The modern power grid is moving toward utilizing distributed energy resources (DERs). The shifting trend from conventional generation based on fossil fuels to dispersed renewable generation aims to reduce greenhouse gas emissions, which have raised many global environmental concerns (Wu et al., 2024). In addition, the shortage and the increasing cost of fossil fuels are causing security concerns regarding the balance between energy supply and demand. In order to meet the need for electrical energy, the utilization of renewable energy sources (RES) is considered a solution to the problem of the deficit of energy supply. The low generation costs and maximum power point tracking (MPPT) capability of solar and wind energy make them the most promising RES technologies (Hossain and Ali, 2016; Kumar et al., 2024). According to the International Renewable Energy Agency (IREA), RES will supply 66% of global energy consumption by 2050 (Gielen et al., 2019). Microgrids (MGs) have recently received significant concentration from many researchers as a promising solution to better utilizing large penetration of RES. Thus, the future distribution grids may consist of a group of microgrids that generate, deliver and store energy. It is an autonomous energy system that serves a particular geographic region. The configuration of the microgrids depends on different types of DERs, such as solar panels, wind turbines, microturbines, and energy storage systems (ESSs), e.g., battery energy systems (BES) and supercapacitors (Marwali et al., 2009; Gowtham et al., 2018). Based on their voltage characteristics, microgrids are categorized into AC microgrids, DC microgrids, or Hybrid AC /DC microgrids. MGs can operate in grid-connected modes, exchanging power with the distribution grid, or operate in islanding modes supplying energy to their local energy demands. In case of catastrophic events, MGs can operate in islanding modes to restore power to some critical loads, hence improving the availability of energy supply. Besides being environmentally friendly, MGs have several advantages, such as providing grid isolation during disturbances, reducing peak load on the grid to prevent failures, and enhancing energy efficiency. However, they face some operational challenges, including maintaining acceptable voltage and frequency, ensuring power quality standards, and difficulty in resynchronization with the utility grid. In islanded green microgrids that consist of only renewable energy sources, the problems related to voltage regulation, power sharing, and battery management are even more severe and require more sophisticated control strategies.

DC microgrids are becoming increasingly popular compared to conventional AC microgrids due to their compatibility with renewable energy sources and electronically controlled loads. DC microgrids offer several advantages, including the absence of conflicts with harmonics or frequencies, eliminating the need for synchronization in islanded mode, and not requiring reactive power control. (Gandhi and Gupta, 2021; Saleh Al-Ismail, 2024). In such a DC microgrid, uncontrollable DGs based on renewable technologies, controllable DGs, and energy storage systems are coupled via DC links. Microgrids with energy storage devices, such as BES and supercapacitors, can enhance system reliability. On the one hand, the stored energy can supply the electric demand during peak hours; on the other hand, the BES and supercapacitors can be recharged during off-peak hours.

An energy management system (EMS) is used to monitor energy usage and control and optimize the generation units of the grid. The EMS generally involves instruments to monitor energy usage, predict future energy demands, control generation assets, and report on the interventions performed. It provides utilities and building management services with real-time data and the ability to tune energy utilization, enabling to minimize energy expenses and promote effectiveness. During periods of peak demands, the EMS may respond to signals from the energy grid to temporarily reduce energy consumption by shedding non-critical loads to stabilize the grid and prevent blackouts. Overall, the EMS aims to ensure optimal energy management of the grid, minimize energy costs, and reduce environmental impact resulting from major power outages.

