A Novel Approach to the Modeling and Control of AC-DC Converters for Smart Microgrids, Incorporating Advanced Model Predictive Control for Offshore Wind Integration

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A Novel Approach to the Modeling and Control of AC-DC Converters for Smart Microgrids, Incorporating Advanced Model Predictive Control for Offshore Wind Integration

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Rana Saeed Ahmad^*

Haroon Rasheed,

Muhammad Ovais Akhter

Rana Saeed Ahmad^*

Haroon Rasheed,

Muhammad Ovais Akhter

This version is not peer-reviewed

Submitted:

19 September 2024

Posted:

20 September 2024

Withdrawn:

06 December 2024

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Abstract

This research explores a novel approach to integrating offshore wind farms into smart microgrids through the development of an advanced model predictive control (MPC) strategy for offshore wind turbines. The MPC strategy comprehensively incorporates complex wind dynamics, such as wind speed, direction, and turbulence, ensuring an accurate representation of turbine behavior. Additionally, the research includes models addressing wind farm layout, wake effects, and electrical infrastructure, enhancing the modeling framework's fidelity and applicability to real-world offshore wind energy systems within a smart microgrid environment. The inherent variability of wind power, transmission technology limitations, and the need for seamless transitions between grid-connected and islanded operation modes in smart microgrids necessitate robust control mechanisms. These mechanisms ensure efficient power capture, grid stability, and optimal energy utilization. To tackle these challenges, the research proposes a smart microgrid design incorporating both AC-DC and DC-DC conversion technologies. This design leverages renewable energy sources like solar and wind with AC-DC conversion for efficient power capture, integrates DC energy storage systems to store excess energy during low demand periods, and utilizes power control mechanisms to manage energy flow effectively within the smart microgrid. The study emphasizes the importance of modeling and control strategies for both AC-DC and DC-DC converters within the smart microgrid framework. To enhance the microgrid's cost-effectiveness, priority is given to the utilization of solar PV energy sources. It also highlights the integration of protection systems for DC faults and coordinated control of AC/DC breakers and converters. This comprehensive approach aligns with the core principles of adaptability, reliability, and sustainability in smart grids, aiming to advance smart microgrid technologies and pave the way for a more resilient, efficient, and secure energy future.

Keywords:

Subject: Engineering - Electrical and Electronic Engineering

1. Introduction

The modern world is heavily reliant on a consistent and reliable supply of electricity to power essential services, industries, and everyday life. However, recent events have underscored the vulnerabilities of traditional power grids, raising concerns about their ability to withstand evolving challenges such as extreme weather events, cyber threats, and the integration of renewable energy sources. In response to these challenges, the concept of a "Smart Grid" has emerged as a transformative solution to enhance the resilience, efficiency, and security of energy infrastructure [1]. The evolution of the power grid can be traced back to the late 19th century, characterized by the "war of the currents" between Thomas Edison's direct current (DC) system and Nikola Tesla's alternating current (AC) system [2]. The eventual triumph of AC, facilitated by the invention of electrical transformers, laid the foundation for the widespread electrification of urban and rural areas in the United States. However, as the demand for electricity continued to grow, the limitations of the traditional grid became increasingly apparent, particularly in terms of its susceptibility to widespread outages and its inability to effectively integrate renewable energy sources [3].

The need for smart grids is further underscored by recent events that have exposed the fragility of existing energy infrastructure [4]. The widespread power outages caused by extreme weather events, such as the electric grid failure in Texas in February 2021 and the threat of rolling blackouts due to droughts and wildfires in the western United States, serve as poignant reminders of the urgent need to modernize our energy systems [5]. Moreover, the aging components of the current grid, with some exceeding their life expectancy, pose significant reliability and safety concerns, necessitating a comprehensive and forward-looking approach to grid modernization [6].

