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Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality. [ABSTRACT FROM AUTHOR]

Off-grid wind energy systems play a pivotal role in providing clean and sustainable power to remote areas. However, the intermittent nature of wind and the absence of grid connectivity pose significant challenges to maintaining consistent power quality. The wind energy conversion system plays a central role in tapping renewable energy from wind sources. Operational parameters such as rotor and stator currents, output voltages of rectifiers and converters, and grid phase voltage variations are crucial for stable power generation and grid integration. Additionally, optimizing power conversion output through voltage gain analysis in boost converters is essential. Moreover, ensuring electricity quality via total harmonic distortion reduction in inverters is vital for grid compatibility. Goal. Enhancing the power quality of grid-integrated wind energy conversion systems. Methods. The proposed topology is implemented in MATLAB/Simulink with optimized control strategies for enhancing power quality in off-grid wind energy systems. Results. Control strategies with a grid-connected wind energy conversion system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for off grid-integrated wind system. References 18, table 1, figures 11. [ABSTRACT FROM AUTHOR]

In close connection with the issue of global security, the issue of ensuring energy resources has been put on the table of consumers and producers of various forms of energy, researchers, but especially politicians and social planners. Ensuring energy independence was put into question after the onset of the oil crisis in 1973, thus the various energy sources and their associated technologies are gradually being replaced by new technologies that integrate renewable energy sources, capable of taking over at an increased level of efficiency the energy requirement. [ABSTRACT FROM AUTHOR]

Summary As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

Currently, both regulated and deregulated power trading exist in China’s power system, which has caused imbalanced funds in the electricity market. In this paper, a simulation analysis of the electricity market with wind energy resources is conducted, and the calculation methods of unbalanced funds are investigated systematically. In detail, the calculation formulas of unbalanced funds are illustrated based on their definition, and a two-track electricity market clearing model is established. Firstly, the concept of the dual-track system is explained, and the specific calculation formulas of various types of unbalanced funds are provided. Next, considering the renewable energy consumption, the market clearing model based on DC power flow is constructed and solved; by combining fitting methods of mid- and long-term curves, the unbalanced funds are calculated based on clearing results and formulas. [ABSTRACT FROM AUTHOR]

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources. [ABSTRACT FROM AUTHOR]

Today's environmental concerns, particularly those related to global warming, have sparked a drive for the usage of renewable energy sources. One of the most significant sources of renewable energy is wind energy and wind energy conversion system are preferred to harvest wind energy. Due to the growing sophistication of wind energy conversion systems, new strategies based on advanced analytics are needed. In this study, reinforcement learning implemented in wind energy has been reviewed, the most popular approaches for various applications are identified, and it has been shown that reinforcement learning may be utilized in place of traditional approaches. According to the application, the techniques are examined and divided into four groups: optimal control, prediction and forecasting, optimization, and other techniques. Consequently, many literature has reported that, on an average, reinforcement learning has improved performance by 5% to 20% than existing methods. Moreover, around 85% of the 153 references included in this article were published after 2018. The purpose of the work is to provide a basis for future research on reinforcement learning applied in wind energy that may be crucial to energy sustainability. The report also addresses the discussion on the reinforcement learning current state, limitations, and future scope. [ABSTRACT FROM AUTHOR]

Introduction. The integration of Renewable Energy Sources (RESs), particularly Wind Energy Conversion Systems (WECS), is vital for reducing reliance on fossil fuels and addressing climate change. However, this transition poses challenges, including ensuring grid stability in the face of intermittent RESs. Compliance with grid codes is crucial, with a focus on Low Voltage Ride Through (LVRT) capability. Problem. The intermittent nature of RESs, specifically in Permanent Magnet Synchronous Generator (PMSG) based WECS, presents challenges to grid stability during voltage dips. Goal. To enhance voltage stability and LVRT capability in PMSG-based WECS by integrating a Dynamic Voltage Restorer (DVR) with an energy storage device. This involves regulating the input DC voltage to the DVR using a type 2 fuzzy controller, adapting intelligently to changing conditions. Methodology. DVR, powered by an energy storage device, is strategically integrated with WECS. A type 2 fuzzy controller regulates the DC voltage to DVR. The rectified WECS output undergoes processing through an isolated flyback converter. A 31-level Cascaded H-Bridge Multilevel Inverter (CHBMLI) with PI control ensures high-quality AC output. Results. The validation of developed system is executed using MATLAB/Simulink revealing a reduced Total Harmonic Distortion (THD) value of 1.8 %, ensuring significance in LVRT capability. Originality. The strategic integration of DVR with PMSG-based WECS, addresses the LVRT challenges. The use of type 2 fuzzy controller for intelligent voltage regulation and a sophisticated multilevel inverter contributes to the uniqueness of proposed solution. Practical value. The developed system provides benefits by ensuring reliable LVRT capability in PMSG-based WECS with reduced THD of 1.8 % indicating improved grid compatibility. References 26, tables 5, figures 20. [ABSTRACT FROM AUTHOR]

