Maximum Power Point Tracking Algorithm Research Articles

MPPT Charge Controller using Fuzzy Logic for Battery Integrated with Solar Photovoltaic System

In comparison to other Renewable Energy (RE) resources, solar energy has become the most prominent and prospective source for generating electricity, substituting conventional sources. However, solar Photovoltaic (PV) energy production is dependent on solar irradiance and cell temperature. By implementing the Maximum Power Point Tracking (MPPT) algorithm, it is achievable to maximize the power from solar PV. In spite of this, there is still a slower convergence rate, a significant fluctuation around Maximum Power Point (MPP), and a drift issue caused by rapid irradiance variations in solar PV. In order to prevent oscillation and attain a steady state and continuous output of the PV module, a Fuzzy Logic (FL)-based MPPT has been designed in this work. With the buck converter as the DC-DC converter and the lead acid battery as the input, the Perturb & Observe (P&O) MPPT method is selected. The overall design will be developed using Matlab Simulink, and the efficiency of the FL-MPPT charge controller will be evaluated under constant and step irradiance. Additionally, the battery's State of Charge (SOC) will be monitored to prevent overcharging and discharge. In addition, the effectiveness of the controller will be evaluated with and without the MPPT method. On the basis of simulation results obtained from constant and step irradiance levels, the FL-MPPT charge controller with the P&O algorithm and the lead acid battery as the load was able to maintain maximum system efficiency while extending battery life. The FL-MPPT charge controller obtained about 96% efficiency for both irradiance profiles, whereas the system without the FL-MPPT algorithm only achieved 42% efficiency.

Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers

Environmental factors and load conditions influence the efficiency of power converters - key elements in Photovoltaic (PV) systems. This study employs optimization algorithms to fine-tune the converter's operation, focusing on metaoptimization, an algorithm increasing attention in recent research. The analysis introduces the Grey Wolf Optimizer (GWO) to enhance the Particle Swarm Optimization (PSO) algorithm. The optimized PSO algorithm is integrated into a PV system's Maximum Power Point Tracking (MPPT) controller. Implemented in MATLAB/Simulink, this approach is validated by combining measured data and simulation scenarios: 1. staggered vs 2. real irradiation changes. The results underscore the efficacy of the GWO-optimized PSO MPPT algorithm in enhancing the MPPT controller's performance. In Scenario 1, the GWO-optimized PSO algorithm demonstrated 9.1 % higher energy generation than the Incremental Conductance MPPT, 19.8 % more than the PSO MPPT, and 20.7 % more than the Perturb and Observe MPPT. Scenario 2 showed the superior performance of the GWO-optimized PSO MPPT, showcasing a 15.36 % increase in generation over the PSO and a 21.62 % improvement compared to the Perturb and Observe MPPT, with a 4.74 % advantage over the Incremental Conductance MPPT. The results highlight the GWO-optimized PSO MPPT's robustness under diverse conditions, emphasizing its potential PV technologies by optimizing MPPT controllers.

Research on MPPT control strategy based on CCAOA algorithm

ABSTRACT Photovoltaic arrays operating under partial shading conditions (PSCs) may exhibit multiple peaks in the power-voltage curve of the PV system output. The conventional maximum power point tracking (MPPT) algorithm cannot address the multipeak problem and demonstrates slow convergence speed, often falling into a local optimum. Consequently, this study proposes a collaborative and cosine arithmetic optimisation algorithm (CCAOA). First, a cosine factor is introduced into the mathematical optimisation. Circle chaotic mapping and cross-variance strategy were incorporated to boost the diversity and randomness within the algorithm population followed by a cooperative search strategy involving addition and subtraction to enhance the algorithm’s local search capability, thereby accelerating its convergence speed. We assessed the effectiveness of the CCAOA using six IEEE standard test functions. The CCAOA outperforms other algorithms in terms of convergence speed and accuracy. Furthermore, when applied to the MPPT control strategy, the CCAOA’s performance is validated through simulations and experiments. MPPT can be performed within approximately 0.3 to 0.4 s under static conditions, with 99.98% efficiency. Under dynamic conditions, only approximately 0.4 s is required for MPPT, thereby achieving a maximum efficiency of 99.99%. Conversely, the AOA, TSO, and PSO algorithms require over 0.4 s for MPPT and can be trapped in local optima under these two conditions. Experiments were conducted using a PV simulator and DC/DC converter, and the algorithm achieved efficiencies of 99.22%, 99.33%, and 99.54% under uniform irradiation and two PSCs, respectively.