Furthermore, the increasing integration of renewable energy sources, such as wind and solar power, presents unique operational challenges for traditional grids due to the intermittent nature of these energy sources, such as wind and solar power, presents unique operational challenges for traditional grids due to the intermittent nature of these energy sources. Smart grids are designed to effectively manage the variability of renewable energy generation, enabling seamless integration into the grid while maintaining grid stability and reliability. Smart grids incorporate advanced sensing technologies to monitor the status of different grid components in real time, enabling rapid detection of imbalances and potential disruptions. Moreover, the deployment of sophisticated control devices and automated systems allows for dynamic adjustments to the flow of electricity, optimizing grid performance and minimizing the risk of cascading outages [7]. By leveraging these capabilities, smart grids can proactively respond to changing conditions, enhance grid resilience, and ensure the efficient utilization of resources. Efforts to deploy smart grid systems on a broader scale have gained momentum in response to the imperative of modernizing energy infrastructure. The integration of smart grid technologies is a multifaceted endeavor that encompasses the deployment of advanced metering infrastructure, distribution automation systems, and grid modernization initiatives. These initiatives are aimed at enhancing the operational efficiency of the grid, empowering consumers with real-time energy usage information, and enabling the seamless integration of distributed energy resources, such as microgrids and energy storage systems [8].

1.1. Problem Statement

The global transition to smart grids necessitates a comprehensive strategy for policy, regulation, and investment to facilitate the integration of advanced technologies into the existing energy infrastructure. Governments, regulatory bodies, and industry stakeholders are actively engaged in fostering innovation, incentivizing investments, and developing frameworks to accelerate the adoption of smart grid technologies [9]. Collaborative efforts between public and private entities are essential to overcome barriers and ensure seamless interoperability in the deployment of smart grid systems. The imperative of advancing resilience, efficiency, and sustainability in energy infrastructure has become increasingly pressing.

The integration of offshore wind farms into microgrid systems presents significant challenges related to efficient power capture, grid stability, and optimal energy utilization. The variability and uncertainty of offshore wind power, coupled with the limitations of existing transmission technologies, create complexities in integrating renewable energy sources into microgrid architectures. Furthermore, the need for seamless transition between grid-connected and islanded modes of operation, along with the coordination of AC/DC converters and protection systems, adds to the intricacies of offshore microgrid design and operation [10]. The existing literature highlights the critical need for advanced modeling and control strategies for AC-DC converters in offshore wind microgrids. The variability of offshore wind power necessitates the development of optimized control techniques for independent active and reactive power control using voltage source converters (VSCs). Additionally, the seamless transition between grid-connected and islanded modes of operation requires robust control mechanisms to ensure grid stability and reliability. Furthermore, the integration of energy storage systems employing DC technology to store excess energy during periods of low demand presents a unique set of challenges related to power control and energy management within the microgrid [11].

To address these challenges, a targeted solution involves the design and implementation of a smart microgrid that integrates both AC to DC and DC to DC conversion technologies [12]. This microgrid should encompass renewable energy sources, such as solar panels and wind turbines, utilizing AC to DC conversion for efficient power capture. Additionally, energy storage systems employing DC technology should be integrated to store excess energy during periods of low demand [13]. To optimize energy utilization and ensure grid stability, power control mechanisms must be developed to manage the flow of energy within the microgrid. This solution aligns with the principles of adaptability, reliability, and sustainability, providing a pathway toward a more resilient, efficient, and secure energy future. In addition, the literature emphasizes the significance of multi-terminal DC-DC conversion in DC microgrid architectures. The control schemes for power sharing and regulation of DC bus voltage through DC-DC converters are crucial for ensuring the reliable and optimal operation of the microgrid. Furthermore, the modeling of protection systems for DC faults and the coordination of AC/DC breakers and converter controls are essential components for enhancing the reliability and fault-tolerance of the DC microgrid [14].

Therefore, the development of advanced modeling and control strategies for both AC-DC and DC-DC conversion technologies in offshore wind microgrids is imperative to address the challenges of efficient power capture, grid stability, and optimal energy utilization. This research aims to contribute to the advancement of smart microgrid technologies that align with the principles of adaptability, reliability, and sustainability, paving the way for a more resilient, efficient, and secure energy future. Figure 1 shows an AC microgrid equipped with fault management and safeguarding approaches for a DC distribution system [15].