Several research studies have proposed energy management systems for microgrids in the literature. An effective energy management system for the DC microgrid with PV systems was proposed in (Awaji et al., 2022), which consists of a photovoltaic system with BES to enhance power generation when the power output of the photovoltaic (PV) system is insufficient and a DC shunt motor as a dynamic load. The DC microgrid was studied in three distinct scenarios of weather conditions to analyze the behaviour of the BESS under the variations of power generation and highlight the contributions of BES in stabilizing the system under the stochastic nature of RES and demand variability in the microgrid. The paper (Alarbidi et al., 2023) introduced modelling and control strategies for a fixed-speed wind turbine powered by a permanent magnet synchronous machine (PMSM). Additionally, a case study was conducted to illustrate the impact of wind energy on the short circuit level. The torque-controlled PMSM was regulated using the field orientation approach, which involved the modelling and control approach for the frequency converter on the generator-rotor side. Reference (Rana and Abido, 2017) presented a strategy for energy management of a DC microgrid with PV systems. Here, a model predictive controller (MPC) was proposed to control the power flow between the bidirectional voltage source converter (VSC) and the grid. The implementation of the proposed controller was assessed and compared with that of the classical PI controller, and the results showed that the MPC outperformed the classical PI control. The hybrid ESS’s frequency control, consisting of a battery and a supercapacitor, was used in the microgrid to eliminate frequency power variation by exchanging power with the DC bus. In the study (Rashid et al., 2021), an efficient approach for home energy management of community microgrids was developed. Here, the microgrid considers RES, distributed battery energy systems, one centralized battery storage system, and demand response programs. The results show potential financial returns for residential customers and facilitate sustainable energy generation options in a community. The positive aspects of leveraging RES, BES, and central BES with the proposed optimal power-sharing algorithm were assessed. Reference (Ahmed et al., 2019) presented a control methodology for the DC microgrid energy managing utilizing hybrid ESS. The microgrid comprised RES, variable load, hybrid ESS, and diesel generator. The implementation of the proposed microgrid was validated using simulation, where the voltage level of DC under load variation and intermittent RER never exceeded the allowable tolerance, i.e., ± 5%. A coordinated control methodology for the energy management of a microgrid in a grid-connected mode with solar PV and BES was proposed in (Zhang et al., 2024a). The algorithm coordinated the VSC and bidirectional DC-DC converter relied on the battery’s state of charge (SOC). It also compensated for the imaginary power of the load and minimized the unbalanced neutral current. Reference (Alahmed et al., 2019) proposed a dynamic load prioritization methodology built on the artificial neural network (ANN) for microgrid reconfiguration. The proposed method prioritized the microgrid demands relied on the current time, available energy supply, and system reliability factors to provide flexible load restoration under different hours and system conditions. An energy management method using fuzzy logic was applied in (Athira and Pandi, 2017) to ensure generation demand balance in the islanded DC microgrid. The system was evaluated under different load conditions to ensure proper operation of the microgrid with PV panels and a hybrid energy storage system composed of a battery and an ultracapacitor to enhance the battery life. A DC microgrid with a standalone solar PV system was proposed in (El-Shahat and Sumaiya, 2019), aiming to enhance the power conversion efficiency and ensure system permanency. The proposed model reduced the component losses and increased the system efficiency by improving the dynamic response, minimizing the harmonic losses, and obtaining a stable maximum power point value using sum-of-squares optimization for the PV system. Reference (Chahal et al., 2023) developed a model for inverter-based distributed generation technology. Here, a control strategy was employed to enhance disturbance rejection and incorporate an extended range of frequency modes through an analogous internal model. Particle swarm optimization (PSO), focused on minimizing the error in the actual power, was used to discover the best controller parameters. A microgrid operating in grid-connected and autonomous modes was described in (Hussaini et al., 2022) using linear and nonlinear models. An optimization algorithm was proposed for designing various controllers, filters, and power-sharing coefficients. The outcomes validated the efficiency of the suggested PSO approach in tuning the PI regulator, filter, and power-sharing coefficient settings to attain adequate system performance under several disturbances. The microgrid’s stability was examined using nonlinear time-domain simulations in a project to construct a nonlinear model of a self-controlled microgrid published in (Wang S. et al., 2024). The PSO methodology was utilized to ascertain the optimal values for the tuned parameters. The outcomes attest to the strength and efficacy of the suggested PSO-based methodology. The real-time digital simulator (RTDS) outcomes validated the suggested controllers’ efficacy for the system under consideration under various disturbances and operating situations. In (Hassan et al., 2018), the influence of active load on the microgrid’s dynamic stability is investigated. The accompanying controllers are developed for an autonomous microgrid with three DGs based on inverters and an active load. The outcomes showed that the suggested controller has the potential to increase the microgrid’s stability and offer effective dampening properties. Additionally, it demonstrated good performance with the autonomous microgrid under consideration having effective damping properties. When used on MG, the study in (Naderi et al., 2023) examined several System of Systems (SOS) control mechanisms. The study concluded that a networked control system allows for improved microgrid control. The (Keerthisinghe and Kirschen, 2020) microgrid concept featured solar systems, a battery, a backup generator, loads, and an electric vehicle charging station with grid connectivity. The performance of the microgrid was evaluated under three operating scenarios: grid-connected mode, islanding mode microgrid using only the battery, and islanding mode microgrid operating using the backup generator. A microgrid constructed by Snohomish PUD in Arlington, Washington, was simulated in real-time using the real-time digital simulator OPAL-RT. Methods for detecting local and remote microgrid islanding were provided in the paper (Rami Reddy et al., 2024). In each class, various detection techniques were investigated, and the benefits and drawbacks of each technique were reviewed based on some assessment indices, such as non-detection zone, detection time, fault detection rate, and power quality indices. The use of signal processing approaches and intelligent classifiers to modify islanding procedures recently was also covered. The control and sharing of real and reactive power outputs of the inverter-based DGs was suggested using an optimal PQ control technique (Ahmad et al., 2023). Here, the study was conducted on two instances of microgrids with various structures, and various disturbances were used to study how well the microgrid performed. The research study found that the dynamic stability of the microgrid was enhanced using the suggested optimal control. A powerful control technique for voltage balancing, grid synchronization, and power management was developed in (Worku et al., 2021) to enhance the stability and dependability of a microgrid based on DERs. The system model and control approach were created in an RTDS. The outcomes demonstrated the efficacy of the suggested control strategy in grid-connected microgrids, islanding microgrids, and microgrid resynchronization.

