Battery Energy Storage Devices Research Articles

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

Optimization of wind farm energy supply system costs through machine learning

At present, the energy crisis is becoming increasingly severe, and changing the energy structure and developing clean energy have gradually become the consensus of the world. Establishing a clean energy power supply system, using clean energy for power generation, and investing in real power supply are currently unresolved challenges. Based the current high cost of new energy power supply systems, this project proposes an optimization plan based on the wind power supply system, targeting the capacity of lithium battery energy storage equipment, and constructs an optimization model for wind power supply in electric vehicle systems. In addition, in order to use lithium-ion battery devices more efficiently and ensure the evaluation of the efficiency and battery life of lithium-ion battery energy storage devices, we predicted the cyclic aging phenomenon of lithium-ion battery energy storage devices through machine learning technology. This prediction can provide suggestions and references for the use of this lithium battery.

A Stabilization Control Strategy for Wind Energy Storage Microgrid Based on Improved Virtual Synchronous Generator

In high-penetration renewable-energy grid systems, conventional virtual synchronous generator (VSG) control faces a number of challenges, especially the difficulty of maintaining synchronization during grid voltage drops. This difficulty may lead to current overloads and equipment disconnections, and it has an impact on the security and reliability of the system, as well as limiting the dynamic reactive power support capability of the system. To solve this problem, in this study, a wind–solar hybrid power generation system is designed with a battery energy storage device connected on the DC side, and proposes a low voltage ride-through (LVRT) control strategy for the grid-connected inverter based on an improved VSG. The control strategy employs an integrated current limiting technique combining virtual impedance and vector current limiting to ensure that the VSG exhibits good dynamic power support characteristics during symmetrical faults by adjusting the setpoint value of reactive power. At the same time, it maintains the synchronization and power angle stability of the VSG itself to achieve the goal of LVRT. Simulation results show that the proposed control strategy can effectively suppress the renewable power fluctuations (about 30% reduction in fluctuations compared to the conventional strategy) and ensure the safe and reliable operation of the renewable energy sources and VSGs during grid-side faults. In addition, it provides a given reactive power support and stable grid voltage control (voltage dips reduced by about 20%), which significantly enhances the LVRT capability of the hybrid wind–solar-storage generation system.

Intelligent Solar PV Grid Connected and Standalone UPQC for EV Charging Station Load

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.

Control strategy and capacity optimization of energy-storage-based railway power conditioner

Electrified railroad has the characteristics of high safety factors, high comfort, large transportation capacity, and less time-consuming, which is an effective means to solve traffic congestion. However, with the rapid development of China’s electrified railroad, harmonic, negative sequence and other power quality problems have also been widely concerned, while more and more regenerative braking energy in the traction power supply system can not be effectively utilized. The input of railroad power regulators makes the power quality problem be solved effectively, this paper studies the control strategy of traditional railroad power regulators. Combined with the actual working conditions of railroad vehicles, the simulation model of traction network, vehicles, and railroad power conditioner is established, and the load characteristics of traction substation are simulated by this model. Secondly, for a domestic substation field collection data processing to obtain the actual train operation load changes, to the battery energy storage device life within the maximum economic returns as the optimization goal, for the peak load reduction judgment threshold and the capacity of the battery energy storage device through the genetic algorithm to process the data of previous years to optimize the design.

Low carbon scheduling method of electric power system considering energy-intensive load regulation of electrofused magnesium and wind powerfluctuation stabilization

The significant expansion of wind power has presented challenging obstacles to power system operation. The inherent stochasticity and variability of wind power have emerged as critical issues that impede the advancement of wind energy, affecting its integration and volatility control. Moreover, with the momentum of carbon peaking and carbon neutrality objectives, reducing CO2 emissions has become urgent. This study proposes a low-carbon dispatching methodology for power systems to address these challenges. It considers regulating energy-intensive loads of electrically fused magnesium and mitigating wind power fluctuations. The approach involves integrating electrically Fused magnesium loads with tight regulation characteristics on the demand side alongside thermal power plants to optimize the power grid and dispatch collectively. And battery energy storage devices are introduced into various wind power stations on the supply side to smooth out wind power fluctuations. The objective function aims to minimize the sum of the average variance of power fluctuations at all wind power busbars while adhering to constraints of maximum wind power consumption and minimum carbon emissions from thermal power plants. The problem is solved using genetic algorithms. Ultimately, we show how effective our proposed low-carbon dispatching method for power systems is, which deals with regulating energy-intensive loads of electrically fused magnesium and reducing wind power fluctuations. We demonstrate this through a case study of wind curtailment, CO2 emissions, wind power fluctuations, and other related parameters under different conditions. The approach can enhance renewable energy consumption, reduce carbon emissions, and mitigate the power fluctuations of renewable energy.