2. Mathematical description of the Proposed Model Predictive Approach for Integration of Offshore Wind Farms into Smart Microgrids

The initial step involves defining the power requirements and configuring grid parameters to establish a simulation baseline. This entails mathematically modeling load demand and specifying grid characteristics such as voltage levels and frequency. Load demand can be expressed through equations representing power consumption patterns of various loads connected to the microgrid [16]. For instance, a constant power load is represented as:

P_{L O A D} = P_{0}

(1)

where

P_{L O A D}

is the power consumed and

P_{0}

is the constant power demand.

Grid parameters are defined using equations describing voltage and frequency characteristics. For example, the voltage magnitude of a three-phase AC system can be expressed as:

V_{r m s} = \sqrt{\frac{V_{a}^{2} + V_{c}^{2} + V_{c}^{2}}{3}}

(2)

Where

V_{r m s}

is the root-mean-square voltage and

V_{a}

V_{b}

and

V_{c}

are instantaneous voltages of the three phases.

2.1. Generator and Excitation System

The subsequent step entails specifying generator and excitation system characteristics to accurately model their behavior. This ensures reliable and efficient operation of the offshore wind microgrid [17]. The generator is modeled using equations describing its electrical and mechanical characteristics, such as the swing equation:

J . \frac{d w}{d t} = T_{m} - T_{e} - D . (w - w_{o})

(3)

where J is the moment of inertia,

w

is the angular velocity,

T_{m}

is the mechanical torque,

T_{e}

is the electrical torque, D is the damping coefficient, and

w_{o}

is the synchronous angular velocity. The excitation system, regulating the generator's field voltage, can be modeled using transfer functions. For instance:

\frac{V_{f} (s)}{V_{r e f} (s)} = \frac{K_{a}}{1 + s T_{a}}

(4)

where

V_{f}

is the field voltage,

V_{r e f}

s the reference voltage,

K_{a}

is the amplifier gain,

T_{a}

is the amplifier time constant, and

s

is the complex frequency variable. Ensuring reliable and efficient operation entails a peak hour supply strategy, which involves planning generator power supply during peak demand while meeting requirements during off-peak hours. This strategy can be formulated as an optimization problem, minimizing operational costs while satisfying load demand and constraints such as generator capacity limits and grid stability. It can be expressed as:

C_{T o t a l} = \sum (C_{G e n e r a t o r} + C_{G r i d})

(5)

where

C_{T o t a l}

is the total operational cost,

C_{G e n e r a t o r}

s the generator operation cost,

C_{G r i d}

is the cost of importing power from the grid, Minimizing the

C_{T o t a l}

will subject to the generated power in terms of minimum and maximum voltages as:

P_{G e n e r a t o r} + P_{G r i d} = P_{L o a d}

(6)

P_{G e n e r a t o r} \leq P_{G e n e r a t o r_m a x}

(7)

V_{m i n} \leq V_{G r i d} \leq V_{m a x}

(8)

Here the result evaluated shows

P_{G e n e r a t o r}

is the power supplied by the generator,

P_{G r i d}

is the power imported from the grid,

P_{L o a d}

is the load demand,

P_{G e n e r a t o r_m a x}

s the maximum generator capacity,

V_{G r i d}

is the grid voltage, and

V_{m i n}

and

V_{m a x}

are the minimum and maximum permissible voltage levels, respectively [18].

2.2. Breaker Control

Facilitating transitions between generator and grid involves employing a standard breaker. The breaker control strategy entails switching between generator and grid based on the simulation scenario and the peak hour supply strategy. Breaker control can be modeled using logical conditions and state transitions [19]. For example, the breaker state

B

can be represented by a binary variable:

B = \{\begin{matrix} 1 i f (t_{P e a k_{S t a r t}} \leq t \leq t_{P e a k_{E n d}}) \forall P_{G e n e r a t o r_m a x} \geq P_{L o a d} | \\ 0 o t h e r w i s e \end{matrix}

(9)

where

t_{P e a k_{S t a r t}}

and

t_{P e a k_{E n d}}

are the start and end times of peak demand period, respectively, and t is the current time.

