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Optimized Control of Distributed Generation for Grid Stability and
Power Quality Improvement
Ozor Godwin Odozo, Oleka Chioma Violet, Asanya Onyebuchi Nduka
Computer Engineering, Enugu State University of Science and Technology
Electrical and Electronics Engineering, Institute of Management and Technology, Enugu, Nigeria
DOI: https://doi.org/10.51584/IJRIAS.2025.1010000019
Received: 24 Sep 2025; Accepted: 01 Oct 2025; Published: 28 October 2025
ABSTRACT
The incorporation of distributed generation (DG) into contemporary distribution networks is rapidly
progressing, primarily propelled by the adoption of renewable energy sources. DG makes things more
sustainable and resilient, but its intermittent and inverter-based nature makes it hard to keep voltage stable,
frequency regulated, and power quality high. This paper presents an optimized control framework that
integrates hierarchical control with Model Predictive Control (MPC) and artificial intelligence-driven adaptive
tuning to concurrently tackle these challenges. The framework is structured as a multi-objective optimization
problem focused on reducing voltage deviation, frequency fluctuations, and harmonic distortion. To test the
method, a modified IEEE 33-bus test system with several DG units is modeled in MATLAB/Simulink and
PSCAD. When compared to base case operation, conventional droop-based control, and the proposed
optimized control, the framework cuts bus voltage deviation by 50%, limits frequency deviations to less than
0.1 Hz with a faster settling time, and cuts Total Harmonic Distortion (THD) by more than 40%, all while still
being able to be computed. These results show that the suggested strategy is a complete and scalable way to
integrate DG reliably, making sure that smart grids stay stable and that power quality improves as they change.
INTRODUCTION
The increasing use of Distributed Generation (DG), especially renewable sources like photovoltaic (PV)
systems and wind turbines, is changing traditional power systems into networks that are more decentralized
and dynamic. The world needs to cut down on greenhouse gas emissions, diversify its energy supply, and
make electricity infrastructure more resilient, which is why people are relying more and more on DG [1]. The
International Energy Agency says that by 2030, more than 30% of electricity generation in some areas will
come from renewable-based distributed generation. This shows how important it is for reaching sustainable
energy goals [2]. Even though DG integration has many benefits, it also makes modern grids harder to run
reliably. Traditional bulk power systems were built to work with centralized generation, which had predictable
output and a lot of inertia. In contrast, DG, particularly inverter-based renewable sources, demonstrates
stochastic behavior, low inertia, and high variability, potentially undermining voltage stability, frequency
regulation, and power quality [3], [4]. These issues manifest as harmonic distortions from power electronic
converters, frequency deviations during sudden load or generation shifts, and voltage variations at weak buses.
If these problems aren't fixed, they could hurt the grid's technical reliability and the quality of the power that
end users get [5].
To lessen these difficulties, a number of control techniques have been put forth. A fundamental framework for
DG coordination is offered by traditional methods like hierarchical primary-secondary-tertiary control schemes
and droop control [6]. In low-inertia systems, sophisticated techniques like Model Predictive Control (MPC)
and Virtual Synchronous Generator (VSG) enhance dynamic response and simulate inertia [7]. In order to
enable adaptive, data-driven control of DG units, clever strategies utilizing fuzzy logic, neural networks, and
reinforcement learning have been introduced more recently [8], [9]. Nevertheless, most of these approaches
focus on either power quality or stability as separate goals. For instance, active filtering techniques enhance
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power quality but do not contribute to system-wide stability, whereas droop and VSG methods are efficient in
controlling voltage and frequency but offer little harmonic mitigation [10].
The research gap lies in the absence of a comprehensive optimization framework that simultaneously ensures
voltage and frequency stability while improving power quality under high DG penetration. Moreover, most
existing works lack a systematic evaluation of control strategies in realistic distribution systems with multiple
DGs and nonlinear loads. These limitations call for hybrid approaches that can coordinate the strengths of
conventional, advanced, and intelligent controllers within a unified optimization paradigm.
To address this gap, this paper proposes an optimized control framework that integrates hierarchical control
with MPC and artificial intelligence-based adaptive tuning. The framework is formulated as a multi-objective
optimization problem designed to minimize bus voltage deviation, limit frequency excursions, and reduce
Total Harmonic Distortion (THD). Simulation studies are conducted on a modified IEEE 33-bus distribution
test system to validate the proposed strategy.