Leveraging cost-effectiveness of photovoltaic-battery system in metro station under time-of-use pricing tariff

As the cornerstone of contemporary urban transit infrastructure, the metro rail transit system significantly contributes to both energy consumption and carbon emissions. Recognizing the potential of rooftop photovoltaic (PV) applications in elevated stations to mitigate the carbon footprint of the metro system, harnessing this potential becomes imperative for advancing toward a cleaner future in the metro transit sector. However, the techno-economic feasibility of the rooftop PV application in elevated metro stations has not been fully reported in existing literature. Addressing this research gap, our study delves into the impact of the photovoltaic-battery (PVB) system on energy conservation and economic benefits within the elevated stations of a metro line in Beijing, China, and a multi-objective nonlinear mixed-integer programming (MIP) is formulated under a 3-tier time-of-use tariff scheme for minimizing grid load and maximizing economic benefits. The results demonstrate that the incorporation of both the battery energy storage device and the PV subsystem leads to an 8.3% and 19.2% reduction in annualized costs, respectively. When applied across the entire metro line, the PVB system is anticipated to yield a substantial annualized cost reduction ranging from 26.1% to 47.8%. Moreover, outcomes from the multi-objective MIP optimization reveal that the simultaneous achievement of economic benefits maximization and grid load minimization is infeasible, and the weighted sum method offers a decision-making platform for investors to choose one option or another according to extra criteria.

Optimal sizing of an integrated renewable energy system and effective utilization of surplus energy in electric vehicle charging

Renewable energy based power generation has proven to be a viable standalone option in areas where extending the grid is challenging. This study addresses this issue by assessing the possibility of an integrated renewable energy system (IRES) to electrify twelve villages in the Uttarakhand state of India. Four battery energy storage (BES) devices, namely Lead-Acid (LA), Sodium-Sulfur (NAS), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered for storage in this study. Using the Chimp optimization algorithm (ChOA) on the MATLAB© platform, eight different configurations consisting of solar photovoltaic (SPV) array, a micro-hydropower (MHP) plant, and a biogas generator (BGG) are modeled and optimized. The study reveals that the optimal IRES configuration with the lowest cost and highest performance comprises 676 SPV panels (260 kWp), one MHP plant (25 kW), one BGG (40 kW), and 648 NAS batteries (778 kWh). This configuration has a total system life cycle cost (LCC) of INR 68.77 million and cost of energy (COE) of 16.77 INR/kWh at 0 % loss of power supply probability. The optimization problem was run 1–50 times and found that the proposed ChOA algorithm is more robust compared to others, displaying the lowest Best, Worst, and Mean values of LCC (across all eight configurations), convergence rapidity (26th iteration), and least computational time (3481 sec). However, GWO (seven configurations), MFO (34th iteration), and GA (4154 sec) stand very close to ChOA performance in terms of providing minimum LCCs, convergence rapidity, and computational time, respectively. Furthermore, surplus energy (SE) is effectively utilized by incorporating electric vehicles (EVs) as dump load in the system. The proposed integrated charging (IC) strategy outperforms other charging strategies by energizing 134 EVs, utilizing 99.59 % of SE, and reducing the total COE to 10.57 INR/kWh. Finally, the proposed IC strategy results in a net saving of 94,479.39 tons of greenhouse gas emissions. These findings support the feasibility of implementing a standalone IRES to electrify the study area and provide electricity to EVs.

Multi-scenario design of ammonia-based energy storage systems for use as non-wires alternatives

Energy storage can be used by power distribution system operators as a non-wires alternative to defer infrastructure upgrades and improve feeder reliability. One emerging energy storage technology is energy storage via the synthesis and subsequent consumption of chemicals in internal combustion engines or fuel cells (i.e., ”chemical energy storage”). Some chemicals, such as hydrogen and ammonia, can be synthesized from renewable, carbon-free feedstocks using excess renewable generation. In this work, we develop an optimization model for investing in and using mobile ammonia-powered generators, ammonia storage equipment, and mobile batteries in tandem to provide non-wires alternatives to distribution networks efficiently. Specifically, we develop a mixed-integer quadratically constrained program to optimize the design and operation of distribution systems with ammonia and battery energy storage devices under multiple operational scenarios. This formulation is applied in a case study on a 15-bus test system. Ultimately, we find that ammonia-powered generators enable flexible operation and are best suited for long-term, high-total-energy use cases, whereas batteries are best suited for short-term use cases. Additionally, we find that a combination of chemical and electrochemical energy storage is more cost effective at responding to a variety of operational conditions than either form of energy storage alone.

