A New Power-Sharing Strategy with Photovoltaic Farms and Concentrated Diesel Generators to Increase Power System Resilience

Abstract

This paper provides a power-sharing strategy designed for an islanded grid that becomes isolated from the main grid due to faults or natural disasters. The proposed topology is introduced for post-disaster scenarios where the restoration process may be time-consuming (from a few hours to a few months). This system is equipped with a step-by-step power-sharing strategy based on priority, inputs from photovoltaic sources, and a diesel generator to enhance reliability. The DC–AC inverter control and AC–DC–AC control for the diesel generator are presented, which provide flexible real and reactive control. The system is divided into three priority load areas, and power sharing is conducted based on these priorities. The distinctive feature of the proposed strategy lies in its ability to manage power-sharing under different power generation conditions, prioritizing critical loads. The proposed method is implemented using the MATLAB Simulink environment. Simulation studies are performed under different solar irradiances and variations in the diesel generator’s output to validate the performance of the proposed method. The results demonstrate the practicality of the proposed algorithm in harnessing renewable resources and diesel generators and dynamically managing the energy consumption in loads.

1. Introduction

The issue of global warming has given rise to numerous challenges for humans. One of the most significant consequences is the rise in harsh weather conditions and destructive events. These natural disasters cause significant damage to critical manmade infrastructure, and they make everyday life exceptionally difficult. Among the critical infrastructure that is significantly affected is the power grid. The process of restoring the power grid after such catastrophic events can vary from a few hours to a few months, depending on the extent of the damage; however, one promising solution to address this situation is the implementation of renewable energy and a reliable source, such as non-renewable energy sources like a diesel generator, as a backup system within islanded power grids. This approach promotes environmental cleanliness due to the use of renewable energy sources and ensures reliable energy generation due to the use of non-renewable energy sources such as diesel generators.

By reducing the dependence on traditional power grids and depending on clean energy sources such as photovoltaics and wind turbines, it is possible to decrease the adverse impact on the environment and promote stable energy. Furthermore, the reliability of these systems guarantees a stable power supply even during disruptions such as severe weather events. Islanded power grids adopt a decentralized model, generating power locally and closer to consumers, thereby enhancing the grid’s resilience and facilitating a faster recovery process in the face of damage and outages. The combination of renewable and non-renewable energy sources in islanded power grids presents a compelling solution to address these challenges, offering a more resilient and sustainable energy supply. In the context of limited power supplies, power sharing becomes a critical aspect of these types of topologies. After a natural disaster, power grids can suffer extensive damage, leading to prolonged outages that hinder recovery efforts. Restoring these grids can take from a few hours to several months, depending on the severity of the damage, necessitating a reliable backup power solution until the restoration of the damaged grid. Thus, this power-sharing strategy can help crucial loads to be fed during the restoration process.

Various strategies have been proposed to overcome these challenges. Reference [1] presents an expanded review of hybrid power systems for off-grid locations, examining the technologies, designs, and applications globally, and offers insights for future research and improvements in system reliability for locations remote from the main grid. In Refs. [2,3], the authors contribute to hybrid power systems for off-grid electrification, proposing an optimized multi-input DC–DC boost converter for smart homes and designing an improved power electronics system for renewable energy management in remote areas. Reference [4] provides microgrid management by introducing a decentralized current-sharing strategy using iterative virtual impedance regulation, increasing the current-sharing accuracy and voltage quality in islanded microgrids. This paper offers a technical framework for impedance management that ensures reliable power distribution across different types of loads, without the need for inter-unit communication, focused on prioritizing energy.

In Ref. [5], the authors present the modeling and simulation of a hybrid microgrid system integrating PV, wind, and diesel generators and demonstrate effective control strategies for stable operation under both grid-connected and islanded conditions. The control method described in their paper for the hybrid PV–wind–diesel microgrid system utilizes integrated MPPT and voltage droop controls in the PV inverter, alongside a droop governor and AVR in the diesel generator, to ensure stable operation under both grid-connected and islanded conditions. In Ref. [6], the authors introduce a robust energy management and control system for a hybrid microgrid, incorporating fuel cells, photovoltaic cells, and supercapacitors to optimize day-ahead power scheduling, enhance the microgrid’s resilience, and improve the power stability under both grid-connected and islanded conditions.