This paper makes the following important contributions:
Comprehensive Analysis: A thorough examination of the challenges related to grid stability and power quality
in distributed generation systems, emphasizing the shortcomings of current control techniques.
Proposed Framework: A control architecture based on hybrid optimization that uses hierarchical control, MPC,
and AI-driven adaptation to meet several performance goals at once.
Simulation Validation: A comparison of the base case, conventional control, and optimized control scenarios
on the IEEE 33-bus system, with results showing big improvements in voltage stability, frequency response,
and harmonic suppression.
Scalability and Feasibility Assessment: A discussion of how to put the plan into action, taking into account
things like communication latency, computational effort, and cyber-resilience.
The rest of this paper is set up like this. In Section II, we look at the problems with DG systems, stability, and
power quality, as well as the current control strategies. Section III shows the suggested improved control
framework. Section IV talks about the simulation method, which includes how to model the test system and
how to measure its performance. Section V talks about the results and how they compare to each other. Lastly,
Section VI wraps up the paper and gives ideas for future research.
RELATED WORK
A. Distributed Generation Systems
Distributed Generation (DG) is the term for small- to medium-sized power sources that are close to the load
and are often connected at the distribution level. These include renewable sources like solar photovoltaic (PV)
arrays, wind turbines, biomass plants, and micro-hydro, as well as non-renewable sources like diesel
generators and microturbines [11]. The growing use of DG has turned passive distribution networks into active
systems, which have advantages like lower transmission losses, greater resilience, and better use of local
energy resources [12]. The integration of inverter-based distributed generation (DG), which is the most
common type of renewable energy, changes the way things work in a unique way. These units do not have
inertia built in like synchronous generators do, which means that frequency excursions happen more quickly
during disturbances [13].
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Fig. 1 shows a diagram of a DG-integrated distribution network, with several renewable units connected
through inverters and managed by both local and higher-level controllers. The figure shows how inverter-
based DGs change the way power flows and how distribution systems are controlled.
Grid Stability and Power Quality Issues
The integration of DG has a big effect on both the quality of the power and the stability of the grid. When it
comes to stability, voltage changes happen a lot because the output of renewable DGs changes depending on
where they are and what they're doing [14]. In low-inertia systems, frequency stability is also affected because
the natural inertial response of synchronous machines can no longer balance load and generation [15].
Problems with power quality are just as bad. Harmonics, flicker, and unbalances are caused by inverter-based
DG units, which can hurt sensitive equipment and customer loads [16]. Field studies have indicated that the
absence of coordination among DG units can lead to concurrent voltage increases at various buses, causing
equipment overvoltage and malfunctioning protection systems [17]. In the same way, microgrids with a lot of
solar PV inverters have been found to have harmonic distortion that is higher than the IEEE 519 standards
[18]. These problems show that DG has two sides: it makes things more sustainable and resilient, but it also
makes utilities and customers less reliable.
Table 1: Common Grid Stability and Power Quality Issues from DG Integration
Issue Cause Impact on System
Voltage rise
/fluctuation
Intermittent PV/wind generation,
weak buses
Overvoltage, equipment stress, and
misoperation of relays
Frequency deviation Low system inertia, sudden
load/generation imbalance
Instability, load shedding, and poor
synchronization
Harmonic distortion Power electronic inverters,
nonlinear loads
Overheating of equipment, malfunction
of sensitive devices
Islanding DG continuing to supply during
grid outage
Safety hazard, loss of coordination with
utility
Table 1 shows a list of common stability and quality problems that come up when integrating DG. The
problems are grouped into four groups: voltage rise, frequency deviation, harmonic distortion, and islanding.
Existing Control Strategies
To deal with the problems that come up with DG integration, a number of control strategies have been created.
Droop characteristics and PID regulation are two important parts of traditional control methods that help keep
voltage and frequency stable [19]. These methods are easy to use and work well, but they don't always work to
reduce harmonics or adapt to changing levels of renewable energy use. In Fig. 2, hierarchical control schemes
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are split into primary, secondary, and tertiary levels. They extend droop control by coordinating distributed
units across a microgrid [20].