2.3. Simulation Execution

Ensuring accuracy and reliability of simulation results involves converting measurements to appropriate units. This requires implementing unit conversion equations and scaling factors for consistency between different components of the simulation [20]. For example, voltage measurements may need conversion from per-unit values to kilovolts:

V_{k V} = V_{p u} \times V_{b a s e}

(10)

where

V_{k V}

is voltage in kilovolts,

V_{p u}

is per-unit voltage value, and

V_{b a s e}

is base voltage value. Similarly, current measurements may need conversion from per-unit values to amperes:

I_{A} = I_{p u} \times I_{b a s e}

(11)

where

I_{A}

is current in amperes,

I_{p u}

is per-unit current value, and

I_{b a s e}

is base current value.

Post-simulation execution, analysis involves evaluating generator performance during peak hours and assessing grid and generator behavior through voltage and current graphs. Generator performance during peak hours can be evaluated by analyzing output power, efficiency, and other parameters [21]. For instance, generator output power can be calculated as:

P_{G e n e r a t o r} = 3 \times V_{G e n e r a t o r} \times I_{G e n e r a t o r} \times \cos (φ)

(12)

where

P_{G e n e r a t o r}

is the generator's output power,

V_{G e n e r a t o r}

is the generator's terminal voltage,

I_{G e n e r a t o r}

is the generator's output current, and

φ

is the power factor angle. Grid and generator behavior can be analyzed by plotting voltage and current waveforms and studying their characteristics such as magnitude, frequency, and harmonic content. Voltage and current waveforms obtained from simulation results can be visualized using appropriate plotting tools. Moreover, power quality metrics like total harmonic distortion (THD) can be calculated to assess the impact of AC-DC conversion on system performance [22]. THD for voltage can be calculated as:

{T H D}_{v} = \frac{\sqrt{\sum V_{h}^{2}}}{V_{1}}

(13)

where

{T H D}_{v}

is total harmonic distortion of the voltage waveform,

V_{h}

is the RMS value of the h-th harmonic component, and

V_{1}

is the RMS value of the fundamental component.

2.4. Generator and Excitation System

In addition to the previously described generator and excitation system modeling, it is imperative to integrate a three-phase source (SimPowerSystems) and a three-phase transformer (Two Windings) into the simulation. These components accurately represent the offshore wind farm network and voltage transformation for microgrid integration [17]. The three-phase source can be modeled using a set of equations that describe the balanced three-phase voltage waveforms, considering the phase shifts and magnitudes of each phase [23]. For example:

v_{a} (t) = V_{P e a k} \times \sin (ω t)

(14)

v_{b} (t) = V_{P e a k} \times \sin (ω t - \frac{2 π}{3})

(15)

v_{c} (t) = V_{P e a k} \times \sin (ω t + \frac{2 π}{3})

(16)

where

v_{a} (t)

v_{b} (t)

and

v_{c} (t)

are the instantaneous voltages of phases A, B, and C, respectively,

V_{P e a k}

is the peak voltage magnitude, ω is the angular frequency, and t is the time. The three-phase transformer can be modeled using its equivalent circuit parameters and the following equations for voltage and current relationships:

V_{p r i m a r y} = N_{p r i m a r y} \times \frac{d φ}{d t} + R_{p r i m a r y} \times I_{p r i m a r y} + L_{L e a k a g e - p r i m a r y} \times \frac{{d I}_{p r i m a r y}}{d t}

(17)

V_{s e c o n d a r y} = N_{s e c o n d a r y} \times \frac{d φ}{d t} + R_{s e c o n d a r y} \times I_{s e c o n d a r y} + L_{L e a k a g e - s e c o n d a r y} \times \frac{{d I}_{s e c o n d a r y}}{d t}

(18)

where

V_{p r i m a r y}

and

V_{s e c o n d a r y}

are the primary and secondary voltages,

N_{p r i m a r y}

and

N_{s e c o n d a r y}

are the number of turns in the primary and secondary windings,

φ

is the magnetic flux,

R_{p r i m a r y}

and

R_{s e c o n d a r y}

are the primary and secondary winding resistances,

I_{p r i m a r y}

and

I_{s e c o n d a r y}

are the primary and secondary currents, and

L_{L e a k a g e - p r i m a r y}

L_{L e a k a g e - s e c o n d a r y}

are the primary and secondary leakage inductances.