Optimal nanogrid planning at building level

This paper presents a planning framework for active buildings as an Energy Nano-Grid (ENG), determining the optimal size and generation mix of distributed energy resources (DERs) and battery energy storage (BES) system, the type of ENG that can be either AC or DC, and the optimal energy management (EM). Due to the increasing penetration of battery energy storage devices, electric vehicles (EVs) and even DC loads on the utility side, DC ENGs would potentially be more useful than AC ENGs by reducing the number of converters, facilitating the connection of various types of distributed energy resources and loads to the common bus with simplified interfaces, and mitigating the losses associated with AC/DC energy conversion. Therefore, the selection of the type of ENG is an economic issue where the planning objective includes the investment, operation and maintenance costs of energy resources, the investment costs of battery energy storage (BES) and converters, and the costs/revenues for buying/selling energy from/to the upstream grid or neighbor ENGs. In this way, the proposed program achieves an optimal load sharing. Optimal results might be affected in terms of some system specifications such as the ratio of DC load (from 0.4 to 1) at ENG, the maximum permissible installation capacity of BESs (from 200 to 800 kWh), and maximum discharge power that EVs can deliver to the ENG or upstream network (from 50 to 200 kW). Using some numerical case studies associated with three residential ENGs, result show that increase in the rate of DC load has the highest effect on the type of ENG (DC feeder is adopted for DC load rate 0.6 at ENG 1 and ENG 2, and 0.8 at the third one) through decrease in investment and operation costs, meanwhile, the capacity of BES directly affect the size of generation units, and the maximum discharging power of EVs just support peak load supply due to being out of the park lot during the day. The proposed planning model is analyzed in detail to demonstrate its applicability, effectiveness and control.

Secure expansion of energy storage and transmission lines considering bundling option under renewable penetration

This paper presents a multi-stage expansion model for the co-planning of transmission lines, battery energy storage (ES), and wind power plants (WPP). High penetration of renewable energy sources (RES) is integrated into the proposed model concerning renewable portfolio standard (RPS) policy goals. The possibility of bundling existing transmission lines to uprate power flow capacity is considered. Renewable energy curtailment and load shedding are included in the model to assess the system operation more precisely. Battery ES devices are co-planned to defer transmission expansion and renewable management. To make the time complexity of the problem tractable and capture the uncertainties of load and RES in an hourly resolution, a chronological time-period clustering algorithm is used to extract the representative hours of each planning stage. Additionally, the flexible ramp reserve is utilized to handle the uncertainty of RES. An accelerated Benders dual decomposition (BDD) algorithm is developed to solve the proposed model mixed-integer linear programming (MILP) formulation. The N-1 security criterion is evaluated by considering a designed contingency screening (CS) algorithm to identify higher risk contingencies. The effectiveness of the proposed co-planning model is evaluated using IEEE RTS 24-bus and IEEE 118-bus test systems.

Mathematical Modelling and Simulation of Second Life Battery Pack with Heterogeneous State of Health

The service life of Lithium-ion batteries disposed from electric vehicles, with an approximate remaining capacity of 75–80%, can be prolonged with their adoption in less demanding second life applications such as buildings. A photovoltaic energy generation system integrated with a second life battery energy storage device is modelled mathematically to assess the design’s technical characteristics. The reviewed studies in the literature assume, during the modelling process, that the second life battery packs are homogeneous in terms of their initial state of health and do not consider the module-to-module variations associated with the state of health differences. This study, therefore, conducts mathematical modelling of second life battery packs with homogenous and heterogeneous state of health in module level using second-order equivalent circuit model (ECM). The developed second-order ECM is validated against experimental data performed in the lab on 3Ah NCM batteries. The degradation parameters are also investigated using the battery cell’s first life degradation data and exponential triple smoothing (ETS) algorithm. The second-order ECM is integrated with the energy generation system to evaluate and compare the performance of the homogenous and heterogeneous battery packs during the year. Results of this study revealed that in heterogeneous packs, a lower electrical current and higher SOC is observed in modules with lower state of health due to their higher ohmic resistance and lower capacity, compared to the other modules for the specific battery pack configuration used in this study. The methodology presented in this study can be used for mathematical modelling of second life battery packs with heterogenous state of health of cells and modules, the simulation results of which can be employed for obtaining the optimum energy management strategy in battery management systems.