Reference [7] presents a novel method for the optimization of the sizing of hybrid photovoltaic–diesel power systems, focusing on minimizing the photovoltaic array area and storage battery size, incorporating pre-operating times for diesel generators, and enhancing the system’s economic efficiency through a developed FORTRAN-based sizing program. In Ref. [8], the authors develop an innovative approach to enhance distribution systems’ resilience against extreme weather by integrating distributed energy resources (DERs) and battery electric vehicles (BEVs) into prosumer-centric microgrids, using a novel optimization algorithm to effectively manage these resources for improved resilience and reliability. In Ref. [9], the authors present a techno-economic feasibility analysis of a grid-connected hybrid energy system combining solar, wind, and hydrogen technologies to meet the energy demands of a single-family home in Sofia, Bulgaria, with an emphasis on optimizing energy efficiency and reducing greenhouse gas emissions.

A comprehensive review of proactive resilience strategies for power systems against natural disasters is provided in [10], focusing on the integration of microgrids and renewable energy resources to enhance system robustness. The authors of [11] conduct a techno-economic feasibility study on a photovoltaic diesel hybrid system for rural electrification in Zambia, revealing that while diesel generators alone are not economically viable due to high lifecycle costs, a PV system with batteries, despite its high upfront costs, offers lower long-term costs and is more sustainable. Reference [12] proposes decentralized renewable hybrid mini-grid systems as a sustainable solution to electrify remote coastal areas in Bangladesh, demonstrating through HOMER simulations that these systems can deliver high-quality electricity at competitive costs by leveraging local renewable energy resources. In Ref. [13], the authors present an energy management strategy for a net-zero-emission islanded PV microgrid that integrates a fuel cell, battery, electrolysis, and hydrogen storage. It employs the equivalent consumption minimization strategy (ECMS) and fuzzy logic-based MPPT for efficient power sharing and stability. This approach enhances the system’s resilience and sustainability and is suitable for isolated microgrids. In Ref. [14], the authors present the techno-economic planning of an off-grid renewable energy-based microgrid for EV charging, integrating wind turbines, solar panels, and hybrid energy storage systems (ESSs). The study introduces a modified cost of energy index to optimize decision-making. It demonstrates the benefits of hybrid ESSs in reducing costs and enhancing the system’s efficiency and feasibility. Reference [15] investigates the design, optimization, and performance of a hybrid stand-alone microgrid integrating PV modules, BESSs, and diesel generators. Using the PVsyst, HOMER Pro, and SAM software, it evaluates the system’s feasibility, efficiency, and environmental impact, highlighting significant CO₂ emission reductions. The study also addresses operational challenges to enhance the reliability and sustainability of the microgrid.

In Ref. [16], the authors review the challenges of real-time power management in microgrids, which are essential in integrating distributed energy resources (DERs) into future power networks. It highlights the importance of a multi-agent-based control architecture designed to optimize microgrid operations according to various objectives, including power demands, fuel consumption, environmental emissions, and costs. The authors propose a qualitative classification tool to aid system planners in assessing the impact of microgrid systems on the broader grid. The paper emphasizes the need for robust and autonomous power management methods to ensure the stable, efficient, and optimal operation of microgrids, addressing both internal coordination and external interconnection challenges. Reference [17] provides a detailed review of advanced control architectures for intelligent microgrids, highlighting decentralized and hierarchical control techniques. The paper discusses the implementation of decentralized control to manage distributed energy resources effectively, focusing on the benefits of local measurement-based control to ensure stability and efficiency in microgrid operations. The authors also address the challenges associated with traditional droop control methods and propose advanced solutions like virtual impedance control to improve power sharing and stability. Furthermore, the paper outlines a hierarchical control framework, incorporating primary, secondary, and tertiary control levels to optimize the performance and resilience of microgrids, ensuring smooth transitions between grid-connected and islanded modes.

Reference [18] proposes a conceptual solution for microgrids, emphasizing the system approach where generation and associated loads operate as a subsystem. This approach allows the microgrid to disconnect from the main grid during disturbances, maintaining high local reliability without compromising the transmission grid’s integrity. The authors highlight the benefits of intentional islanding, which enhances the local reliability and optimizes the use of waste heat, thereby increasing the overall system efficiency.

In this study, it is assumed that, before a disaster, the renewable energy system serves as a supplementary source for the main grid. In the aftermath of a disaster, the connection between the main grid and the islanded grid is severed. In this isolated state, the system must prioritize feeding the loads. A novel power-sharing structure is proposed for the post-disaster scenario. Two distinct sources, including a PV generator and a diesel generator, are harnessed to supply power to the loads.

