Microgrid (MG) operation with distributed generation units (DG) and renewable energy sources (RES), such as photovoltaic (PV) systems, wind turbine (WT), and proton exchange membrane (PEM) fuel cells (FC), is now the most commonly used in electrical power systems (Evans et al., 2009; Qazi et al., 2019; Sinsel et al., 2020). In addition, the use of these sources necessitates the addition of a supplementary energy storage system unit in order to reduce power fluctuations and increase the availability of electricity (Molina and Juanico, 2010). In contrast, stand-alone or off-grid photovoltaic (PV) systems using Li-ion batteries that are more efficient have gained popularity in recent years (Vonsien and Madlener, 2020). It is essential in that system to have an EMS between the photovoltaic cells, the battery, and the load in order to transfer the right amount of power between the sources and increase the lifespan of the battery (Rydh and Sandén, 2005; Bragard et al., 2010; Campana et al., 2021). In point of fact, it is possible to achieve the maximum power of the PV system by using a maximum power point tracking (MPPT) device. This device not only improves the effectiveness of the PV systems but also improves their performance in a variety of weather conditions (Saleh et al., 2020; Yaqoob et al., 2020). In order to manage the duty cycle and extract the peak power, an MPPT device is installed into a PV system or module with an appropriate DC/DC converter. Several other MPPT methods, including perturb and observe (P & O), incremental conductance (IC), fuzzy logic, and several others, are used in the application of PV systems (Rizzo and Scelba, 2015; Robles Algarín et al., 2017; Rezk et al., 2019; Shang et al., 2020). Managing lithium-ion batteries, on the other hand, can improve performance, extend battery life, and cut down on power losses in the grid. The right way to manage the battery may help keep the DC bus voltage constant and improve the way power is shared between the load and the generator (Mesbahi et al., 2017; Hannan et al., 2018; Berrueta et al., 2020).

Off-grid photovoltaic (PV) systems or DCMG systems with battery energy storage systems and renewable energy sources have been proposed by a number of researchers (Shiau et al., 2009; Ongaro et al., 2012; Li et al., 2015). These systems are intended to improve efficiency and deliver optimal power to the load by utilizing power DC/DC converters. For example, the authors of (Fathabadi, 2019) proposed a hybrid power system consisting of solar panels, fuel cells, and batteries that could charge electric vehicles (EVs) that use lithium-ion batteries. The work that was given was modeled and simulated by utilizing the MATLAB® and Simulink software, and the efficiency of the system fulfilled expectations. This was shown by the fact that the generated outputs matched up with the outcomes that were needed. It was also found out that it is feasible to increase the energy efficiency of this system by making use of electrical equipment that is more energy efficient, such as batteries that run on lithium-ion. In a remote location, Khadepaun et al. (Khadepaun and Shah, 2020) presented a demonstration of a hybrid MG that was powered by a solar PV system as well as PEMFC cells. According to the findings of this research, the use of environmentally friendly fuel cells as energy source units in small grid plants is the superior choice to the utilization of lithium batteries, which have a life span of between five and 7 years. When it has reached the end of its useful life, it should be thrown away, which is another way in which it harms the environment. According to the findings of the research, making use of DC loads, such as electric vehicles, lights, and chargers, makes the MG system more efficient and productive since it eliminates losses that would have been generated by shifting current from DC to AC. The power system that Gonzalez et al. (Gonzalez et al., 2019) suggest using is a PV/FC/battery MG system that also includes a battery unit for energy storage. The model predictive control method is used inside an EMS in order to distribute the appropriate amount of real power to the grid. This strategy cuts down on how much power the battery needs, but it depends on how accurate the prediction is, which is a variable and unpredictable factor.

The authors of (Tofighi and Kalantar, 2011) suggested using EMS to manage the power flow between the sources and load for a PV/battery system using linear PI management. This would allow the authors to control the flow of power. MATLAB® and Simulink were used in order to verify the accuracy of the provided technique. The findings that were acquired were achieved under a variety of load situations, and they demonstrate that the suggested PV-battery system has a quick speed response as well as a minimal oscillation in DC voltage. However, in classic EMS, a proportional-integral (PI) control was used to regulate the power sharing between PV, the battery, and the load (Kewat et al., 2018). This type of control has several problems, the most important of which are the need for tuning, the linear control type, and the slow dynamics.

The study in (Sharma et al., 2022) presented a sensitivity analysis and techno-economic modeling of a MG system consisting of solar, wind, hydro, and battery storage, with this MG system linked to the utility grid. One benefit of the on-grid approach for rural areas is that any extra power from RES can be sold back to the utility grid. This helps find the lowest Levelized Cost of Energy (LCOE) and Total Net Present Cost (TNPC). The authors in (Belaid et al., 2022a; Belaid et al., 2022b), proposed a power management control (PMC) based optimization for a wind/battery system. This method is created and coupled to the suggested two-level MPPT controller in order to facilitate the management of the wind and battery power system. The used PMC guarantees that the utilised storage batteries are subjected to the least amount of stress possible by providing efficient optimum functioning of two MPPT algorithms. The primary purpose of the PMC-based system is to meet the power requirements of the load, while secondary goals include preserving the state of charge of the battery bank, avoiding power outages, and maximizing the useful life of the batteries. The authors in (Dashtdar et al., 2022) proposed an EMS for a residential MG system in order to reduce the cost of operating the MG sources. The suggested EMS method is implemented on the combined heat power (CHP) with batteries and the obtained results show the effect of the battery on MG participation in load response. In addition, the cost of operating a MG and how to exchange with the grid under varying market price tariffs are analyzed, with results demonstrating the efficacy of the suggested strategy.