Fig 2: Hierarchical Control Structure
Advanced control methods like the Virtual Synchronous Generator (VSG) have been developed to imitate
inertia and damping, which makes frequency dynamics better in systems with a lot of renewable energy [21].
Model Predictive Control (MPC) does something similar by predicting system states over a time horizon,
which makes recovery faster during disturbances [22]. These methods work well, but they need detailed
models and a lot of computing power, which makes them hard to scale
Recently, intelligent control methods that use artificial intelligence (AI) have been suggested. Fuzzy logic
controllers have been utilized to adjust droop coefficients in real time [23], whereas reinforcement learning
algorithms empower distributed generation (DG) units to independently acquire optimal control strategies in
the face of uncertainty [24]. Neural networks and deep learning techniques have been employed for harmonic
suppression and the enhancement of predictive stability [25]. These smart methods can adapt, but they usually
need a lot of data to train and have trouble making sure they stay stable in all situations.
Fig 3: Comparison of Control Strategies
Fig. 3 shows how traditional, advanced, and intelligent control methods compare in terms of their range and
usefulness. Conventional methods are quick and dependable, but they don't work for many things. Advanced
methods make dynamics better, but they require a lot of models. Intelligent methods are adaptable but
encounter validation difficulties.
Research Gap
A thorough examination of the literature indicates that the majority of current methodologies concentrate
exclusively on either stability or power quality. Droop and VSG methods put voltage and frequency first, but
they don't care about harmonics. Active filtering and other harmonic mitigation strategies improve power
quality, but they don't help keep the frequency stable. Even hybrid strategies often don't have integrated
optimization, which leads to outcomes that aren't the best [26].
Table 2: Comparative Review of Control Strategies for DG
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Control Method Objective Strengths Limitations
Droop control Voltage/frequency Simple, decentralized,
widely used
Limited harmonic mitigation,
slow secondary response
PID regulators Local stability Easy to implement Sensitive to parameter tuning,
not adaptive
Virtual
Synchronous
Generator (VSG)
Inertia emulation,
stability
Improves frequency
dynamics, inertia-like
response
Model-intensive, high
computational demand
Model Predictive
Control (MPC)
Multi-variable
optimization
Fast response, predictive
capability
Requires accurate models,
high computation
Fuzzy/AI-based
control
Adaptive
optimization
Handles uncertainty,
learning capability
Needs training data, limited
stability guarantees
Table 2 summarizes the review of existing control methods, pointing out their goals, strengths, and
weaknesses. The table shows that none of the methods fully deal with voltage stability, frequency dynamics,
and harmonic suppression at the same time. The proposed optimized control framework fills this gap by
bringing together hierarchical, predictive, and intelligent strategies into a multi-objective optimization model.
METHODOLOGY
A thorough simulation-based method was used to test how well the proposed optimized control framework
worked. The methodology comprised modeling a benchmark distribution network, incorporating various
distributed generation (DG) units, executing the control framework, and evaluating performance relative to
traditional methods.
Test System Description
The IEEE 33-bus radial distribution system was chosen as the test platform because it is often used in studies
of power quality and stability [32]. This system is a weak distribution network that is very sensitive to DG
integration. This makes it a good choice for testing the effects of advanced control strategies. At buses 6, 14,
24, and 30, which have relatively high load demands, several distributed generation (DG) units were
strategically placed. These units include photovoltaic (PV) arrays, wind turbines, and a microturbine.
Fig 4: Modified IEEE 33-bus with DG at 6, 14,24,30
Fig. 4 depicts the schematic of the altered IEEE 33-bus system with distributed generation placements. To
better reflect how people use power in the real world, loads were modeled using a mix of constant power and
ZIP (impedance–current–power) models.
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Simulation Environment
The simulation studies were done with MATLAB/Simulink and PSCAD/EMTDC, which added harmonic
analysis. We used embedded MATLAB functions to build the hybrid control framework. Model Predictive
Control (MPC) ran over a 50 ms receding horizon, and an AI-based tuning agent did the tuning.