2. Methodology and Proposed Structure

The proposed microgrid system employs a comprehensive approach to tackle challenges associated with power sharing in renewable energy-based systems. In the dynamic environment of such systems, where energy production is influenced by factors like sun radiation, the wind velocity, and the temperature, innovative strategies become imperative. The proposed strategy aims to enhance the resilience and efficiency of the microgrid. Figure 1a depicts a grid-connected condition, where the microgrid maintains its connection to the main grid. All loads are serviced by the main grid, the energy from the photovoltaic (PV) farm is fed to the grid, and the diesel generator is disconnected.

In adverse conditions, such as severe weather events, the microgrid showcases its ability to operate independently by disconnecting from the main grid. It relies on its own energy generation, ensuring an uninterrupted power supply even in challenging circumstances. Figure 1b illustrates the grid-disconnected operation condition of the microgrid. In this case, the loads rely on PV and a diesel generator. The diesel generator is used to supply power to the most critical load, while other loads with lower priority can utilize PV farms. Moreover, PV acts as a backup for critical loads in overload situations and bolsters the microgrid’s self-sufficiency.

The system dynamically adjusts parameters like the frequency and voltage to maintain its stability, making real-time adjustments to the generator and inverter. Power sharing within the microgrid is managed based on the energy produced at the AC bus. The system follows a prioritized approach, with the generator taking the first priority to supply critical loads reliably. The second priority allows for the flexible allocation of power based on the available generation capacity and load demand. The third priority enables additional power distribution while maintaining the overall system stability.

Figure 2 shows the algorithm for the power sharing and local and central control of the proposed islanded microgrid. The diesel generator supplies power to the first-priority load, and, if the diesel generator cannot fully satisfy demands of the first-priority load, the PV farm will also supply power to the first-priority load. The PV farms then supply power to the second-priority and third-priority loads.

In Figure 2, the PV system is connected to a DC–DC converter, and, under normal conditions, the on-grid inverter feeds the main grid, which then supplies the grid. When a natural disaster occurs, the system disconnects from the main grid, and commands are directed to the diesel generator and inverter to start and change the power flow path to the AC bus, prioritizing load feeding based on criticality. The local control system introduces a binary mechanism for the management of loads, with the threshold values detailed in

Table 1. Power distribution prioritization in three proposed load areas.

Produced Power in kW (Pin)	First Priority Load	Priority in Section 2	Priority in Section 3
Pin = 80	✔	✘	✘
80 < Pin < 85	✔	✔	✘
85 < Pin < 90	✔	✔	✘
90 < Pin < 95	✔	✔	✘
95 < Pin < 100	✔	✔	✘
100 < Pin < 105	✔	✔	✘
105 < Pin < 110	✔	✔	✘
110 < Pin < 115	✔	✔	✘
115 < Pin < 120	✔	✔	✘
120 < Pin < 125	✔	✔	✘
125 < Pin < 130	✔	✔	✔
130 < Pin < 135	✔	✔	✔
135 < Pin < 140	✔	✔	✔
140 < Pin < 145	✔	✔	✔
145 < Pin < 150	✔	✔	✔
150 < Pin < 155	✔	✔	✔
155 < Pin < 160	✔	✔	✔

, ensuring that critical loads are always supplied first. The central control conveys appropriate commands to the inverter and diesel generator, adjusting the power production to maintain the system’s stability and efficiency. Figure 2 shows the PV farm’s integration and role in the power-sharing algorithm, providing an overview of the control mechanisms and the interaction between local and central controls.

The proposed hybrid system, which combines renewable energy sources and backup generators, offers a robust and reliable solution to address power-sharing challenges in microgrids. This system ensures a stable and flexible power supply under various operating conditions, significantly contributing to the broader goals of energy sustainability and environmental conservation. Table 1 shows the loads prioritized based on the produced power (Pin). For instance, the first-priority load is always active when the Pin is 80 kW or higher; the Section 2 becomes active above 100 kW; and the Section 3 is active above 125 kW. This ensures that critical loads are met first, followed by less critical loads as more power becomes available.

When the system encounters a fault at the connection point between the microgrid and the main grid, the microgrid will transition to operate in an island state and face challenging conditions. In this step-by-step power distribution strategy, based on binary logic and increments of 5 kW, loads are added to the system according to the power generated, as depicted in Figure 3.

When the total power Pin varies due to sun irradiance variations and the diesel generator’s output variations, the Pout is adjusted accordingly in a step-by-step procedure. Figure 4 shows a 24 h power generation profile, with the Pout represented as a staircase plot, which illustrates how the load is managed based on the available generation by satisfying the most critical load. Both the Pout and Pin start at 80 kW, peak at 160 kW at around 12 h, and return to 80kW, reflecting the daily power demand and supply cycle.