In short, an energy management system constantly monitors energy use by analyzing real-time data and weather patterns; it predicts future demand and controls generation, including renewables, for efficient power production, as explored in (Lei et al., 2023). Moreover, it provides utilities with valuable data for cost-saving and environmentally friendly grid improvements while integrating renewable energy (Xie et al., 2016). Recent advancements offer promising solutions to microgrid challenges. Research is shifting towards more sophisticated models that capture the dynamic behavior of DERs within the microgrid, as proposed in (Kumar et al., 2024). Additionally, exploring novel control strategies using artificial intelligence and machine learning for optimized energy management and enhanced system resilience holds significant potential, moving beyond approaches such as particle swarm optimization (PSO) (Chahal et al., 2023; Hussaini et al., 2022), as demonstrated in research on a DC microgrid energy management system (Awaji et al., 2022). This paper dives deeper into the challenges faced by isolated, renewable-powered microgrids (MGs). To ensure alignment with current research, we comprehensively review recent literature, including microgrid dynamics and control (Gandhi and Gupta, 2021), virtual inertia control for DC microgrids (Saleh Al-Ismail, 2024), advanced control strategies like optimal PQ control and networked control systems for MGs (Naderi et al., 2023; Ahmad et al., 2023), and islanding detection methods (Rami Reddy et al., 2024). Table 1 summarizes the existing work on microgrid dynamics and control strategies.