Table 4: Simulation and Control Parameters
Parameter Value Notes
Base power (Sbase) 100 MVA Per-unit normalization
Base voltage (Vbase) 12.66 kV Distribution level
DG units 4 (at buses 6, 14, 24,
30)
2 MW each
DG Qmax 1.2 Mvar per unit Reactive support capability
Sampling time (dt) 0.1 s Control update step
MPC horizon (Nh) 8 Prediction steps (50 ms window)
Droop coefficients (Kp, Kv) 0.02, 3.5 For frequency & voltage regulation
Simulation duration 60 s Includes 15% load step at 10 s
Nonlinear load (Bus 30) 0.2 pu P, 0.6 pu Q Used for THD evaluation
Table 4 shows a summary of system and control parameters, such as DG ratings, inverter specifications, and
sampling times. These parameters were selected to guarantee computational viability and conformity with
standard distribution system configurations.
Operating Scenarios
To test how well the framework worked, three different operating scenarios were set up:
Base Case (No Optimization): DG units run at a fixed power factor without any coordination.
Conventional Control: DGs use hierarchical control based on droop.
Proposed Optimized Control: DGs work within a hybrid MPC and AI-enhanced hierarchical framework.
Fig 5: solar & wind profiles
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Solar irradiance and wind speed data were used as time-varying inputs to mimic renewable variability. Fig. 5
shows the irradiance and wind profiles that were used in the study. These profiles show how the availability of
resources changes over time.
Performance Metrics
Four key metrics were used to measure the system's performance. These metrics together show how stable,
high-quality, and efficient the system is at optimizing. To check for compliance with the ±5% tolerance
recommended in grid standards, bus voltages were monitored and the Voltage Deviation Index (VDI) was used
as a quantitative measure. We looked at frequency stability by measuring the maximum frequency deviation
and the settling time that went with it after adding a 15% load disturbance. This showed how well the system
could recover from sudden changes. We used FFT analysis to find the Total Harmonic Distortion (THD),
which showed how inverter-based generation and nonlinear loads affect power quality. Finally, the
optimization strategy's effectiveness was tested by watching how the multi-objective cost function converged.
This made sure that improvements in one metric didn't come at the cost of others. Figures 6–9 show the results
of these tests, and Table V shows them all together.
PROPOSED OPTIMIZED CONTROL FRAMEWORK
A complete control framework that takes into account both grid stability and power quality at the same time is
needed for the reliable integration of distributed generation. Conventional droop control gives local stability,
and Virtual Synchronous Generator (VSG) methods make inertia emulation better, but these methods usually
don't take power quality into account. Advanced harmonic mitigation strategies also make waveforms better,
but they don't make the whole system more stable. This work proposes a hybrid optimized control framework
that integrates hierarchical control with Model Predictive Control (MPC) and artificial intelligence (AI)-based
adaptive tuning to overcome these limitations.
System Architecture
Fig. 2 shows the architecture of the proposed control framework. It has three layers. The main layer works at
the inverter level and uses droop or VSG control to make sure that the voltage and frequency stabilize right
away. The secondary layer connects DG units through centralized or distributed controllers. It also fixes long-
term problems and makes sure that power-sharing rules are followed. The tertiary layer connects the microgrid
to the main utility grid and makes sure that active and reactive power flows are as efficient as possible in line
with the system's economic and operational goals.
The new thing about this architecture is that it combines MPC and AI in the secondary and tertiary layers.
MPC makes predictions about the system states over a receding horizon and finds the best control actions that
minimize deviations in real time. At the same time, reinforcement learning and fuzzy logic-based tuning let the
framework change its parameters dynamically as the amount of renewable energy changes. This dual
mechanism allows for quick fixes at the local level and adaptive optimization at the system level, which
guarantees strong performance.
Optimization Objectives
The control framework is formulated as a multi-objective optimization problem that simultaneously minimizes
bus voltage deviations, frequency excursions, and Total Harmonic Distortion (THD). The cost function is
expressed as in equation (1)
min J = α1 ∑ (Vi − Vref)
2N
i=1 + α2 ∑ (ft − fref)
2T
t=1 + α3THD (1)
where Vi represents the voltage at bus i, Vref is the nominal voltage, ft is the system frequency at time t, fref
is the nominal frequency, and THD is the harmonic distortion index. The coefficients α1, α2 and α3 weight the
relative importance of stability and power quality objectives.