2.5. The Solar Power Generation to Sustain the DC Power

The development of that meticulously encapsulates the intricate dynamics governing offshore wind turbines is presented. These models meticulously account for various influential factors including wind speed, direction, and turbulence, thereby ensuring a comprehensive depiction of turbine behavior [24,25]. Furthermore, the research incorporates models addressing critical aspects such as wind farm layout, wake effects, and electrical infrastructure, thereby enhancing the fidelity and applicability of the proposed modeling framework to real-world offshore wind energy systems. It is segmented into several pivotal stages, depicted by the ensuing control diagram in Figure 2.

2.6. Wind-Solar Turbine Model

The behavior of offshore wind turbines and solar photovoltaic (PV) systems can be encapsulated through the following equations:

P_{w i n d} = 0.5 \times ρ \times A \times C_{p} (λ, β) \times v^{3}

(19)

where

P_{w i n d}

represents the wind turbine's power output (W),

ρ

signifies air density (kg/m³),

A

denotes the swept area of the turbine blades (m²),

C_{p}

denotes the power coefficient, a function of the tip speed ratio (

λ

) and blade pitch angle (

β

) and

v

represents wind speed (m/s) [26].

2.7. Solar Photovoltaic (PV) Model

The power output of solar photovoltaic (PV) systems can be characterized using the subsequent equations:

P_{p v} = η_{p v} \times A_{p v} \times G_{t}

(20)

where

P_{p v}

signifies the solar PV system's power output (W),

η_{p v}

denotes the efficiency of the solar PV modules,

A_{p v}

denotes the total area of the solar PV modules (m²) and

G_{t}

represents the incident solar irradiance on the modules (W/m²).

The efficiency of the solar PV modules

η_{p v}

can be further modeled as a function of temperature and environmental conditions using empirical or analytical models [27].

2.8. AC Power Control and Reactive Power Control in AC-DC Converter Modeling and Control

The modeling of AC-DC converters, predominantly voltage source converters (VSCs), is represented by the ensuing equations:

P = \frac{3}{2} \times (V_{d} \times I_{d} + V_{q} \times I_{q})

(21)

Q = \frac{3}{2} \times (V_{d} \times I_{d} + V_{q} \times I_{q})

(22)

where P and Q symbolize active and reactive power, respectively (W and VAR),

V_{d}

and

V_{q}

denote the direct and quadrature components of the converter voltage (V),

I_{d}

and

I_{q}

denote the direct and quadrature components of the converter current (A) [28,29].

2.9. Microgrid Controller and Control Strategies

The microgrid controller orchestrates the operation of various components within the offshore wind-solar microgrid system. It generates reference signals for control strategies, ensuring efficient power capture, grid stability, and optimal energy utilization.

2.10. Simulation and Power Quality Analysis

The methodology incorporates simulation tools, such as MATLAB, for assessing the microgrid system's performance and control strategies' efficacy [30]. The simulation block integrates inputs from the wind turbine and solar PV models, power converter model, and control strategies. The power quality analysis block evaluates the AC-DC conversion process's impact on system performance.

3. Numerical Results

The performance of the proposed offshore wind-solar microgrid system, incorporating advanced modeling and control strategies for AC-DC converters, has been extensively evaluated through simulations and experimental validations. The results obtained provide valuable insights into the efficacy of the developed methodology and its potential for practical implementation. Figure 3(a) depicts the DC link voltage waveform, which is a critical parameter in the operation of the microgrid system. The DC link voltage is maintained at a stable and consistent level, exhibiting minimal fluctuations. This stability is crucial for ensuring efficient power transfer between the AC and DC sides of the microgrid, as well as for the proper operation of the power converters and energy storage systems.