To obtain accurate simulation results, it is essential to consider fundamental factors in the photovoltaic module, such as the solar radiation, temperature, and time of day. As illustrated in Figure 5, seven points have been selected for use in the simulations.

3. Control Strategy

In this system, there are two control loops for the power sources. One is dedicated to controlling the PV farm, and the other controls the diesel generator. Prior to a natural disaster, the microgrid operates in a grid-connected model. The control adjusts to align with the main grid, enabling the microgrid to supply power to the grid. In a post-disaster scenario, the microgrid operates in an islanded mode, and it must collaborate with concentrated diesel generators to feed the loads. Comprehensive control strategies for these scenarios are detailed in the following sections.

3.1. Control Strategy for PV Generation

In the proposed control system for the inverter interfacing a photovoltaic (PV) source with an AC bus and grid, a comprehensive control strategy is implemented to ensure optimal performance and grid compliance, as shown in Figure 6. The process begins with the conversion of the DC voltage, generated from the PV panels, by the DC–DC converter and inverter into an AC output. This AC output is then passed through an LCL filter, which serves the dual purpose of mitigating switching noise and reducing harmonic distortions, thereby smoothing the output waveform prior to interfacing with the grid before the fault and with the diesel generator after the fault. The control architecture utilizes a transformation sequence where the three-phase AC signals are first converted into two-phase stationary frame components (αβ) and subsequently into the rotating dq frame. This transformation to the dq frame aligns the system’s reference frame with the grid and diesel generator’s rotating magnetic field, simplifying the dynamic control of the current’s direct (Id) and quadrature (Iq) components. The control of these components is achieved through a proportional–integral (PI) controller, which adjusts the voltage signals to match the reference current values set based on the operational demands or grid requirements. The outputs from the PI controller dictate the modulation of the inverter’s switches via a pulse width modulation (PWM) technique, ensuring the delivery of the desired current levels to the grid. A phase-locked loop (PLL) is integrated within the system to synchronize the inverter’s output frequency with the grid and diesel generator, maintaining stability and coherence in the phase and frequency. This synchronization is critical for grid-tied applications, where phase alignment and frequency matching are imperative. Finally, the control signals, fine-tuned in the dq domain, are transformed back to the αβ and then to the abc domain, facilitating their application for synchronization. This reverse transformation ensures that the precisely controlled voltages and currents are effectively applied to the three-phase grid system, showcasing the robustness of the control strategy in maintaining the system’s performance and adherence to defined standards.

3.2. Diesel Generator Modeling and Control Loop of Diesel Generator

The section presents the control and modeling of a diesel generator, designed to optimize the integration of a diesel generator with a PV farm, particularly emphasizing the dynamic management of both active (P) and reactive (Q) power. Figure 7 shows the diesel generator model. The diesel generator plays a crucial role in maintaining the stability and resilience of this hybrid power system. The configuration involves a series of power converters that facilitate precise control over both the active and reactive power supplied to the AC bus. The diesel generator produces an alternating current (AC). This AC power is the initial form of energy generated, which needs to be managed effectively to ensure a stable power supply and system resilience. The AC power is first converted into a direct current (DC) using an AC–DC converter. This conversion is essential in regulating the active power (P) output. The power conversion system shown in Figure 8 involves a PWM generator, an AC–DC converter, a DC bus with an output filter, and an inverter. The PWM generator with a 60-degree phase shift produces pulse width modulation (PWM) signals (T1, T2, …, T6) to control the switching devices in the AC–DC converter. This DC power is then collected on a DC bus, where an output filter smooths the voltage, filtering out any ripples to ensure a stable DC output, which is input into the DC–AC converter shown in Figure 7. The system employs signal processing and control techniques to ensure stable operation and efficient power distribution, even under varying load conditions, as shown in Figure 9.