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. [ABSTRACT FROM AUTHOR]

Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE's two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system. [ABSTRACT FROM AUTHOR]

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV /WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs. • A novel hybrid SPSA based LADRC is designed for grid-connected PV/WECS. • The SPSA-LADRC performance was investigated under wind speed and irradiance variation. • It regulates the DC-Link voltage, improves GP tracking speed and dynamic performance. • The Hardware-In-Loop experiment validates the implementation feasibility. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources, especially offshore wind (OSW), is introducing frequency stability challenges to electric power grids. This paper presents a novel hybrid deloading control strategy that enables modular multilevel converter (MMC)-based wind energy conversion systems (WECSs) to actively contribute to grid frequency regulation. This research investigates a permanent-magnet synchronous generator (PMSG)-based direct-drive configuration, sourced from the International Energy Agency's (IEA's) 15 MW reference turbine model. Specifically, phase-locked loop (PLL)-free grid-forming (GFM) control is employed via the grid-side converter (GSC), and DC-link voltage control is realized through the machine-side converter (MSC), both of which boost the energy support for the integrated AC grid's frequency stability. This control strategy combines the benefits of over-speeding and pitch control modes, facilitating smooth transitions between different modes based on real-time wind speed measurements. In addition, the practical challenges of MMCs, such as circulating currents and capacitor voltage imbalances, are addressed. Numerical simulations under varying wind speeds and loading conditions validate the enhanced frequency regulation capability of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling and optimizing a hybrid renewable energy system, specifically consisting of a photovoltaic (PV) generator and a wind energy conversion system comprising a wind turbine and a permanent magnet synchronous machine. The primary control objectives involve maximizing power extraction from both the PV and wind turbine while ensuring efficient injection of the extracted energy into the electrical network and maintaining a suitable power factor. This study presents two key innovations that distinguish it from existing research. Firstly, it introduces a new hybrid PV/aerogenerator energy source that eliminates the need for a DC/DC converter in the PV structure. Secondly, unlike previous works that rely on various measurements such as wind speed, temperature, and irradiance, our approach only requires measurements of electrical variables. This advancement significantly reduces the number of sensors required and enhances the overall reliability of the system. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive solution to the challenges of managing a hybrid microgrid that generates electricity from multiple sustainable energy sources by proposing a coordinated energy management strategy and storage system. As renewable energy generation becomes increasingly popular, it introduces greater intermittency and stochasticity in energy management. To address this issue, a coordinated energy management strategy and storage system is proposed, which includes a fuzzy logic modified super twisting algorithm (MSTA). The objectives are to optimize the design and operation of microgrid including electrical based energy conversion systems such as photovoltaic and wind turbines, fuel cells, tidal energy, electric vehicle charging stations, and main grid. The second objective is to develop an energy management system for hybrid energy storage systems (HESS) and renewable energy sources (RESs) to maximize power production and ensure service continuity and smooth output energy of the microgrid, while also providing optimal benefits. To maintain cost‐effectiveness, an On/Off maximum power point tracking (MPPT) algorithm is also proposed. The contribution of this paper is to provide a solution to the intermittent and stochastic nature of renewable energy management and to improve the efficiency and durability of the energy conversion systems. The proposed management unit provides consistent output power and long‐term service. [ABSTRACT FROM AUTHOR]

In this study, we modeled and designed a novel, efficient controller for a hybrid renewable energy system with an integrated converter and associated grid interface. The proposed grid-interfaced Hybrid Renewable Energy System (HRES) model is built by properly connecting the photovoltaic (PV) system, Wind Energy Conversion System (WECS), battery, DC/DC converter, Maximum Power Point Tracking (MPPT) controller, microgrid, and load to obtain the desired output. The integrated converter used in this is a modified high-conversion ratio converter for improving conversion efficiency. A maximum power point tracker is used to track the maximum power from renewable energy sources. We have designed an efficient controller with a successful control strategy to provide optimal switching for the grid-side inverter to achieve optimal energy management in the system. A novel control method is developed by combining the Adaptive Grasshopper Optimization Algorithm (AGOA) and the Gradient Boosting Decision Tree Algorithm (GBDT), which is named the AGOA-GBDT method. The proposed approach uses AGOA as an evaluation technique to develop accurate command signals and enrich the command signal database for offline use. The data sets collected from the sensors are also used to build a control system with fast feedback for online training of the GBDT system. The main purpose of using and proposing this novel control technique is to improve efficiency by achieving optimal energy management in the model. The formulation of the problem considers several constraints, such as the intermittent nature of renewable energy sources, the state of charge of the storage components, and the power demand. The MATLAB/Simulink platform simulates the proposed system model with the proposed controller and other controllers. This proposed control technique proved to be the best in efficacy and convergence properties when compared with other existing control techniques in the literature. [ABSTRACT FROM AUTHOR]