Several studies on real-time energy management simulations have examined renewable energy integration in microgrids, leading to many approaches to enhancing their efficiency and stability. In (Shirkhani et al., 2023), an extensive survey of decentralized energy management and voltage controls for microgrids is presented and highlights some crucial operational challenges. An online reinforcement learning-based energy management for centrally controlled microgrids is introduced in (Meng et al., 2024), highlighting how adaptive learning can be useful in real-time optimization. Moreover, Ref. (Zhou et al., 2024). proposes a multi-stage adaptive stochastic-robust scheduling approach for hydrogen-based multi-energy microgrids, incorporating stochastic programming and robust optimization to enhance system resilience against uncertainties. Furthermore, (Zhang et al., 2024b), emphasizes embedded DC power flow regulators based on full-bridge modular multilevel converters, which control power flow and improve reliability. Ju et al. (2022) proposes a distributed three-phase power flow for AC/DC hybrid microgrids while considering converter limiting constraints aiming at tackling complexities associated with hybrid AC/DC microgrids.

This presented paper investigates the issues related to operation of a DC microgrid powered by solar and controllable DGs, using readily available batteries and supercapacitor storage connected via DC links. The well-established InCond algorithm has been employed to maximize solar output, while batteries and supercapacitors operate as backups. The energy management strategy for the proposed DC microgrid is based on the system operators’ experience, where the solar system is the primary source, while the battery and supercapacitor act as backup power sources. The proposed model will be evaluated through various scenarios, i.e., normal operation and load changes, to ensure effectiveness considering the utilization of the non-advanced controller. All results are tested in a real-time condition using simulation software (OPAL-RT). The combined BES and supercapacitor approach addresses the limitations of BES alone, improving power stability and system efficiency and offering a more comprehensive performance evaluation by considering diverse weather conditions. This demonstrates the feasibility of a reliable and efficient DC microgrid maximizing renewable energy with conventional controls. The contribution of the presented paper can be summarized as follows:

• Analyzing a DC microgrid combining RES and energy storage technologies, such as BES and supercapacitors, to overcome the limitations of battery-based microgrids and leveraging the InCond method for the MPPT of the PV system.

By addressing these aspects, this paper not only advances the field of energy management in DC microgrids but also offers practical profits like improved energy efficiency, minimized emissions, cost savings, and validation through rigorous simulations. The structure of this article is as follows: Section 2 provides an explanation of the DC Microgrid, while Section 3 contains the system model. Section 4 includes simulation results and a discussion on energy management. Section 5 provides a concise overview of the research findings and explores potential avenues for future research.

A microgrid is a small-scale grid that comprises DGs and ESSs to serve local loads (Dawoud et al., 2021). The microgrid may operate in conjunction with the main grid, referred to as grid-connected mode, or operate independently in an islanded mode, and it can be AC, DC, or hybrid AC-DC based on the voltage characteristics (Fani et al., 2022). The microgrids operating in grid-connected mode can offer ancillary services to the main grid, such as voltage and frequency control, to improve the system’s flexibility and reliability by trading energy based on the available energy supply and current energy demands. In this grid-connected mode, DERs operate in an MPPT mode to supply the maximum available power to the grid. The microgrid operating in islanding mode, i.e., isolated from the main grid, can serve its local load from the DERs (Worku et al., 2019). In these microgrid operational modes, the power generation and demand balance is the most critical issue in the energy management of the microgrids. In AC microgrids, DC sources such as PV systems are coupled with the AC system using DC/AC converters. In DC microgrids, DC sources are directly linked to the DC bus, and AC sources are coupled with the DC bus via AC/DC converters. Therefore, DC microgrids have higher system efficiency than AC microgrids due to less power. Converters are required and hence, less investment and operating costs. Hybrid microgrids can supply energy to AC and DC loads; however, they are more complex than DC microgrids. The DC Microgrid can facilitate the utilization of large RES with less complexity and operating costs compared with its counterparts. However, the optimal operation of microgrids should be addressed to ensure efficient performance of both grid-connected and islanding modes and promote sustainable power generation (Jirdehi et al., 2020).