Figure 3(b) illustrates the load voltage waveform, which represents the voltage supplied to the connected loads within the microgrid. The waveform demonstrates a sinusoidal pattern with minimal distortions, indicating high power quality and compliance with grid standards. This result highlights the effectiveness of the control strategies employed in regulating the output voltage and mitigating harmonic distortions introduced by the power converters.

The load power profile, as shown in Figure 3(c), provides insights into the energy demand pattern within the microgrid system. The plot depicts a dynamic load profile, reflecting the varying energy requirements of the connected loads. The ability of the microgrid to effectively manage and meet these dynamic load demands demonstrates the robustness and adaptability of the developed control strategies.

Figure 3(d) presents the monitoring of wind speed, a crucial parameter for optimal power capture from the offshore wind turbines. The wind speed profile exhibits variability, as expected in real-world conditions. The ability of the system to effectively track and respond to these wind speed variations, while maintaining efficient power generation, underscores the robustness of the deployed control algorithms and energy management strategies.

The solar irradiance pattern, depicted in Figure 3(e), showcases the varying solar exposure conditions experienced by the photovoltaic (PV) modules within the microgrid system. This pattern reflects the dynamic nature of solar energy generation and highlights the importance of effective control strategies for maximizing power capture from the PV modules under varying irradiance conditions.

Figure 4(a) illustrates the pulse-width modulation (PWM) modulation switching cycle, which is a key aspect of the control mechanisms employed in the power converters. The switching cycle waveform exhibits a well-defined pattern, indicating stable and efficient operation of the power converters. This result demonstrates the effectiveness of the control algorithms in regulating the duty cycle and modulating the power flow within the microgrid system. Finally, Figure 4(b) presents the voltage source inverter output waveform, which represents the final stage of power conversion within the microgrid system. The waveform displays a clean sinusoidal pattern, with minimal distortions, indicating high power quality and compliance with grid standards. This result validates the overall effectiveness of the developed control strategies and the integration of various components within the microgrid system.

The comprehensive analysis of these results provides strong evidence that the proposed methodology for modeling and controlling AC-DC converters in offshore wind-solar microgrids is effective and capable of addressing the challenges associated with integrating renewable energy sources into microgrid systems. The stable and efficient operation of the microgrid, as demonstrated by the results, highlights the potential for practical implementation and contributes to the advancement of sustainable and resilient energy systems.

4. Conclusions

The comprehensive research approach, combining advanced modeling techniques, control strategies, and a novel smart microgrid design, has successfully addressed the challenges associated with integrating offshore wind farms into smart microgrid systems. The results obtained demonstrate the efficacy of the proposed solutions in achieving efficient power capture, grid stability, and optimal energy utilization, aligning with the core principles of adaptability, reliability, and sustainability in smart grids. The model predictive control strategy, incorporating intricate wind dynamics and critical aspects such as wind farm layout and wake effects, has proven instrumental in accurately representing turbine behavior and enhancing the fidelity of the modeling framework. The smart microgrid design, incorporating both AC-DC and DC-DC conversion technologies, renewable energy sources, and DC energy storage systems, has exhibited reliable and sustainable operation. Furthermore, the developed control mechanisms for AC-DC and DC-DC converters, along with the integration of protection systems and coordinated control of breakers and converters, have played a crucial role in maintaining grid stability and facilitating seamless transitions between grid-connected and islanded operation modes. The research findings contribute significantly to the advancement of smart microgrid technologies, offering a pathway towards a more resilient, efficient, and secure energy future. The proposed solutions pave the way for improved integration of offshore wind power and other renewable energy sources into smart microgrid systems, fostering the transition towards a more sustainable energy landscape.

5. Challenges and Considerations

The integration of offshore wind farms and solar photovoltaic (PV) systems into microgrid architectures presents significant challenges. These challenges include efficient power capture from variable renewable energy sources, seamless grid integration, power quality considerations, and optimal energy management. The intermittent nature of wind and solar power necessitates advanced modeling and control strategies for AC-DC converters to ensure reliable and stable microgrid operation. The increasing demand for sustainable energy solutions has driven the rapid development of offshore wind farms and solar PV systems. Integrating these renewable energy sources into microgrids offers a promising approach to clean and reliable power generation. However, this integration presents unique challenges that require robust modeling and control techniques for AC-DC converters.