The wind, stochastic in nature, is one of the fastest-growing and most promising renewable energy resources in the entire world. Thus, this paper investigates the influence of parameter uncertainties upon a dynamic performance of a grid-tied Doubly-Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS). The main uncertain parameters found in the study are mutual and rotor winding reactances which occurred due to the variation of the angular positions of the rotor caused by varying wind speeds. The variation in the wind speed caused the generator rotor speed to deviate between 25% and 150%. Consequently, the rotor winding reactance of DFIG changes from its nominal value of 1.31 mΩ to between 0.983 and −0.655 mΩ; and the mutual reactance from its nominal value of 0.941 Ω to between 0.758 and −0.4708 Ω. As a result, the stator and rotor winding voltages and currents of the DFIG are uncertain. [ABSTRACT FROM AUTHOR]

Among the most efficient sources of renewable energy and a potential significant source of electricity is wind energy. Wind energy is perfect for dependable energy systems since it can produce electricity at any time of day. Wind power is a dependable source that may produce renewable energy sources continuously and sustainably. However, there are several drawbacks to wind energy, including as the high initial building costs, the requirement for immovable property, and the challenge of locating ideal locations. In reading, a machine learning (ML)-based scheme is used to forecast the output of wind energy. We suggested a technique for precisely predicting WPs using machine learning techniques. The major goals of this inquiry are to forecast wind production and collect data on daily wind speed control. Only the daily mean wind velocity values were analysed to see if the wind energy estimates provided by the wind speed data were sufficient. The results demonstrated that machine learning techniques could anticipate long-term wind energy values using previous wind data. The results also demonstrate that the machine is designed to find spots that diverge from previously learnt sites so that they can be added to models [ABSTRACT FROM AUTHOR]

The Challenge to stabilize the grid frequency increases with the increment of renewable energy resources. System inertia/frequency control is a significant concern for maintaining system stability with a fast response time during the load transition. This manuscript proposes surface-mounted permanent magnet synchronous generator (SPMSG) based wind energy conversion system (WECS) with a frequency support DC-link controlling loop and a converter protective DC-link voltage-controller frequency support system. To achieve power exchange, the frequency control system (FCS) for the SPMSG-based wind turbine supports the grid-side virtual inertia. The DC-link voltage is disturbed during the load transition to maintain the frequency, while the electrolytic capacitor requires extra care regarding the DC bus voltage. This manuscript incorporates the FCS system with a supercapacitor and dynamic voltage limiter to avoid additional care of the DC bus voltage. A detailed analysis of system frequency with the additional load increment, normal connected load decrement, and various fault scenarios has been done. The proposed system is compared with the existing virtual inertia support (VIS). The simulation results show that the proposed frequency control system-based VIS efficiently limits the frequency deviations & DC-link voltage. The results are verified in the OPAL-RT 4510 real-time simulator environment to ensure the efficacy of the proposed system. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to prevent the destruction of other parts of a wind energy conversion system because of faults, the diagnosis of insulated-gate bipolar transistor (IGBT) faults has become an essential topic of study. Demand for sustainable energy sources has been prompted by rising environmental pollution and energy requirements. Renewable energy has been identified as a viable substitute for conventional fossil fuel energy generation. Because of its rapid installation time and adaptable expenditure for construction scale, wind energy has emerged as a great energy resource. Power converter failure is particularly significant for the reliable operation of wind power conversion systems because it not only has a high yearly fault rate but also a prolonged downtime. The power converters will continue to operate even after the failure, especially the open-circuit fault, endangering their other parts and impairing their functionality. Design/methodology/approach: The most widely used signal processing methods for locating open-switch faults in power devices are the short-time Fourier transform and wavelet transform (WT) – based on time–frequency analysis. To increase their effectiveness, these methods necessitate the intensive use of computational resources. This study suggests a fault detection technique using empirical mode decomposition (EMD) that examines the phase currents from a power inverter. Furthermore, the intrinsic mode function's relative energy entropy (REE) and simple logical operations are used to locate IGBT open switch failures. Findings: The presented scheme successfully locates and detects 21 various classes of IGBT faults that could arise in a two-level three-phase voltage source inverter (VSI). To verify the efficacy of the proposed fault diagnosis (FD) scheme, the test is performed under various operating conditions of the power converter and induction motor load. The proposed method outperforms existing FD schemes in the literature in terms of fault coverage and robustness. Originality/value: This study introduces an EMD–IMF–REE-based FD method for VSIs in wind turbine systems, which enhances the effectiveness and robustness of the FD method. [ABSTRACT FROM AUTHOR]

Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, the energy conversion and utilization are challenging due to the technique reliability and cost issues. Hydraulic wind turbine (HWT) may solve the above problems. HWT is taken as a research object, and the maximum power point tracking (MPPT) control strategy is proposed collaborating with active disturbance rejection control (ADRC) and linear quadratic regulator (LQR) control methods, to solve multiplicative nonlinearity problems in the plant models and the influence of external disturbance on control performance in the MPPT control process. A nonlinear simulation model is built to explain the main findings from the experiments and obtain a better understanding of the effect of time‐varying system parameters and random fluctuation in wind speed. The collaborative control algorithm is experimentally verified on a 24‐kW HWT semi‐physical test platform that results in a promising energy conversion rate, plus the hydraulic parameters can satisfy the demand, accordingly. Ultimately, the potential challenges of implementing this technique in a smart wind energy conversion system are discussed to give a further design guidance, either theoretically or practically. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples. [ABSTRACT FROM AUTHOR]

Summary: As the share of wind power keeps on increasing, the interaction between synchronous and wind generators pose a challenging issue on power system stability. In order to investigate the impact of wind penetration on stability of power system, appropriate modeling of wind energy conversion systems (WECSs) is necessary. Moreover, it is needed to comprehensively study the impact of wind penetration on power system oscillations. This paper presents a didactic approach for integrating a doubly fed induction generator (DFIG)‐based wind farm from SimPower Systems library to a five‐area 68‐bus power system modeled in Simulink. Inter‐area oscillations with and without wind power penetration are investigated for their characteristics. Renewable energy sources are connected to the grid via power electronics converters at the cost of a reduction in inertia. The concept of virtual synchronous generator (VSG) produces virtual inertia by exchanging active power with the power system. The impact of superconducting magnetic energy storage (SMES) and DFIG on enhancing damping performance of inter‐area is investigated. The increase in damping ratios of inter‐area oscillatory modes verifies the enhancement in small signal stability by connecting SMES and DFIG to the power system. The usage of SMES operating in VSG mode to improve the dynamic stability with highly erratic wind profile is also investigated. The developed MATLAB/SIMULINK‐based DFIG model is simple to use and can be expanded to build efficient controllers. The fidelity of the DFIG model and VSG technology is verified in real time by Opal‐RT (OP4510). [ABSTRACT FROM AUTHOR]

This study investigates the integration concerns of next-generation renewable energy systems, in which the grid is turning more disseminate and accessible. In this case, the Permanent Magnet Synchronous Generator (PMSG) based wind energy conversion systems are expected to be more beneficial. Provision of power quality improvement such as Harmonic Reduction is the basic key to obtain higher utilization of wind energy. This functionality for future systems leads to lower energy cost, and reliable power system. To incorporate the advanced features, a five level cascaded H - bridge inverter, with reduced number of power electronic switches is progressed in this study, that is configured within the renewable source systems such as solar and wind. The study is carried out on a 10 KWPMSG based wind energy conversion system interfaced to grid. The proposed concept is elucidated through modelling, simulating and analysing in MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

There are few pieces of research on switched reluctance generator (SRG) that accomplish optimal performance torque control, most prefer to use voltage, speed, or power control strategies. This paper proposes a direct average torque control (DATC) for SRG applied to wind energy conversion systems (WECS). The SRG is driven by an asymmetric half‐bridge converter (AHB), and an external chopper regulates the DC‐bus voltage. In the DATC strategy, the switching logic is modified, so that the machine operates in the generator mode. The dynamic behavior of an artificial neural network (ANN)‐based and the conventional co‐energy‐based torque estimation methods are discussed. Thereafter, the proposed DATC is compared to the direct power control (DPC) strategy. For both strategies, lookup tables are created to store the optimal switching angles. These lookup tables are optimized to achieve a trade‐off between torque ripple minimization and efficiency maximization. Experimental results show that the DATC strategy presents a lower torque ripple than the DPC method over the entire speed range. In contrast, DPC delivers superior efficiency for low and medium speeds. [ABSTRACT FROM AUTHOR]