Comparative Analysis of Gravitational Search Algorithm and Particle Swarm Optimization for Solar MPPT

This work delves into the optimization of Maximum Power Point Tracking (MPPT) for photovoltaic (PV) systems through a comparative analysis of two advanced algorithms: the Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). With the escalating demand for renewable energy sources, enhancing the efficiency of solar panels has become crucial. MPPT techniques are pivotal in maximizing the power output from solar panels by adjusting to varying environmental conditions. This work implements GSA and PSO within a MATLAB environment to track the MPP of a solar cell array efficiently. The study systematically evaluates the performance, convergence speed, and stability of both algorithms under diverse operational scenarios. Preliminary findings indicate that GSA exhibits faster convergence and reduced oscillatory behaviour compared to PSO, suggesting a superior efficiency in tracking the MPP. This paper aims to provide a comprehensive comparison between GSA and PSO, offering insights into their applicability and effectiveness in optimizing solar energy systems, thereby contributing to the development of more efficient renewable energy solutions.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Enhancing photovoltaic system maximum power point tracking with fuzzy logic-based perturb and observe method

Photovoltaic systems have emerged as a promising energy resource that caters to the future needs of society, owing to their renewable, inexhaustible, and cost-free nature. The power output of these systems relies on solar cell radiation and temperature. In order to mitigate the dependence on atmospheric conditions and enhance power tracking, a conventional approach has been improved by integrating various methods. To optimize the generation of electricity from solar systems, the maximum power point tracking (MPPT) technique is employed. To overcome limitations such as steady-state voltage oscillations and improve transient response, two traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb and observe (P&O), have been modified. This research paper aims to simulate and validate the step size of the proposed modified P&O and FLC techniques within the MPPT algorithm using MATLAB/Simulink for efficient power tracking in photovoltaic systems.

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

This work aimed to develop and evaluate a maximum power point tracking (MPPT) control system for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). PMSG is commonly used to generate direct-drive and variable-speed wind energy. Initially, the generator and converter on the DC load side are controlled to follow the wind speed reference set by the MPPT algorithm. The paper presents the optimization problem formulation, including the optimization space, constraints, and objectives. The genetic algorithm (GA) is used to extract the maximum power from the WECS in this design improvement. In this study, to control and stabilize the maximum power point (MPP) of the wind turbine, a proportional integral (PI) controller and a GA heuristic approach were utilized. The GA approach was employed to determine the best settings (Kp, Ki) using MATLAB/Simulink with a 12.3 kW PMSG to model and simulate the proposed system. Based on four performance indicators-integrated squared error (ISE), integrated absolute error (IAE), integrated time absolute error (ITAE), and integrated time squared error (ITSE), the GA approach was used to optimize the controller settings. The results of the simulation show that the wind turbine (WT) can effectively track the necessary MPP. The simulation's output also includes generated power, DC bus voltage, electromagnetic torque, and currents.

Particle swarm optimization based sliding mode control for maximum power point tracking in solar PV systems

One of the most significant renewable energies is photovoltaic (PV) energy, however it has a low efficiency due to its variable maximum power point that depends on weather conditions. In order to guarantee the system's best performance, intelligent algorithms can effectively track this point in real-time utilizing the maximum power point tracking (MPPT) method. Consequently, it is crucial to maximize the use of the solar energy that has been captured as well as the PV system's generated electricity. Variations in solar irradiance affects the amount of electric energy obtained from solar arrays. For efficient extraction of electricity from solar PV systems, MPPT algorithms are required. Sliding mode control (SMC) can be used in the control of nonlinear systems. However, the effectiveness of SMC can be improved by the choice of the sliding coefficients. In this paper, optimal search using particle swarm optimization (PSO) is used in the design of the sliding manifold. Results obtained via simulations showed that MPPT tracking efficiencies obtained for the PSO based SMC and the conventional SMC are 99.65% and 96.79% respectively. That means, PSO based SMC is 2.86% better than conventional SMC.

Comparative Analysis of Mppt Techniques for Grid Tie Solar Pv Applications

For effective energy harvesting, photovoltaic (PV) systems must produce their maximum amount of power. This is especially true for grid-tie applications, where the objective is to feed generated power into the grid. The different Maximum Power Point Tracking (MPPT) methods used in grid-tie solar PV systems are compared in this paper. In order to guarantee that PV arrays function at their maximum power point in a variety of environmental circumstances, MPPT algorithms are essential. A DC-DC boost converter is also used for interfacing the load and photovoltaic module. Result is simulated in MATLAB/Simulink environment, and all the condition is kept same for both techniques in simulation for the best outcomes of this comparative study. The efficiency, accuracy, convergence speed, and implementation complexity of several MPPT techniques—Perturb and Observe (P&O), Incremental Conductance (INC), Fractional Open Circuit Voltage (FOCV), and Particle Swarm Optimization (PSO)—are assessed and contrasted in this study. The performance of each technique under various operating conditions is demonstrated through MATLAB/Simulink simulation results, which help in the selection of the optimal MPPT method for grid-tie solar PV applications.