This article presents an optimum EMS based on the non-linear flatness control theory for an off-grid hybrid solar PV system.

The following are the main contributions that are presented by this research: 1

Suggests a non-linear control based EMS for PV-battery power systems that provides excellent quality for the output power.

This paper was structured into the following sections: Section 2 introduces photovoltaic system modeling. Section 3 presents the proposed energy management strategy. Section 4 reports the simulation results and discussion. Finally, Section 5 presents the conclusion of the paper.

Figure 1 depicts the electrical circuit of a PV cell model used in this study (Molina and Juanico, 2010; Saleh et al., 2020). It is simple and straightforward to express in the MATLAB® environment and provides enough representation for the PV array. The PV array current ( I p v ) for given N P   × N s   can be written as follows (Molina and Juanico, 2010). I p v = N P   I s − N P   I o exp V p v + R s N S   N P   . I p v γ   V T h N S   − 1 − V p v + R s N S   N S   . I p v R s h N S   N P   (1) where the components in the above equation are summarized as follows:

✓ I P h is the source of the photo.

Furthermore, in this research, the PV array employed in simulation is made up of 5 × 10 array, resulting in a large PV array of 10 kW to deliver adequate DC power and voltage to the DC load side and battery. Furthermore, the employed PV array was connected via a DC/DC boost converter to amplify the PV voltage and implement the MPPT algorithm for varied irradiance profiles. Table 1 shows the datasheet parameters of the employed PV module type KC200GT under STC circumstances (Energy Matters, 2020). These parameters include maximum output power P m p p , the voltage of the open-circuit V o c , the voltage at MPP V m p p , current at MPP I m p p , short-circuit current I s c , temperature coefficient at I s c ( K i ) , temperature coefficient at V o c ( K v ) and N s e r .

A MPPT approach is required in PV system applications to optimize power from the PV system and to increase system performance under varying weather conditions (Rizzo and Scelba, 2015; Yaqoob et al., 2020). Furthermore, the MPPT control is used to create the required duty cycle and to activate the boost circuit’s switch as seen in Figure 2. Several low-cost and easy MPPT approaches, including P&O and IC techniques, were applied to extract the maximum power from the PV module (Saleh et al., 2020; Yaqoob et al., 2020). In this paper, a VSSP&O MPPT was used to track the MPP of the P-V characteristics of the PV system. The flowchart of the presented MPPT method is illustrated in Figure 3. The presented MPPT was designed with a variable step size in order to vary the duty ratio with respect to the change in power. Thus, this method is more efficient than the traditional fixed step size P&O. Therefore, the PV system will be controlled without power oscillation around the MPP, and the performance will be improved.

Li-ion batteries are extensively used in many power systems due to their increased efficiency when compared to other technologies (Ferahtia et al., 2021). It is known as a lithium-metal battery because it contains metallic lithium as an anode. However, a greater energy density was attained from a battery with a little power density. The increased energy density of the Li-ion battery makes it more appropriate for off-grid PV applications, especially when electricity availability is limited. The battery model utilized in this study is based on the MATLAB/Simulink® model shown in Figure 4. The battery’s output voltage can be stated as follows (Ferahtia et al., 2021): V b = E 0 − K   Q Q − i t − R b i + A b   e − B i t − K   Q Q − i t i * (2) where the above battery’s parameters can be defined as.

• V _b is the output voltage of the battery (V),

The battery design was implemented under simulation based on the characteristics listed in Table 2. The battery module parameters are shown in Figure 5. The initial SOC of the battery is 95%. A battery bank of 50   A h with a voltage of 48   V was used in this study. The maximum extracted energy from the battery is 2,400 Wh, and this value can cover the demnded load that used in this analysis.

The electrical boost converter circuit that shown in Figure 2 have three main parameters which are should be calculated to design the boost conveter in the simulation or practical operation. As a result, the following Equations 3-5 are utilized for the inductor current ( L ), the input capacitor ( C i n ), and the output capacitor ( C o u t ) (Saleh et al., 2020): L = V p v   δ f s ∆ I p v   (3) C i n ≥   δ 8 × f s 2 × L × 0.01   (4) C o u t = I o u t   δ f s ∆ V o u t   (5) where V o u t represents the voltage of the boost converter, the volatge’s ripple denotes by ∆ V o u t = 0.02 V o u t , δ is the duty cycle, f s is the switching frequency, and the ripple in the inductor current is ∆ I p v . = 0.3   I p v . In this study, the continuous conduction mode (CCM) was used to analyze boost circuit and compute the electrical parameters of the circuit. By assuming the maximum duty ratio is 0.8, the inductance, the input capacitor, and the output capacitor are 1   m H , 600   μ F , and 1000   μ F , respectively.