N‐Doped Carbon‐Coated Mixed‐Phase SnS–SnS2 Anode with Carbon Nanofibers Skeleton for Improving Dual‐Ion Battery in Concentrated Electrolyte

Metal sulfides exhibit great potential for anode material due to the lamellar structure. However, the intrinsic low conductivity and large volume expansion result in poor cycle stability. Herein, a porous hierarchical structure (SnS–SnS2)–NC–CF anode material composed of carbon nanofibers skeleton and N‐doped carbon is designed, and a full dual‐ion battery based on the anode is constructed, coupled with natural graphite and high‐concentration electrolyte (6 M LiTFSI + 5% vinylene carbonate). The graphite/(SnS‐SnS2)–NC–CF dual‐ion battery shows a high initial discharge specific capacity up to 154.3 mAh g−1 at a current density of 100 mA g−1 in a working voltage window of 1.0–4.0 V. Also, the battery obtains a considerable capacity of 67.1 mAh g−1 at the high current density of 1000 mA g−1 and achieves an excellent cycling performance up to 2000 cycles with remarkable Coulombic efficiency near 100%. In addition, the battery exhibits a low self‐discharge of 1.83% h−1 and superior fast charge performance. Furthermore, the results of the X‐ray diffraction and scanning electron microscope characterizations further demonstrate good reversibility and stability. Consequently, this work provides a feasible strategy for the design of a high performance dual‐ion battery energy storage device.

Research on optimal operation of grid-connected microgrid based on improved whale algorithm

In this paper, a small microgrid system is composed of wind turbines, photovoltaic generators, micro gas turbines, fuel cells and battery energy storage devices. In order to realize the optimal operation of the grid-connected microgrid, a multi-objective optimal operation model with economical and environmental friendliness is established. Secondly, an adaptive strategy is introduced for the whale optimization algorithm (WOA) to improve the optimization accuracy and convergence speed of the whale algorithm. The improved whale algorithm is used to solve the target model, and the optimal operation scheme of each distributed power source in the microgrid is obtained, and the simulation analysis results are compared to verify the rationality of the improved whale algorithm.

AN OVERVIEW OF SOC ESTIMATION IN LI-ION BATTERIES WITH DIRECT MEASUREMENT METHODS AND COULOMB COUNTING

The current spike in demand for new and better battery technologies and energy storage devices has intensified as a result of the recent boom in the electric car industry. A better battery management system enables a better solution for the desired application, allowing the batteries to perform to their full capacity. As a result, improving Li-ion battery technology, not only in chemistry but also in maintenance, performance, and efficiency, has become critical to meeting today's demand. Lithium-ion batteries are used in a variety of applications, and battery management systems (BMS) guarantee that the batteries last a long time and are properly utilized. BMSs are sophisticated and generate significant overhead consumption, which has an impact on the batteries. The SOC (State of Charge) Estimation, which assesses the ratio of accessible capacity to the maximum potential charge stored in the battery, is an incredibly important metric for this. The state of charge (SOC) of a battery indicates its usable capacity. It is one of the most important variables to monitor to improve the efficiency of lithium-ion batteries. Estimating the state of charge (SOC) of a battery is a serious challenge. Because the SOC is an important statistic for determining battery performance, accurate estimation of the SOC may protect the battery, reduce overcharging, extend its life, and allow the application to adopt energy-saving control measures. A battery, on the other hand, is a chemical energy storage source that cannot be accessed quickly. Estimating a battery's SOC is difficult as a result of this issue.

Using inventory as energy storage for demand-side management of manufacturing operations

