESEARCH OBJECTIVES
The primary objective is to investigate droop control contribution to grid resilience in
maintaining frequency stability during grid disturbances.
Specific Objectives
1. To implement an identified control method
2. Develop an algorithm for innovative Proportional Integral droop controller in a
MATLAB/Simulink modeling environment
3. To evaluate the algorithm using a case study: Djoum 369.48KW PV plant
METHODOLOGY
The study utilizes a simulation-based approach, leveraging MATLAB/Simulink as the primary
modelling platform.
Experimental Design
The experiment uses an anti-windup PI droop controller under various grid disturbance
scenarios to determine its impact on grid resilience.
Factors:
Two main factors will be considered:
1. Droop Control Strategy: Examining the performance of Proportional-Integral (PI)
Droop.
2. Grid condition-Disturbance Scenarios: Simulating three distinct grid disturbance
scenarios to replicate real-world events: no sudden load change, Sudden Load Change
with and without droop control.
Experimental Procedure:
1. Initialization: Set up the initial conditions of the simulation model and configure droop
control parameters.
2. Execution: The simulation is executed for each grid condition-disturbance scenario.
3. Data Analysis: Analyze the results to assess the performance of droop control under the
different experimental conditions.
4. Replication: This experimental procedure is replicated multiple times to ensure the
reliability and consistency of the results.
Conceptual Design:
The PI droop controller has been designed to regulate the power output of distributed energy
resources (DERs) connected to the grid. The controller will use a proportional-integral (PI)
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Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability
During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
URL: http://dx.doi.org/10.14738/tecs.1302.18485
control algorithm [14] [15] to adjust the power output of DERs in response to frequency
deviations from the nominal value thereby stabilizing the frequency within its predefined
limits.
Algorithm:
1. Initialize controller parameters: Proportional gain (Kp ), Integral gain (Ki
), Nominal
frequency (fnom), Desired power output (Pdesired) and Integration time step (dt) [16]
[17] [18].
2. Repeat for each time step:
a) Measures the current frequency deviation (∆f) from the nominal frequency.
b) Calculates the proportional control action (Pprop) using the formula:
Pprop = Kp ∗ ∆f Equation 1
c) Integrates the frequency deviation over time to compute the integral control action
(Pint):
Pint = Pint + Ki ∗ ∆f ∗ dt Equation 2
d) Calculates the total power adjustment (Padjustment) as the sum of the proportional
and integral control actions:
Padjustment = Pprop + Pint Equation 3
e) Calculate the desired power output (Pdesired) based on the nominal power output
and the total power adjustment:
Pdesired = Pnominal + Padjustment Equation 4
f) Adjust the power output of the DERs to match the desired power output (Pdesired).
g) Repeat the control loop for the next time step
Explanation of Algorithm:
a. The controller initializes the proportional and integral gains, nominal frequency, desired
power output, and integration time step.
b. At each time step, the controller measures the current frequency deviation from the
nominal frequency.
c. The proportional control action is calculated based on the proportional gain and the
frequency deviation. This action provides an immediate response to frequency
deviations.
d. The frequency deviation is integrated over time to compute the integral control action.
This action accumulates over time and helps eliminate steady-state error.
e. The total power adjustment is calculated as the sum of the proportional and integral
control actions.
f. The desired power output is adjusted based on the nominal power output and the total
power adjustment.
g. Finally, the power output of the DERs is adjusted to match the desired power output,
and the control loop repeats for the next time step.
This algorithm incorporates both proportional and integral control actions to regulate the
power output of DERs and maintain grid stability in the face of frequency deviations.
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Figure 1: Matlab/Simulink Design of Djoum PV Plant
➢ The control system is responsible for managing the operation of the inverters. This
includes implementing droop control to regulate power-sharing and grid stability.
➢ The PV plant is composed of solar panels connected to the inverters.
➢ The grid is illustrated by two (02) fixed three-phase RLC load.
➢ The variable load represents the dynamic active and reactive load on the grid.
➢ The measurement system is used to monitor electrical parameters at the time of
simulation.