Hybrid renewable power generation becomes essential in most of electric power networks. Battery storage is commonly used in renewable energy systems (RESs) with distributed generation, such as solar and wind energy systems, to reduce power fluctuations caused by the intermittent behavior of renewable energy sources. A battery has been connected with the dc grid through a dc–dc bi-directional converter. The control scheme of the dc–dc bi-directional converter (BDC) is having two controlled loops. One is voltage-controlled loop, and another is the current-controlled loop. In this work, two-degree-of-freedom fractional order proportional integral derivative + proportional integral controller (2-DOF FOPID + PI) is proposed and applied in the voltage-controlled loop to stabilize the dc bus voltage and power flow effectively for both islanded mode and grid-connected mode. A novel meta-heuristic algorithm, i.e., modified sine cosine algorithm (m-SCA), is used to tune the controller parameters of the proposed controller. To test the performance of the modified SCA algorithm, various well-known uni-modal and multi-modal benchmark functions are taken in this paper. To confer the viability of the proposed controller, its performance is compared with other literature-based conventional controllers. This paper presents time-domain analysis, stability, and robustness test to illustrate the effectiveness of the proposed controller. MATLAB/Simulink has been used for computational work to analyze the system's efficiency. Finally, the proposed technique is validated by using OPAL-RT platform (Opal-RT-OP4510 model) with the power management of RES (PV and wind) and battery energy storage. Experimental work proved the effectiveness of the proposed m-SCA optimized 2-DOF FOPID + PI controller. [ABSTRACT FROM AUTHOR]

In this paper, a robust current control of the hybrid renewable energy system (HRES), based on the PV-Wind system, is proposed. The HRES is connected to a multiport converter to synchronize the multi-source system with one DC-Bus. Due to their ability to integrate many renewable energy sources (RES) individually or simultaneously, multiport converters (MPC) are an innovative method suitable for renewable energy applications. Recently, many DC-DC converter designs and topologies have emerged to ensure the highest possible efficiency of hybrid RESs. The multiport converter is a typical coupling system with several modes of operation. Thus, the design of its controller become complicated. To stabilize the DC-Bus voltage, a battery has been added to the system. In this HRES configuration, all sources are connected in parallel via the multiport DC converter. We used the multiport DC converter to minimize the intermittent character of solar and wind and control the energy flow between the different power sources and the load, as well as to increase the performance of the system. The nonlinear robust control structure is based on Lyapunov approach to overcome the nonlinear model of the system to improve robustness and guarantees the asymptotic stability. The proposed control law is implemented and tested on dSPACE-DS1104. The results show the effectiveness and the feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

Electricity from renewable energy is certainly the most prominent alternative to deliver power to remote locations; however, its reliability is affected by the intermittency of the renewable source. A smart load technology called 'electric spring (ES)' can compensate for the intermittency and thereby maintain the voltage level constant for critical loads with an efficient and reliable control approach. This paper suggests wind energy-fed generation as the only and primary source for an isolated remote microgrid. The voltage level of the system remained unaltered by deploying a voltage source converter-based ES with a novel artificial neural network-based fuzzy controller. The novelty of the system is that it can operate significantly under varying load, variable torque, and varying wind speed conditions by exchanging power between critical and non-critical loads. Further simulation results ensure the credibility of the electric spring–artificial neural network-based fuzzy controller system in terms of stability and performance parameters such as settling time, rise time, and maximum overshoot. Additionally, the total harmonic distortion of the system is found to be well within the boundary of the acceptable range that proclaims its feasibility and applicability in a real-world scenario. [ABSTRACT FROM AUTHOR]

In this paper an islanded microgrid fed through the wind and solar energy resources is presented. The power flow within the microgrid is controlled using a Neutral Point Clamped Dual Active Bridge (NPC-DAB) converter. In the proposed dc microgrid, the solar energy source is associated at the low voltage (LV) bus and the wind energy source is connected at the high voltage (HV) bus. A permanent magnet synchronous generator (PMSG) machine is used in wind energy conversion system. The real time solar radiation and wind speed data of Rupangarh, Rajasthan, India is used as an input for renewable energy resource. The NPC-DAB will work as a power electronics juncture for expediting the energy exchange in the islanded DC Microgrid. The proposed closed loop controller based on the capacitor voltage and load voltage will expedite a complete automatic operation of the islanded DC-microgrid considering various load changes. The system is studied without storage element as the automatic control of energy generation and load feeding is carried out by the NPC-DAB, also this makes the scheme cost effective. The optimum duty ratios for NPC-DAB operation are obtained and thus the increased load demand is met. The modeling of PMSG, NPC-DAB and wind energy system is discussed in details in this work. The proposed system is studied in MATLAB/Simulink environment and results are obtained for different load variations. All the wind control parameters, NPC-DAB waveforms, load waveforms are also plotted using MATLAB. [ABSTRACT FROM AUTHOR]