A comparison of several maximum power point tracking algorithms for a photovoltaic power system

This paper presents a comparative study between traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for Photovoltaic (PV) powered DC Shunt Motors. Given the nonlinearity of PV systems, they require nonstandard approaches to harness their full potential. Each PV module has a unique maximum power point on its IV curve due to its nonlinear characteristic nature. Power electronic converters are utilized to enable operation at that point. There are many different algorithms described in the introduction, each with its have their own advantages and drawbacks. Recognizing the potential enhancement of PV system efficiency through effective Maximum Power Point (MPP) tracking, this paper evaluates five MPPT methods under varying DC loads. The five algorithms will be as follows: Incremental Conductance and Perturb and Observe as traditional algorithms. Fuzzy Logic Control, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference Systems as Intelligent Algorithms. While traditional algorithms generally produced acceptable results except for Perturb & Observe, intelligent algorithms performed well under rapidly changing solar radiation conditions. Due to inadequate data, intelligent algorithms relying on data training struggled to track the maximum power point when the temperature changed due to inadequate data used for the training. The analysis focuses on the time required by each method to reach peak power under different load conditions, solar irradiance, and temperature variations. The advantages and disadvantages of each MPPT with a shunt DC motor are detailed in the comparative study.

Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms.

Control strategies for grid-connected hybrid renewable energy systems: Integrating modified direct torque control based doubly fed induction generator and ANFIS based maximum power point tracking for solar PV generation

This research article introduces advanced control strategies for grid-connected hybrid renewable energy systems, focusing on a doubly fed induction machine (DFIM) based wind power plant and a photovoltaic based solar power plant. The proposed approach integrates Direct Torque Control (DTC) with modified filters and a fractional Proportional-Integral (PI) controller for the DFIM wind power plant. The modified DTC utilizes estimated torque and flux, employing a modified filter to minimize deviations, and a fractional PI controller for improved vector selection. This results in an enhanced duty cycle for the rotor side converter, optimizing power output to the grid. Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) algorithm is implemented for the solar power plant, dynamically adjusting the duty cycle for increased power output compared to conventional MPPT methods. Simulation results in MATLAB and experimental verification demonstrates the stability and feasibility of the proposed controllers, showcasing superior performance compared to conventional counterparts under various operating conditions.

Artificial Neural Network Control Applied to a Photovoltaic-Battery Microgrid System

This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.

Design and Implementation of an Intelligent Maximum Power Point Tracking Controller for Dual-Axis Solar Tracking Photovoltaic Systems

This research presents the design and implementation of an intelligent maximum power point tracking (MPPT) controller for dual-axis solar tracking photovoltaic (PV) systems. The proposed controller integrates an advanced MPPT algorithm with a dual-axis solar tracker mechanism to optimize energy extraction from PV panels. Sensor data is utilized to dynamically adjust the solar tracker's orientation and the PV array's operating point simultaneously. The system's hardware and software components are developed, including a microcontroller, sensors, motor drivers, and control algorithms. Experimental results demonstrate improved energy yield and efficiency compared to conventional fixed-axis or single-axis tracking systems. Keywords: Maximum Power Point Tracking, Dual-Axis Solar Tracking, Photovoltaic Systems, Intelligent Control, Energy Optimization

Comparative Analysis of Hybrid Maximum Power Point Tracking Algorithms Using Voltage Scanning and Perturb and Observe Methods for Photovoltaic Systems under Partial Shading Conditions

Partial shading significantly affects the performance of photovoltaic (PV) power systems, rendering traditional maximum power point tracking (MPPT) methods ineffective. This study proposes a novel hybrid MPPT algorithm integrating voltage scanning and modified Perturb and Observe (P&O) techniques to overcome the limitations posed by partial shading. This algorithm has a simple structure and does not require panel information such as the number of panels or voltage due to its voltage scanning feature. To test the proposed algorithm, a grid-connected PV power system with a power of 252.6 kW was created in the MATLAB/Simulink environment. In this power system, six different PS conditions, containing quite challenging situations, were listed in three different scenarios and simulated. The proposed algorithm was compared with the voltage scanning and P&O and voltage scanning and variable-step P&O methods. It was observed that the proposed algorithm has lower power fluctuations compared to the other two traditional methods. Additionally, this algorithm managed to achieve higher efficiency than the other methods in some cases.

Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.

Power equalization and optimization of photovoltaic module based on forward-flyback converter

This paper focuses on enhancing the energy extraction efficiency of photovoltaic (PV) modules through the use of a straightforward power converter and control algorithm. This research delves into the electrical characteristics of PV modules, explaining the concepts of global maximum power point, and local maximum power points. By integrating maximum power point tracking algorithms and differential power processing technology, an innovative scheme for power equalization and optimization of PV modules is introduced. The scheme is based on a single-switch multi-winding forward-flyback converter. Using the STP-340-72-Vfh-type PV module as a case study, a simulation model is developed with PLECS simulation software. The simulations cover 30 different irradiance scenarios. The findings illustrate the effectiveness of the proposed PV module power optimization system in achieving maximum power output under different irradiance conditions, achieving an average efficiency of 94.61%. This efficiency rate is 13.95% greater than that of existing global maximum power tracking schemes.

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.