Paramatisation
➢ Grid subsystem
➢ Load 1 ➢ Load 2
• Active power = 1000kW • Active power = 500kW
• Inductive Reactive power = 200Var • Inductive Reactive power = 0Var
• Capacitive Reactive power = 0Var • Capacitive Reactive power = 0Var
• Frequency = 50Hz • Frequency = 50Hz
• Voltage = 400Vrms • Voltage = 400Vrms
❖ Inverter subsystem
➢ Variable load • Voltage = 400V
• Active power = 300kW to 1000kW depending on
scenario,
• Frequency Regulator:
• Inductive Reactive power = 100Var to 250
depending on scenario
• Propotional gain = 0.3
• Capacitive Reactive power=0Var • Integral gain = 2
• Frequency = 0Hz • Voltage Regulator :
• Voltage = 400Vrms • Propotional gain = 0.1
• Integral gain = 7
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Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability
During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
URL: http://dx.doi.org/10.14738/tecs.1302.18485
Measurement filter (fn) =
10Hz
➢ Supervisory Control
Frequency = 50Hz
➢ Inverter
• Power = 400kW ➢ Voltage Regulator
• Frequency = 50Hz • Propotional gain = 2
• Primary line voltage = 400Vrms • Integral gain = 14
• Secondary line voltage = 480Vrms • Measurement filter (fn) = 10Hz
• Dc link voltage = 1000V ➢ PMW generator
➢ Droop values: • Switching frequency = 2700Hz
• Frequency droop = 1% • Carrier initial phase angle = 90°
• Voltage droop = 4% • Sample time = Ts = 5e
-05
➢ Current Regulator
• Propotional gain = 0.3
• Integral gain = 20
SIMULATION RESULTS AND DISCUSSIONS
System without any Disturbance
In this simulation, there is no sudden increase in load. This represents the normal load (active
load=500KW, reactive load=0 KVar) operation without any grid disturbance. The figures below
illustrate the scenario.
Figure 2: No sudden increase in load-normal operation without any grid disturbance.
Frequency (50Hz) and voltage (400Vrms) are stable as shown on the curves.
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Figure 3: No sudden increase in load-normal operation without any grid disturbance. Inverters
active power (P) & reactive power (Q) output normal for each of the four inverters.
Sudden Load Change
During the simulation, the model goes through the following stages:
a. Load Variation
✓ At 1 second, the total load on the microgrid increases from 1500 kW/200 kvar to
2000kW/400 kvar.
✓ The inverters share the increased load based on their rated capacities, thanks to the
droop control.
b. Droop Control Activation: At 1 seconds, the droop control is enabled on all inverters,
allowing for proportional load sharing without central coordination.
c. Supervisory Control: At 3 seconds, the supervisory control is activated, which adjusts
the droop set points to restore the system's voltage and frequency to nominal values (50
Hz and 400V).
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Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability
During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
URL: http://dx.doi.org/10.14738/tecs.1302.18485
Figure 4: Sudden load change- At 1 second, the droop control is enabled on all inverters,
allowing for proportional load sharing without central coordination. At 3 seconds, the
supervisory control is activated, which adjusts the droop set points to restore the system's
voltage and frequency to nominal values (50 Hz and 400V).
Figure 5: Sudden load change- At 1 second, the power output of the inverters are readjusted.
Sudden Load Change without Droop Control
During the simulation, the model goes through the following stages:
a. Load Variation
✓ At 1 second, the total load on the microgrid increases from 1500 kW/200 kvar to
1900kW/300 kvar.
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✓ The inverters are overwhelmed due to frequency droop and so the system collapses.
b. Droop Control Activation
Droop control is not activated.
Figure 6: Sudden load change with no drop action-At 1 second, the total load on the microgrid
increases from 1500 kW/200 kvar to 1900kW/300 kvar. The inverters are overwhelmed due
frequency droop and so the system collapses.
Figure 7: Sudden load change with no drop action-the system collapses. No power output from
the inverters.
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Anyangwe, C. N., Anyanwu, C. N., & Udanor, C. (2025). Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability
During Grid Disturbances. Transactions on Engineering and Computing Sciences, 13(02). 84-94.
URL: http://dx.doi.org/10.14738/tecs.1302.18485
CONCLUSION
This experiment illustrates that droop control maintains system operation by adjusting
frequency and voltage under disturbances, while supervisory control acts as a secondary layer
to fully restore parameters to their reference values, ensuring reliable operation during grid
disturbances. It is recommended to test the model under larger grid configurations or higher
penetration of distributed energy resources to provide insights into the scalability of the
proposed control strategy. Considering the rise of cyber threats in grid-connected systems, it is
recommended that future research should investigate incorporating cybersecurity measures to
protect control systems from attacks that could destabilize the grid.
ACKNOWLEDGEMENT
I warmly express my profound gratitude and acknowledge the efforts of Prof C.N. Anyanwu and
Dr C.Udanor who always spared their time to thoroughly read, check, correct, advise me on the
way forward and their mentorship guidelines. I wish to appreciate the various support I got
from the academic and non-academic staff of the Africa Centre of Excellence for Sustainable
Power and Energy Development of the University of Nigeria Nsukka as well as the Faculty of
Engineering of the same University.
List of Symbols
Symbol Meaning Unit
Padjustment Power adjustment W
Pnominal Nominal power W
f Frequency Hz
Vrms root-mean-square voltage V
Var Reactive power Var
V voltage V
Kp Proportional gain
Ki Integral gain
fnom Nominal frequency f
Pdesired Desired power W
dt Integration step time s
∆f frequency deviation f
Pprop Proportional control action
Pint Integral control action
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