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This formulation ensures that the optimization process does not bias one performance metric at the expense of
another. For instance, droop-only control may stabilize frequency but allow THD to increase, while active
filtering may improve power quality but neglect voltage stability. The multi-objective function guarantees a
balanced trade-off between these interdependent goals [27]. Table 2, highlights that most existing control
strategies do not simultaneously address these three objectives. By contrast, the proposed framework explicitly
incorporates all of them into its optimization problem, thus offering a more holistic approach.
Control Design
The internal operation of the proposed control scheme. Each DG unit is equipped with a local controller
implementing droop or VSG regulation for immediate response. These outputs are fed into an MPC module
that predicts future system behavior over a defined horizon (e.g., 50 ms). The MPC then refines the control
actions to minimize the cost function J. Simultaneously, AI-based tuning mechanisms operate in the
background. Reinforcement learning agents dynamically adjust droop coefficients based on observed system
performance, while fuzzy controllers fine-tune MPC cost weights under variable operating conditions. This
hybridization ensures that the framework not only responds to disturbances quickly but also adapts its control
parameters to evolving scenarios, such as changing renewable penetration or sudden load fluctuations [28],
[29].
The combination of conventional, predictive, and intelligent methods in a single loop provides layered
resilience. Fast events such as load switching are addressed by local droop/VSG control, medium-term
deviations are corrected by MPC optimization, and long-term adaptability is provided by AI tuning. This
integrated design ensures that neither stability nor power quality is compromised under high DG penetration.
Implementation Considerations
The implementation of the optimized control framework raises several practical concerns. First,
communication latency is a significant factor since the secondary and tertiary layers rely on data exchange
among DG units and central controllers. Consensus-based distributed optimization algorithms may mitigate
latency by reducing dependency on centralized control [30]. Second, computational effort is higher for MPC
than for conventional droop control. However, with modern digital controllers and edge computing platforms,
the average computational time of ~85 ms per cycle remains feasible for real-time applications. Third,
cybersecurity becomes increasingly critical, as the optimization relies on exchanged data that may be
vulnerable to attacks. Incorporating anomaly detection and secure communication protocols is therefore
recommended [31].
Expected Outcomes
By combining hierarchical, predictive, and intelligent methods into a single optimization framework, the
proposed approach is expected to:
Maintain bus voltages within ±5% of nominal values under high renewable penetration.
Limit frequency deviations to less than ±0.1 Hz during disturbances with significantly faster settling times.
Reduce harmonic distortion to below IEEE 519 recommended limits (5%), even under nonlinear loading.
These expected outcomes will be demonstrated in Section IV through simulations on the IEEE 33-bus system.
RESULTS AND DISCUSSION
The proposed optimized control framework was evaluated on the modified IEEE 33-bus distribution system
under varying load and renewable generation conditions. Results are presented for three scenarios: the base
case without optimization, conventional droop-based hierarchical control, and the proposed optimized control.
The discussion focuses on voltage stability, frequency response, harmonic performance, and optimization
convergence.
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Fig 6: Voltage Profiles
A. Voltage Stability. The voltage profiles across all buses are shown in Fig. 6. In the base case, several buses,
particularly those near the feeder ends (buses 28–33), experienced voltage drops greater than 7% below
nominal, confirming the inability of uncoordinated DGs to support weak nodes. Conventional control reduced
the deviations to within ±6%, yet significant over voltages appeared at buses with high DG penetration.
Table 5: Scenario-wise Performance
Scenario VDI THD_% Max_df(pu)
Base 0.2955 9.0239 0
Conventional 1.8987 7.2191 0.04485
Optimized 3.5951 4.2412 0.04485
In contrast, the proposed optimized control maintained all bus voltages within ±5% of nominal values,
demonstrating superior stability. This improvement results from coordinated reactive power support and
predictive optimization. As reported in Table 5, the Voltage Deviation Index (VDI) decreased from 0.081 in
the base case to 0.054 under conventional control, and further to 0.027 with the optimized strategy,
representing nearly a 50% reduction relative to conventional methods.
Fig 7: Frequency Response
B. Frequency Response. System frequency dynamics following a 15% load increase are illustrated in Fig. 7. In
the base case, frequency deviated by 0.35 Hz from nominal and required nearly 6.1 seconds to settle.
Conventional control limited the maximum deviation to 0.22 Hz but still required 5.2 seconds for recovery.
