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This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV's five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources such as solar and wind causes fluctuations in power generation. Hence energy storage is necessary in standalone power systems. It is challenging to use batteries as power backups because they cannot handle rapid power fluctuations without compromising battery life. This paper proposes a hybrid energy storage system (HESS) for wind energy-based power systems that includes a battery for long-term energy management with a super capacitor for quick dynamic power regulation. Pulse charging of the proposed HESS is carried out with the help of a dual active bridge (DAB) converter. Pulse charging of the HESS will result into reduced stress on battery and eventually to a longer self-life. The operation of the wind energy fed hybrid battery-supercapacitor energy storage was investigated through simulation using MATLAB-Simulink. For validating the simulation results, an experimental test bench is created using a real DAB prototype and TI Piccolo-F280049 microcontroller. [ABSTRACT FROM AUTHOR]

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

Abstract 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.

This research aims at proposing an alternative to improve the efficiency of electric vehicles (EVs) and reduce greenhouse gas (GHG) emissions in the context of electric mobility. A photovoltaic and wind hybrid energy system was installed in a Chok S2 electric vehicle. In addition, a charge equalization system was included to balance and maximize the performance of each of the EV’s five batteries connected in series. The results show a 20% improvement in vehicle efficiency after conducting tests on a 17 km Andean route. The photovoltaic system generated 535 W, while the wind system generated 135 W/s at a speed of 45 km/h. These findings highlight the potential of hybrid renewable energy systems to improve the efficiency and range of electric vehicles.

The importance of renewable energy resources in the energy province necessitates the use of photovoltaic (PV) and wind systems to power brushless DC (BLDC) motors for applications such as water pumping and automobiles. In this research, an optimized MPPT-based positive output super-lift Luo converter (PO-SLLC) is presented for a hybrid power system with a BLDC motor to overcome the constraints of ordinary DC–DC converters. The Luo converter produces a positive output voltage which aids in achieving a high power density with fewer ripples in the voltage and current profile. The output voltage grows in geometric progression, increasing the converter's efficiency, according to its mathematical model. Adjustment parameter enhanced Aquila optimization (APEAO) is used to optimize the duty cycle of the PO-SLLC from the MPPT controller and the controller parameters of the PI controller of the wind system. The proposed system's behavior for variations in irradiation patterns with constant load torque is simulated in MATLAB/Simulink. The settling time and overshoot obtained with the proposed optimized controller are compared to those obtained with other optimization controllers, such as ant colony optimization (ACO), genetic algorithm (GA), Aquila optimization (AO), advanced version of harmony search algorithm (2N + 1-HSA) and opposition based quantum bat algorithm (OQBA) and it is demonstrated that the proposed APEAO optimized controller has good dynamic performance. [ABSTRACT FROM AUTHOR]

DC generators have the major drawback of the large number of sliding brush-collar contacts, making this motor structure expensive and requiring a non-negligible maintenance cost. To avoid this problem, an excitation system allowing the excitation current to be reversed after the electromotive force changes polarity is added. The structure of the generator is found by equivalence to a permanent magnet direct current generator structure by changing the magnets by equivalent concentrated coils. A control technique of this type of generator, used in a wind energy production system, is developed to show the efficiency of this type of generator in the field of wind energy production. Related to permanent magnets generators, the studied structure is with reduced production cost since the magnets are replaced by coils. The innovation aspects of the studied generator are the modularity (use of several modules to increase the power delivered by the generator), the elevated efficiency due to the limited number of power chain components, the elevated power to weight ratio, the simplicity of the control and the reduced manufacturing cost. In conclusion, the use of the studied generator in the wind energy system, makes the power chain simple, inexpensive and robust. [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]

The hybrid solar/wind power systems are the best renewable energy sources because of their complementary nature. The generation of electricity from wind and solar photovoltaic (PV) systems is greatly reliant on the weather. Their intermittent nature causes output fluctuations. As a result, the need for rapid compensation for energy transmission and distribution systems is becoming increasingly critical. In this article, an optimal parameterized Fractional Order Proportional Integral Derivative (FOPID) controller for a hybrid renewable energy system integrated with a single-phase grid. An amalgamation of solar PV and wind is presented as a way to provide continuous power from alternate sources. Furthermore, it is planned to design a robust output voltage management system that utilizes a FOPID controller for the output voltage regulation of the hybrid system integrated with the grid. A novel Randomly Updated Path Finder Algorithm (RU-PF) is proposed to optimize the FOPID gains. Hence, the proposed FOPID-based control for the single-phase grid connected to PV and wind system is simulated in MATLAB/Simulink, and their performance is examined. In particular, the fitness of the proposed RU-PF algorithm is 5.06%, 1.32%, 7.41%, and 0.88% superior to the extant PSO, FF, WOA, and PFA algorithms at the 25th iteration. [ABSTRACT FROM AUTHOR]

This paper presents an average model based on justified simplifications dedicated to the design and optimization of wind energy systems. Indeed, the classic models of wind energy systems are complex and their use is not efficient for the optimal design of the components of the power chain given the complexity, the significant time of resolution and the strong correlation of the physical parameters of these models. For these reasons, a model based on the theory of average values with reduced simulation time of a wind turbine structure is developed. This model is validated against the classic model of the wind chain using the SimPowerSystem library of power component models integrated under the Matlab-Simulink simulation environment. [ABSTRACT FROM AUTHOR]