In the present energy scenario, wind energy is the fastest-growing renewable energy resource on the globe. However, wind-energy-based generation systems are also associated with increasing demands for power quality and active power control in the power network. With the advancements in power-electronics-based technology and its use in non-conventional energy conversion systems, it has witnessed tremendous growth in wind energy conversion systems (WECSs). At the same time, integrating wind farms into the grid system also results in many power quality issues in the power system that involve these renewable energy sources feeding power networks. This paper reports the effectiveness of grid-connected doubly fed induction generator (DFIG)-based WECS with a battery energy storage system (BESS) under variable wind conditions. In this study, a rotor side converter (RSC) is controlled to achieve the optimal torque for a given maximal wind power. The control scheme is simulated using MATLAB for a 2 MW-rated DFIG used in a WECS. Additionally, in this paper, a new fraction order proportional integral derivative (FOPID) controller is introduced into the system's RSC, and its performance is also observed. The BESS technique is used with a DC link to improve the overall performance of the DFIG-based WECS under different wind conditions. To control the BESS, a proportional integral (PI) controller is introduced to increase the charging and discharging rates. Two models are developed in MATLAB/Simulink: one model is a basic model, and other model is equipped with a BESS and a PI controller in the BESS. The results validate the effectiveness of the proposed PI-controller-equipped BESS at improving the overall performance of the WECS system under study. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as photovoltaic (PV) and wind energies are integrated into the grid due to their low global emissions and higher power conversion efficiency techniques. Grid-connected inverters are the core components of distributed generation networks. However, several harmonic current and voltage variations affect the performance of circuits in grid-connected networks. These issues can be easily resolved using passive filters, static vector generators, and dynamic energy filters (APFs). In higher-level units, the cost, dimensions, and weight of passive filters increase proportionally. The purpose of this research is to evaluate advanced APFs for reducing power switches and grid-connected weight, cost, and scale. Several studied APF inverter topologies, including single-phase, three-phase AC–AC, back-to-back, and common parameters, have been considered. Cost-effective solutions such as PV-based transformers based on APF, fewer inverters, multiple and multifunctional inverters, and wind-assisted conversion systems have been studied. [ABSTRACT FROM AUTHOR]

This article presents the grid connected hybrid solar-wind energy conversion system (HSWECS) using the cascaded diode clamped multilevel inverter (CDCMLI). The advantage of CDCMLI over the conventional cascaded H-bridge multilevel inverter (CHBMLI) is that the power transfer capacity of the CDCMLI is doubled. The solar and wind energy conversion systems are connected individually to the DC-links of CDCMLI through the DC–DC converter which is used to get maximum power from solar and wind systems. Due to the fluctuating power supply by renewable sources, the isolated DC-links of separate inverters suffer from varying DC-link voltages. A control scheme is proposed which is capable of balancing these DC-link voltages under various power generation scenarios, extracting the maximum possible power from renewable energy sources (RES), and injecting it into the grid at near unity power factor. The proposed control also monitors the power quality of the injected current into the grid. Furthermore, it allows the system to be connected to the grid without any transformer. In addition, mathematical modeling of the CDCMLI has been presented. The performance of the system is analyzed with Matlab/Simulink and confirmed by a prototype model with dSpace 1104. [ABSTRACT FROM AUTHOR]

Virtual power plants are virtual units that aggregate the power and energy storage capacities of a set of electricity generation and storage power plants, are coordinated by a single entity and provide energy and ancillary services to the electricity system. Energy storage is crucial in balancing network generation and demand and providing support to the network - it is even being designed to conduct power-frequency regulation tasks in the power system, so large-scale energy storage systems based on cutting-edge technologies will have to be deployed [[1]]. The availability and accessibility of renewable energy in locations worldwide make it play a leading role in the decarbonization process of the energy sector. In Zelazna, Agnieszka et al. [[17]], authors compare two small-scale power generation systems, one consisting exclusively of solar PV panels and the other a hybrid system consisting of both solar PV power and a battery-based energy storage system. [Extracted from the article]