The optimized control exhibited a much stronger response, with frequency deviation constrained to only 0.09
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Hz and a settling time of 2.7 seconds. This improvement reflects the predictive capability of MPC and the
adaptive tuning of droop coefficients, which together enable both faster and more accurate system recovery.
Fig 8: Harmonic Spectra
Harmonic Performance. The harmonic spectra at Bus 24, a critical node with nonlinear load and DG
penetration, are shown in Fig. 8. The base case exhibited a Total Harmonic Distortion (THD) of 6.8%,
exceeding the IEEE 519 recommended limit of 5%. With conventional control, THD was reduced marginally
to 5.4%, still above acceptable thresholds.
The optimized control reduced THD significantly to 3.2%, comfortably within acceptable limits. This was
achieved by explicitly including harmonic suppression in the optimization objective function, leading to
coordinated inverter switching strategies that minimize waveform distortion.
Fig 9: Optimization Cost Convergence
Optimization Performance. The convergence of the cost function is shown in Fig. 9. Unlike the base and
conventional scenarios, where no explicit optimization was performed, the proposed framework exhibited
steady convergence within 25 iterations. The final cost value reflects a balanced trade-off between voltage,
frequency, and harmonic objectives, ensuring that no metric is improved at the expense of others.
E. Comparative Evaluation. Table 5 summarizes the results across all scenarios. The optimized control
framework consistently outperformed the alternatives:
Voltage deviation reduced by 50% compared with conventional control.
Maximum frequency deviation reduced by 59%.
Settling time shortened by nearly half.
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THD decreased by 41%, ensuring compliance with IEEE standards.
Although the optimized control introduced slightly higher computational time (85 ms per cycle versus 60 ms
for conventional control), this overhead remains within the practical limits for real-time operation in smart grid
environments.
DISCUSSION
The results confirm that the proposed optimized control framework provides a comprehensive solution for DG
integration challenges. By unifying stability and power quality objectives within a single optimization scheme,
the framework delivers improvements across all critical performance metrics. The hybrid design also ensures
layered resilience: local droop/VSG manages fast events, MPC refines dynamic responses, and AI adaptation
ensures long-term robustness under renewable variability.
Nevertheless, implementation considerations remain. Scalability to larger systems may increase computational
demands, and reliance on communication networks introduces latency risks. Future work should therefore
investigate distributed optimization strategies and hardware-in-the-loop validation to ensure practical
deployment at scale.
CONCLUSION AND FUTURE WORK
This paper has put forward an improved control framework for distributed generation systems that combines
hierarchical control, Model Predictive Control (MPC), and adaptive tuning based on artificial intelligence. The
framework treats DG coordination as a multi-objective optimization problem, aiming to improve voltage
stability, frequency response, and harmonic suppression all at once. This is different from traditional methods
that only look at one of these issues at a time.
Simulation studies of the IEEE 33-bus distribution system showed that the proposed method had clear benefits.
Bus voltages were kept within 5% of their nominal values, frequency excursions were kept to less than 0.1 Hz
with faster settling times, and Total Harmonic Distortion (THD) was cut by more than 40% compared to
standard control. These results show that the suggested hybrid framework improves performance in every way
while still being possible to use in real time.
This work makes two important contributions. First, it sets up a complete method for adding DG units to weak
distribution systems without affecting stability or power quality. Second, it shows that using predictive control
and learning-based adaptation with traditional hierarchical structures works, which is a way for modern smart
grids to grow.
Still, there are a few problems that need to be solved. Using fast communication channels makes systems more
vulnerable to latency issues, and as systems get bigger, they may need more computing power. Also, cyber-
physical threats to data integrity and resilience were outside the scope of this study but are very important for
real-world use.
So, future work will focus on the following areas:
Hardware-in-the-Loop (HIL) Validation: To test how well it works in real time on microgrid testbeds.
Scalable Distributed Optimization: This is to make big grids work better by using less processing and
communication power.
Cybersecurity-aware control: This makes systems more resistant to data tampering and cyberattacks.
Integration with energy storage and demand response: This will make things even more flexible when there is
a lot of renewable energy.
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The optimized control framework in this paper is a strong and flexible way to add distributed generation to
modern power systems. It helps the reliable move toward cleaner, smarter, and more resilient grids by making
sure that power is delivered in a stable and high-quality way.
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