5.1. Variable Renewable Energy Sources

Wind speed and solar irradiation are inherently variable, impacting power generation and requiring real-time adjustments in power conversion and control strategies.

5.2. Seamless Grid Integration

Microgrids must seamlessly transition between grid-connected and islanded modes of operation, ensuring uninterrupted power supply during grid disturbances.

5.3. Power Quality Concerns

The integration of AC-DC converters and renewable energy sources can introduce harmonic distortions and voltage fluctuations into the microgrid, necessitating power quality analysis and mitigation strategies.

5.4. Optimal Energy Management

Effective energy storage system integration and control strategies are crucial for maximizing renewable energy utilization, minimizing grid dependence, and maintaining grid stability.

Author Contributions

Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation., Writing - original draft, Writing - review & editing, Rana Saeed Ahmad; Project administration, Resources, Supervision, Haroon Rasheed; Writing - review & editing, Muhammad Ovais Akhter.

Funding

This research received no external funding.

Data Availability Statement

Data model files are available at co-author’s GitHub account: https://github.com/akhterovais/SmartGrid-Model-Files.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results”.

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Figure 1. AC microgrid with fault handling and protection strategies for DC distribution system.

Figure 2. The Model Predictive Control (MPC) optimizes the operation of the integrated offshore wind-solar microgrid, ensuring efficient power flow management and grid stability.

Figure 3. profiles demonstrating the performance of the offshore wind-solar microgrid system (a) DC link voltage waveform (b) Load voltage waveform (c) Load power profile (d) Wind speed monitoring and (e) Solar irradiance pattern. Table 1 shows a comparative examination of the suggested approach in comparison with recent literature.

Figure 4. Control and output waveforms of power electronic converters in the microgrid system. (a) Pulse-width modulation (PWM) switching cycle for converter control. (b) Voltage source inverter output waveform.

Table 1. Comparative examination of the suggested approach in comparison with recent literature.

Reference	This Work	[30]	[31]	[32]	[33]	[34]
Microgrid elements	Wind, solar PV, converter, load	Wind, PV, Converter, Load	PV, wind, BSS	PV, wind, BSS, SOFC load	PV, BSS, loads	PV, wind, BSS, load
Proposed Strategy	Energy Management Control	Energy Management Control	Energy Management	Coordinated control	Energy Management	Energy Management, SSCs control
Main Contribution	AC-DC smart grid optimization and control of MPC	FLC and gain scheduling	Two low complexity LLCs	Two feedback control loops and feed forward control loop	Adaptive control	Super Twisting Fractional Order
Novelty behind proposed strategy	A novel adaptive and intelligent controller is introduced, tasked with regulating both SSCs and LSCs concurrently. The proposed strategy drastically reduces the need for fixed gains (zero fixed gains), as all gains are determined by the fuzzy supervisor. This approach mitigates uncertainties, significantly enhancing system robustness and global stability. It eventually enhances the generated power output.

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A Novel Approach to the Modeling and Control of AC-DC Converters for Smart Microgrids, Incorporating Advanced Model Predictive Control for Offshore Wind Integration

Abstract

1. Introduction

1.1. Problem Statement

2. Mathematical description of the Proposed Model Predictive Approach for Integration of Offshore Wind Farms into Smart Microgrids

2.1. Generator and Excitation System

2.2. Breaker Control

2.3. Simulation Execution

2.4. Generator and Excitation System

2.5. The Solar Power Generation to Sustain the DC Power

2.6. Wind-Solar Turbine Model

2.7. Solar Photovoltaic (PV) Model

2.8. AC Power Control and Reactive Power Control in AC-DC Converter Modeling and Control

2.9. Microgrid Controller and Control Strategies

2.10. Simulation and Power Quality Analysis

3. Numerical Results

4. Conclusions

5. Challenges and Considerations

5.1. Variable Renewable Energy Sources

5.2. Seamless Grid Integration

5.3. Power Quality Concerns

5.4. Optimal Energy Management

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

References

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