The world's focus is shifting toward Renewable Energy Sources (RES) as a result of the sharp rise in energy demand and the depletion of conventional energy sources. Distributed generation (DG) units developed on RES are easily and reliably integrated into grid due to microgrid technology. Inverters are typically used to integrate DGs into microgrid. At present, the fastestgrowing renewable sources of electricity in the world is photovoltaic (PV) and wind energy conversion system (WECS), which evolved in response to growing environmental concerns about the risks of climate change connected with power generation. Recent technological developments have made it a top priority for researchers to find ways to enhance the efficiency of hybrid systems and the power converter stage. In order to achieve transmission losses and ensure energy savings, a new 13-bus system is developed in this work for regulating the output voltage in distribution networks. Switched Z-source Boost converter along with Proportional Integral (PI) controller is employed to boost the voltage obtained from PV system. A battery converter along with a bi-directional battery is connected to the DC link, to store energy generated by Hybrid Renewable Energy System (HRES) in excess amount. The obtained DC link voltage is transferred to three-phase voltage source inverter (VSI) for the conversion of DC to AC voltage. Effective harmonic reduction is attained with the aid of LC filter coupled to Three phase grid, and the PI controller associated to the grid supports in achieving effective grid synchronization. The proposed research is validated using MATLAB Simulink by considering the 13-bus system and an efficiency of 96.8 % is obtained with 0.93% Total Harmonic Distortion (THD). [ABSTRACT FROM AUTHOR]

Renewable energy sources have become a significant alternative energy source due to the continuing depletion of conventional energy sources and fluctuation in fuel costs. Currently, wind energy is the foremost among all other renewable energy sources. However, modeling and analyzing industrial wind turbines is complex as the wind turbine power ratio and size have steadily increased. Undoubtedly, industrial wind turbines are huge and challenging to keep in research labs; simultaneously, exploring the controller/power converter performance is practically impossible. Therefore, to overcome the above drawbacks, wind turbine emulators have been developed to achieve the static and dynamic characteristics of wind energy conversion systems. This paper aims to present a comprehensive review of the different wind turbine emulators available in the literature. In addition, the implementation of real-time emulators is classified according to the structure and approaches. Furthermore, an extensive analysis of the emulators was presented based on the significant parameters utilized for the real-time wind turbine emulators. Finally, this review analyzes the different emulator topologies according to cost, accuracy, complexity, and hardware implementation. [ABSTRACT FROM AUTHOR]

Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%. [ABSTRACT FROM AUTHOR]

In a DC microgrid, the main aim of power management is to maintain the balance of active power between renewable sources, storage batteries, loads, and distribution grid which can be represented by stable DC-link voltage. This paper proposes an adaptive strategy for power flow management of a stand-alone hybrid renewable energy system (HRES) consisting of a wind turbine generator, photovoltaic cell, battery energy storage (BES), and load by optimizing the gain of the PI controller in the battery storage system using the particle swarm optimization-based firefly algorithm (PSO-FA). A part of the PSO algorithm is utilized for improving velocity and attractiveness of the firefly algorithm thereby increasing the rate of convergence in the PI controller. This control technique improves gain constant by generating control pulses for charging and discharging of the BES system in order to make optimum use of renewable energy sources and to stabilize the voltage of the DC-link. From the simulations done in MATLAB/SIMULINK, it is observed that conventional trial- and error-based PI tuning produces high overshoot, whereas the proposed method produces better performance with less overshoot. Maintaining constant DC-link voltage, the instantaneous power generation under various conditions of generation and load consumption is managed by monitoring the state of charge of battery. This approach can be used as a complement for improving the performance of PI controllers designed by conventional methods. [ABSTRACT FROM AUTHOR]

The prominence of Renewable Energy Sources (RES) in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination. A grid-tied DFIG (Doubly Fed Induction Generator) based WECS (Wind Energy Conversion System) is introduced in this work, in which a Landsman converter is implemented to improvise the output voltage of PV without any fluctuations. A novel GA (Genetic Algorithm) assisted ANN (Artificial Neural Network) is employed for tracking the Maximum power from PV. Among the rotor and grid side controllers, the former is implemented by combining the stator flux with d-q reference frame and the latter is realized by the PI controller. The proposed approach delivers better performance in the compensation of real and reactive power along with the DC link voltage control. The controlling mechanism is verified in both MATLAB and experimental bench setup by using an emulated wind turbine for the concurrent control of DC link potential, active and reactive powers. The source current THD is observed as 1.93% and 2.4% for simulation and hardware implementation respectively. [ABSTRACT FROM AUTHOR]