The inverter provides the ability to control the output real power and reactive power of the system. The control strategy for the DC–AC section begins with the accurate calculation of the instantaneous three-phase power. This is achieved through the direct computation of the active and reactive power components using standard power equations and an analogous formula for Q. These calculations are crucial for real-time monitoring and decision-making within the power management system. To simplify the control algorithms and enhance their responsiveness, the system utilizes dq0 transformation. This approach converts the three-phase power quantities into direct (d), quadrature (q), and zero (0) components, aligning them with the generator’s rotating frame of reference. This transformation facilitates the manipulation and control of the power outputs, particularly in systems where synchronization with the PV farm’s output is paramount. Control of the d and q components is realized through proportional–integral (PI) controllers, which adjust the diesel generator’s output to match predetermined reference values for Id and Iq. These references are set based on the desired levels of active and reactive power, allowing the system to adaptively respond to changes in load or generation capacity. The PI controllers are integral to maintaining the balance between power generation and consumption, ensuring optimal efficiency and stability. A phase-locked loop (PLL) is also incorporated to synchronize the frequency and phase of the generator’s output with those of the grid (in this case, the PV farm). This synchronization is critical, as it minimizes the disturbances caused by frequency or phase mismatches and ensures cohesive operation between the diesel generator and other power sources within the PV farm.

This configuration is particularly beneficial in microgrid applications, where the integration of various power sources and the maintenance of system reliability are critical. The two-stage conversion process (AC–DC and DC–AC) provides an effective solution for the integration of different forms of generation, including AC generators, battery storage systems, fuel systems, etc., into modern power systems. This capability is essential in enhancing the resilience of power systems, especially in scenarios where renewable energy sources are integrated alongside traditional generators.

3.3. Control Strategy for Loads

The load management section of the hybrid power system plays a pivotal role in maintaining energy efficiency and system reliability by intelligently distributing power among various loads based on priority, as shown in Figure 10. The load control module connects or disconnects the load depending on the available power generation and criticality of the load. This section is engineered to dynamically allocate power from both renewable sources and a diesel generator to ensure that critical loads are always met and energy is utilized most effectively. The system incorporates a prioritization protocol that assesses the load criticality in real time. Priority is given to essential services and infrastructure, particularly during periods of limited power availability. This prioritization ensures that the most critical functions of the infrastructure remain operational, even under constrained energy conditions. Less critical loads are dynamically shed or reduced based on the overall system capacity and current energy production levels, optimizing the balance between the load demand and available power. The load management system employs extensive monitoring equipment that provides immediate feedback on power usage and system performance.

4. Simulation Results

This section presents sample simulation results of the proposed power-sharing system and methods. The microgrid in the islanded mode shown in Figure 1b, which consists of the PV farm and the diesel generator, is used in this simulation study.

The case studies illustrate the system performance when the diesel generator output and sun irradiance change. The diesel generator’s output is changed from 60 kW to 20 kW at the time of 1.5 s; the decrease could be due to various reasons, such as a fuel deficiency or technical limitations. The sun irradiance is changed from 800 W/m² to 70 W/m² at the time of 0.7 s and from 70 W/m² to 1000 W/m² at the time of 1 s.

The PV farm generates power and transforms it through a DC–DC converter, resulting in an output voltage of 480 V, as shown in Figure 11. The output voltage increases from zero to 480 V as the irradiance increases from zero to 800 W/m². The early fluctuations are due to the system adjusting to the optimal operating point. Once stabilized, the voltage indicates a steady state of operation, where the system efficiently converts solar radiation into electrical energy.

Assume that, in 0.7 s, the sun irradiance decreases from 800 W/m² to 70 W/m². In this situation, the voltage value decreases to 400 V; then, within 1 s, the irradiance increases again to 1000 W/m². Consequently, the voltage value increases to 500 V. At 1.5 s, a notable dip occurs, corresponding to the change in the diesel generator’s output, followed by another period of oscillation. Eventually, the voltage stabilizes around 480 V, indicating the system’s adaptation to the new operating conditions.

Figure 12 displays the active power output from the PV farm. Initially, the PV power output oscillates due to transient processes and then settles to a lower steady-state value. At 0.7 s, the irradiance decreases to 70 W/m², causing the output power to decrease. At 1 s, the irradiance increases to 1000 W/m², leading to an increase in output power. Due to the change in the diesel generator’s power, there is a spike in the output power. Subsequently, the PV farm stabilizes at a constant value.

Figure 13 displays the three-phase voltages of the PV farm’s inverter, which remain stable and sinusoidal with a small deviation from the clean sinusoidal waveform, indicating effective DC-to-AC power conversion. Notably, between 0.7 and 1 s, there is a decrease in voltage due to changes in solar irradiance, yet the peak voltage remains approximately 380 V. Figure 14 shows the corresponding three-phase currents, which are also sinusoidal, reflecting the efficient load management by the inverter. The current waveforms have a peak value of 100 A, and a similar decrease between 0.7 and 1 s occurs due to reduced solar irradiance. Additionally, at 1.5 s, a small current spike is observed due to a change in the diesel generator’s output from 60 kW to 20 kW.