Traditionally, electrical grids were designed to have unidirectional flow of power and related services from the utility to the end-user. Modern grids are now tasked with bi-directional flow of power and information. To capitalize on this reality, utilities have designed programs that financially incentivize energy consumers to adjust their energy profile in such a way that is advantageous to the utility. At the same time, users seek to maximize the benefits offered by the utilities. Since this is a complex decision-making process, particularly as users continue to adopt energy storage, solar photovoltaics, and other distributed energy resources, the need for decision support tools is increasingly important. To maximize the financial incentive received by the user, and the benefit gained by the utility, this paper proposes an optimization model to aid manufacturers in operating a battery energy storage device while also using product inventory as additional energy storage. Using inventory as additional energy storage is accomplished by scheduling production to build buffers of inventory during low electricity cost times so that production may be reduced during high electricity cost times. We refer to this problem as the energy job-shop scheduling problem (EJSSP). To accomplish these efforts, a deterministic optimization model is developed in order to determine optimal production and energy storage schedules. It is shown that under the proper conditions, utilities can work directly with energy users such that both parties stand to gain from cooperation with one another. Using several synthetic scenarios, it is found that more flexible manufacturing configurations can have as high as 25% more relative energy cost savings when compared to their more rigid counterparts. Moreover, production scheduling allows inventory to be effectively used as energy storage. Ultimately, the methodology forms the foundation of a gateway for electric utilities and their customers to align their interests and act cooperatively.

A distributed energy management scheme with the extended optimization horizon for Energy Internet

This paper proposes a distributed energy management with the extended optimization horizon for Energy Internet to minimize the system operation cost. The scheme dispatches various of controllable distributed energy sources of the traditional energy generation and battery energy storage devices. Two original contributions make the proposed scheme distinctive from the existing studies. Firstly, both the classical alternating direction method of multipliers (ADMM) and symmetric ADMM are combined in the new scheme to acquire fast and stable energy management policy, which improves the efficiency of energy management decisions. Secondly, an extended optimization horizon is designed based on the data from the previous day to simultaneously minimize the long-term cost and maintain battery state-of-charge. Simulation results from Pecan Street dataset demonstrate the superiority of the proposed method. Compared to existing distributed methods, the convergence time is reduced by almost 80% and the long-term cost is reduced by 47.8%. • A distributed energy management scheme is proposed for Energy Internet. • A hybrid ADMM is proposed for a fast and stable energy management policy. • An extended optimization horizon is designed to minimize the long-term cost. • The efficiency of the proposed scheme is verified through Pecan Street dataset.

Benefit maximization and optimal scheduling of renewable energy sources integrated system considering the impact of energy storage device and Plug-in Electric vehicle load demand

The intermittent nature of renewable-based generation may cause the dip or rise in generation and load imbalances. This paperwork obtains optimal generation scheduling, market benefit maximization, and daily energy loss minimization considering the impact of Plug-in Electric vehicles (PEV) and battery energy storage devices using nonlinear programming. The Plug-in EV load demand has been modelled using the Monte Carlo simulation (MCS), and the size of the energy storage device is obtained in the renewable energy source (RES) Microgrid distribution system. The proposed work's main contributions are (i) total cost-benefit maximization, (ii) daily energy loss minimization, (iii) optimal generation scheduling of RES based Microgrid and (iv) probabilistic modelling of PEV load demand and BES sizing. Furthermore, the results were compared to those available in the literature. The voltage variations in the Microgrid are also calculated with the effects of PEV and BES into account. The IEEE-33 bus test system was the subject of the investigation. The multi-objective problem has been solved using iterative Monte Carlo Simulation (MCS) and Nonlinear Programming (NLP). The proposed approach was implemented using MATLAB and GAMS interface. The daily energy loss has been decreased by 30.085 %, and the voltage deviation (VD) has been minimized by1.165 %.

A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse. It is solved using a Benders-dual cutting plane algorithm and a column and constraints generation algorithm in a tractable manner. A dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution is proposed to improve the results. A secondary contribution is to use a recently developed deep learning model known as normalizing flows to generate quantile forecasts of renewable generation for the robust optimization problem. This technique provides a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Overall, the robust approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. The case study uses the photovoltaic generation monitored on-site at the University of Li\`ege (ULi\`ege), Belgium.

Energy management system for a small-scale microgrid

In recent years, the power system has been evolved into microgrids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on microgrids is gaining popularity day by day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. To solve these issues, a variety of novel approaches have been explored in the literature. For the stand-alone microgrid in this research, efficient energy management and control mechanism is adopted. A photovoltaic system, a wind turbine, and a battery energy storage device make up this stand-alone microgrid. The power stability of the hybrid system is ensured by a sophisticated controller. The main purpose of this study is to regulate the DC/DC bidirectional converter (DBC), which connects the Li-ion battery to the DC bus of the stand-alone microgrid. This paper describes the development of a wind/photovoltaic power generation system to the load, as well as MPPT techniques like perturb and observe (P&O). The system is simulated and the results are presented using MPPT techniques. There is no requirement for a specific power model in the suggested method. Only power and voltage system data are used by DBC. A stand-alone microgrid system was simulated using MATLAB.