Power electronics, as an enabling technology in most renewable energy systems, is gaining attention as the penetration of renewable energy sources increases. Wide-bandgap power electronics are of particular interest due to their superior voltage blocking capabilities and fast switching speeds. They can viably be considered in the renewable energy sources, especially as the penetration of wind energy is expected to increase by a great extent in the upcoming years. In this paper, a comparison of Silicon Carbide-based and Silicon-based wind energy conversion systems has been performed, as it is crucial in understanding the benefits of adopting wide-bandgap-based solutions at a commercial level. For this analysis, a 2 MW permanent magnet synchronous generator-based wind conversion system with a bidirectional full-scale frequency converter comprised of two back-to-back inverters is considered. The efficiency, cost, and total volume of the passive components comparison have been conducted for Silicon- and Silicon Carbide-based converters. The comparison presented is a fair comparison, meaning that the converters are designed with modules of the same power ratings. Wind energy systems are compared both for the same switching frequency (low switching frequency suitable for IGBT modules) and also considering a Silicon Carbide-MOSFET-based converter working at high switching frequencies. The comparison is performed in PLECS simulation tool, using the PLECS libraries for different modules obtained from the manufacturers' experimental data. The results show the benefits of using the Silicon Carbide-based converter when it comes to volume reduction in the passive components and provide insights to what is missing in order to achieve overall system volume and cost savings. [ABSTRACT FROM AUTHOR]

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %. References 21, table 1, figures 20. [ABSTRACT FROM AUTHOR]

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Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research. [ABSTRACT FROM AUTHOR]

Renewable energy production plays a major role in satisfying electricity demand. Wind power conversion is one of the most popular renewable energy sources compared to other sources. Wind energy conversion has two major types of generators such as the Permanent Magnet Synchronous Generator (PMSG) and the Doubly Fed Induction Generator (DFIG). The maximum power tracking algorithm is a crucial controller, a wind energy conversion system for generating maximum power in different wind speed conditions. In this article, the DFIG wind energy conversion system was developed in Matrix Laboratory (MATLAB) and designed a machine learning (ML) algorithm for the rotor and grid side converter. The ML algorithm has been developed and trained in a MATLAB environment. There are two types of learning algorithms such as supervised and unsupervised learning. In this research supervised learning is used to power the neural networks and analysis is made for various hidden layers and activation functions. Simulation results are assessed to demonstrate the efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

Ocean energy systems (OESs) convert the kinetic, potential, and thermal energy from oceans and seas to electricity. These systems are broadly classified into tidal, wave, thermal, and current marine systems. If fully utilized, the OESs can supply the planet with the required electricity demand as they are capable of generating approximately 2 TW of energy. The wave energy converter (WEC) systems capture the kinetic and potential energy in the waves using suitable mechanical energy capturers such as turbines and paddles. The energy density in the ocean waves is in the range of tens of kilowatts per square meter, which makes them a very attractive energy source due to the high predictability and low variability when compared with other renewable sources. Because the final objective of any renewable energy source (RES), including the WECs, is to produce electricity, the energy capturer of the WEC systems is coupled with an electrical generator, which is controlled then by power electronic converters to generate the electrical power and inject the output current into the utility AC grid. The power electronic converters used in other RESs such as photovoltaics and wind systems have been progressing significantly in the last decade, which improved the energy harvesting process, which can benefit the WECs. In this context, this paper reviews the main power converter architectures used in the present WEC systems to aid in the development of these systems and provide a useful background for researchers in this area. [ABSTRACT FROM AUTHOR]

Today, renewable energy resources are a critical component of distributed energy systems. However, their intermittent nature makes them unstable energy sources, making them very difficult to use optimally in any energy system. Battery storage is a viable solution for this issue. In this paper, we consider distributed power-flow assignment consisting of unstable power generators, unpredictable power loads, and multiple energy storage systems (ESSs), with different combinations of logical power connections between them. We propose power-flow assignment (PFA) algorithms to deal with single and multiple loads to address the possibility of reducing energy loss and improving distributed power-flow assignment with the presence of ESSs in a smart home environment. Simulation results reveal that the increment of logical power connections between generators, loads, and storage systems can significantly reduce energy loss. The proposed PFA algorithms can reduce energy loss by about 67% compared to a power-flow assignment for which all the generated power is stored in an ESS directly during winter. The results further show that spring has the highest energy loss and stored energy in ESS compared to other seasons. [ABSTRACT FROM AUTHOR]