Figure 1 displays the configuration of the DC microgrid. The physical layer is comprised of PV, battery and super capacitor connected to the DC bus through a DC-DC converter to supply a local load. The power production of the photovoltaic (PV) system is influenced by the diurnal cycle, encompassing both day and night periods. Consequently, it has an adverse impact on the DC motor’s ability to meet the necessary power demand. Hence, using Battery Energy Storage (BES) in conjunction with the Photovoltaic (PV) system is imperative for a streamlined energy management system to maximize system performance during critical scenarios. The connection in a direct current (DC) system is regarded as simpler compared to an alternating current (AC) system due to the sole requirement of regulating the voltage in a DC bus. In the DC microgrid, distributed generators, ESSs, and local loads are linked via a single DC bus using line regulating converters or are directly connected to the DC bus depending on the operating voltage.

The MPPT algorithm can be employed to maximize the power output of the PV panel by regulating the output voltage and current, considering the variations in irradiance (Elbarbary and Alranini, 2021). Several methods exist to achieve this goal. Among them, the Incremental Conductance (InCond) algorithms are widely used (Seyedmahmoudian et al., 2014). Other approaches for the MPPT of the PV systems leverage heuristic algorithms, such as fuzzy logic control and neural networks. However, these methods provide a local maximum or approximated solution for the MPPT. The V-P curve of the PV panel under normal conditions contains merely one maximum value. However, partial shading of the PV panel may lead to multiple maxima in the curve. The InCond method for the MPPT of the PV system is developed based on the “hill-climbing” principle (Seyedmahmoudian et al., 2014), which involves adjusting the operation point of the PV panel in a direction that can increase the output power of the PV panel. The hill-climbing approach is prevalent for the MPPT of the PV system because of its straightforward implementation and satisfactory performance, particularly during constant solar irradiation. Although this method offers simplicity and requires low computational power, it also has some drawbacks. Oscillations can occur around the MPP, and during rapidly changing weather conditions, the algorithm may fail to track the MPP and move in the wrong direction.

In this paper, the InCond method will be utilized for the MPPT of the PV panels, primarily because of its advantages over other techniques mentioned earlier. The flowchart presented in Figure 2 outlines the process of the InCond MPPT method (Seyedmahmoudian et al., 2014). This method tracks MPP through a simultaneous comparison of InCond ( ∆ V p v / ∆ I p v ) and instantaneous conductance ( ∆ V p v / ∆ I p v ) (Seyedmahmoudian et al., 2014). Based on the location and PV output characteristic, the controller adjusts the operating point of the PV panel in the direction of the MPP with a step size of C . The speed of the controller’s track mechanism depends on the step size value. A larger step size enables faster MPP tracking but leads to oscillations around the MPP. To address this issue, some researchers have developed a variable step size InCond method. This technique initially uses a large step size to approach the approximate MPP region and then switches to a smaller step size to accurately track the MPP. This enhances the controller’s accuracy and prevents oscillations around the MPP (Abdel-Salam et al., 2018). For this study, a regular MPPT technique without advanced control methods was used. The primary goal is to analyse the dynamics of DC microgrids under various conditions.

Herein, the DC microgrid system model is presented. The model comprises a PV system, power converters, BES, supercapacitors, and a DC shunt motor. The following subsections will explain each component in the proposed DC microgrid.

The performance of the Microgrid is validated by using a real-time simulation (Opal-RT), as shown in Figure 6. The proposed DC microgrid and the proposed algorithms are implemented using MATLAB Simulink. Subsequently, we compiled the algorithmic code in Opal-RT (OP4510) system. The primary objective is to assess the microgrid’s dynamic behaviour and evaluate the proposed control strategy’s effectiveness under various operating conditions. This research proposes a non-sophisticated control strategy for a DC microgrid powered by solar and controllable DGs, using readily available battery and supercapacitor storage connected via DC links. The control method is based on prioritizing the available sources in the DC microgrid. The power output of the PV system is of high priority, as it will supply the DC motor and charge the BES. When solar irradiation is low, the BES and supercapacitors will kick in to supply the DC motors. The control signal will be sent by the system operator from the control system that is considered to be a centralized controller who has full access to the available power sources. The well-established InCond algorithm maximizes solar output, while batteries and supercapacitors act as backup. Three main power generation scenarios for the energy management strategy are conducted:

• Full System: Utilizing all available sources - PV system as primary sources, while energy storge system BES and supercapacitors as backup sources.