Figure 15 and Figure 16 depict the three-phase voltages and currents of the diesel generator, respectively. The stable and sinusoidal waveforms confirm that the diesel generator reliably supplies power to the microgrid, particularly during periods of low solar irradiance. A dramatic decrease in current is noted when the diesel generator’s output reduces from 60 kW to 20 kW. These figures verify that the AC–DC and DC–AC conversion system for the diesel generator functions as expected.

Figure 17 shows both the active (P) and reactive (Q) power output of the diesel generator. The active power decreases at the time of 1.5 s due to various reasons, such as a fuel deficiency or technical limitations. The reactive power (Q) remains relatively low in this study.

Figure 18, Figure 19, Figure 20 and Figure 21 illustrate the distribution of active power to loads of different priorities in the power management system.

Figure 18 shows the total active power delivered to all loads. The decrease in power at 0.7 s indicates reduced production due to the decrease in sun irradiance from 800 W/m² to 70 W/m². Subsequently, the irradiance increases to 1000 W/m², boosting the power production. Additionally, at 1.5 s, there is another decrease in power output due to a reduction in the diesel generator’s production.

Figure 19 shows the power distribution to the first-priority loads, which are critical infrastructure components requiring a continuous power supply. It can be seen that a relatively stable power level is achieved. The decrease in power at 0.7 s indicates reduced production due to the decrease in sun irradiance from 800 W/m² to 70 W/m². Subsequently, the irradiance increases to 1000 W/m², boosting the power production. Additionally, at 1.5 s, there is another decrease in power output due to a reduction in the diesel generator’s production.

Figure 20 displays the power delivered to the second-priority loads, which include less critical but still important services. Between 0.8 s and 1 s, the system cannot feed the second-priority loads due to the decreased sun irradiance and the priority given to the first-priority loads.

Figure 21 shows the active power supplied to the third-priority loads, which include non-essential services or deferrable loads. Between 0.7 s and 1.1 s, the system cannot feed the third-priority loads due to the decreased sun irradiance and the priority given to the first- and second-priority loads.

These graphs collectively demonstrate a well-structured power distribution strategy that effectively manages different load priorities. They illustrate the system’s performance in the grid-disconnected mode and its effectiveness in distributing power to the loads according to predefined priorities. This tiered approach ensures that critical services are the least affected by power generation fluctuations, supporting system resilience.

5. Conclusions

In conclusion, this paper presents a power-sharing strategy, particularly focusing on post-disaster scenarios where the main grid connection is severed. The proposed system integrates renewable energy sources and a diesel generator to ensure a reliable and resilient power supply under challenging conditions. Emphasis is placed on a prioritized power-sharing strategy, where critical loads are given precedence. The methodology introduces a comprehensive approach to power sharing, addressing the dynamic nature of renewable energy production. The proposed system seamlessly transitions between normal grid-connected operation and islanded mode, showcasing its adaptability to adverse conditions. The single-line topology further highlights the microgrid’s ability to operate independently, ensuring self-sufficiency during grid disruptions.

The simulation results demonstrate the effectiveness of the proposed system, with stable output voltages and frequencies and strategic load distribution. Notably, the system’s performance was validated through various scenarios.

• The PV farm’s output voltage was stable at around 480 V under normal irradiance conditions, with fluctuations corresponding to changes in irradiance.
• The active power output from the PV farm reached up to 160 kW during peak solar irradiance, demonstrating the system’s ability to harness the maximum solar energy.
• The diesel generator’s active power output decreased from 60 kW to 20 kW at specific intervals, simulating conditions like fuel deficiencies. The system efficiently managed these variations to maintain load stability.
• Power distribution to critical loads was prioritized, ensuring that first-priority loads were consistently met even when the total power input (Pin) varied from 80 kW to 160 kW throughout the simulation period.

The power-sharing strategy, particularly in prioritizing critical loads, is effectively designed to guarantee an uninterrupted power supply even in the face of uncertainties and disruptions. In essence, this paper contributes to the advancement of microgrid control strategies, offering a robust solution for enhanced performance. The proposed system aligns with the broader goals of energy sustainability, environmental conservation, and the creation of resilient power infrastructure. Future work may explore scalability considerations, using other sources such as fuel cell and battery storage systems, robustness assessments, and the integration of intelligent control methods for further improvements in microgrid implementations.