The DC microgrid is tested under these power generation scenarios. Firstly, power sources include the PV system, BES, and supercapacitors used to form the DC microgrid. The second scenario considers a standalone PV microgrid, and the last scenario is the operation of the DC microgrid consisting solely of the backup power sources, i.e., BES and supercapacitors. These different power source scenarios are used to test the dynamics of the microgrid and evaluate the performance of the proposed method under Standard Test Conditions (STC). The following cases are performed to identify the system behaviour under steady-state conditions. Moreover, the case study evaluates the performance of the ESS under different operating conditions. Lastly, the fault analysis on the DC microgrid is performed. It is worth noting that the power rating of the system under study is considered to be 20 kW. These cases have been listed as follows:

1. Steady-State Scenarios:

Sever studies have focused on energy management strategies in DC microgrids. Furthermore, all these studies have agreed on efficient power management and system reliability are crucial. However, these studies have employed different in methods as well as areas of focus. While (Li et al., 2023; Duan et al., 2023) utilized optimization focused decision-making process to improve the performance of their systems (Li et al., 2023), relies on using artificial intelligence (AI) whereas bidirectional converters are used by (Duan et al., 2023). Conversely (Luo et al., 2024), focused on using model predictive control for enhanced power flow whereas (Li et al., 2023; Duan et al., 2023) discuss energy losses based on their observation. Nevertheless, these studies serve the same objective of energy management in a DC microgrid. It is obvious that the performance could be slightly different as a result of complex control methodology. The presented paper showed adequate performance with a conventional controller.

For future work, one potential area involves investigating the influence of frequency on the microgrid’s behaviour. Additionally, incorporating advanced control systems capable of predicting changes in solar power output and adjusting power supply accordingly could further reduce power fluctuations and enhance stability. Overall, this study provides a valuable foundation for developing and optimizing reliable DC microgrids that effectively integrate renewable energy sources like solar power. A valid extension of the presented work is performing an experimental validation.

The proposed paper develops an energy management system (EMS) for a DC microgrid, aiming to ensure stable and dependable power delivery for DC motors with conventional control strategies. The microgrid integrates a photovoltaic (PV) system, battery energy storage (BES), and supercapacitors to provide a robust power supply. To optimize PV power generation, the Incremental Conductance (InCond) technique is employed for maximum power point tracking (MPPT). Furthermore, this work investigates the microgrid’s performance under various operating scenarios, including diverse power generation conditions and fluctuating solar irradiation. The focus is on maintaining grid stability and reliability. Furthermore, the study examines the performance of the BES in both charging and discharging modes, highlighting its crucial role in power management. Additionally, a fault analysis is conducted to assess the system’s response to potential failures and its overall dependability. Further validation of the proposed microgrid is achieved through real-time simulation using OPAL-RT and MATLAB. The results demonstrate that under standard test conditions (1000 W/m² solar irradiation and 25°C), the DC motors operate efficiently across various power generation scenarios. The fault analysis emphasizes the importance of the BES in mitigating instability caused by fault conditions. This highlights the BES’s role in enhancing the microgrid’s overall reliability and ensuring consistent power delivery. The load condition of a system greatly affects its efficiency. This is observable when examining full-load and no-load situations. Conversely, fault tolerance has the potential to affect efficiency through systems such as BES. Even though BES guarantees the constant supply of power during faults, there are energy conversions needed that might lower overall efficiency. Consequently, this gives rise to a trade-off between the reliability of a system and its peak efficiency thus necessitating careful analysis for specific fault conditions. This research has a few limitations. First, the repeated use of the InCond algorithm might limit adaptability to advanced strategies. Additionally, while the study covers various weather conditions, it does not address extreme events or long-term climate impacts.