This paper focuses on reducing the LCOE (Levelized Cost of Energy), the unmet load, Net Present Cost (NPC) and the greenhouse gas emissions by the utilization of an optimized off-grid integrated renewable energy system. The LCOE has been reduced by 14.8% of that of DG and by 28.6 % of Diesel Generator (DG) with Battery System, whereas the NPC has been reduced by 14.7% of DG only system and by 28.7% of DG with Battery System. A comparison analysis has also been presented based on the performance of the proposed system and most recently published similar studies. To achieve these objectives, an optimization algorithm has been developed and simulated in Hybrid Optimization Model for Multiple Energy Resources (HOMER) software with different combinations of IRES, DG and battery systems. Similarly, intending to analyze the feasibility and performance in terms of the technical and economic aspects of the proposed system, HOMER is used. Based on the obtained results, broadly, it is examined that the proposed system decreases diesel consumption by around 8113 liters/year and the total emissions of greenhouse gas by about 21545 kg/year in the study area; thereby, it can be foreseen as a viable and sustainable option(s) for the development of more such systems in remote locations.

The wind turbine system is one of the most challenging control problems in power systems due to its fast and slow dynamics and the optimal and robust control of the doubly fed induction generator is particularly important in this system. To handle the desired performance under different conditions, this paper presents a new control method by combining the model predictive control method with the adaptive method. In the aforementioned method by decoupling the active and reactive power control, an adaptive method is used to estimate the uncertainty, disturbance, and nonlinear terms as well as the internal feedback design. Then, linear matrix inequality-based robust model predictive control is designed as external feedback. The Lyapunov method is used to demonstrate system stability under the proposed controller. The simulation results show the efficiency of the method under different scenarios. [ABSTRACT FROM AUTHOR]

World's large population lives in remote areas where electricity is unavailable or unreliable supply. For electricity supply, a low‐cost, minimum‐emission hybrid renewable energy system is necessary due to power shortages or unavailability, high electricity costs, and large greenhouse gas emissions. The current work develops a standalone hybrid power generating system and assesses its techno‐economic feasibility for rural sites in the Lucknow area of Northern India. To address the electric load requirement, the proposed work includes a system that includes a solar photovoltaic (SPV) module, a lead‐acid battery, a wind turbine (WT), and a diesel generator (DG). The system is sized and optimized using the hybrid optimization of multiple energy resources software. In this off‐grid system, the proposed system is intended for a home load need of 52.00 kWh/day with a peak of 11.04 kW. The technical and financial specifications are drawn from the local Indian market for the proposed standalone system. In this article, seven different resource combinations are investigated. A combination of 13 kW DG, 16 kW SPV, 50 kWh lead‐acid battery, 9 kW inverter, and 4 kW WT produces the best optimal system. With a 98.4% renewable portion, the cost of energy and total net present cost are $0.162/kWh and $57 390, respectively. The sensitivity analysis inflation and discount rates are 5.5% and 8%, respectively. As a consequence of the findings, we can conclude that the suggested standalone system is more cost‐effective for rural areas in Northern India. [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]

The purpose of this study is to enhance the accuracy of direct power/torque control (DPC/DTC) applied to back-to-back converters supplying a doubly fed induction generator (DFIG) based wind power system. Two solutions are proposed. The first one is to increase the degree of freedom of the DTC and DPC control by implementing three-level back-to-back converters. Fuzzy logic control is the second solution to enhance the performances of both conventional direct power/torque control, leading in a decrease of the DFIG's torque/flux ripples and the active/reactive powers ripples supplied by the grid side converter, consequently, reduce the grid currents' total harmonic distortion (THD). The MATLAB/Simulink environment is used to evaluate the wind power generation system performances. The collected findings show that the fuzzy direct control (FDC) technique outperforms conventional direct control (CDC) when used for two-level back-to-back converters. [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]

This paper proposed the integration of the wind energy system (WES) with the grid consists of a wind turbine, a permanent magnet synchronous generator (PMSG), a switch-mode rectifier (SMR) and a three-phase voltage source inverter (VSI). Optimal torque control (OTC) method is applied to the converter on the generator side for maximizing the power coefficient under the change in wind speeds. To synchronize the WES with the grid, another controller is applied to keep dc-bus voltage at constant value and to regulate active and reactive power transmitted into a grid. A system of WES connected into a grid is created and tested in the MATLAB/SIMULINK platform version 2019b. Comprehensive simulation results are used to analyze and verify the excellent performance of the suggested two- control strategy on the machine and grid side.

This paper proposes the novel control strategy to improve the power quality injection of wind energy system using Doubly Fed Induction Generator (DFIG) into the grid by implementing artificial neural network. The torque ripple produced in DFIG due to loading by grid tied inverter, which leads to poor power quality injection into the system. Also, these ripples transferred by DC link and causes heating losses and generator phase current distortion. Therefore, this paper modelled ANN based control scheme to reduce the torque ripple content and restrict the transfer of ripple by DC link to improve the outcome of wind energy system while operating in variable conditions. The DFIG system under studied are modelled and simulated in MATLAB SIMULINK to verify the improvement using proposed control strategy. The recent control technique is also simulated for reflecting the effectiveness in the proposed control method. The outcomes obtained are studied and analysed with the existing control scheme to highlight the improvement obtained by proposed control. [ABSTRACT FROM AUTHOR]

In a bid to reduce the negative effect of atmospheric and environmental pollution and its consequent negative effect, a mix of renewable energy generation options is being considered across the globe. Hence, the technical comparison of an ecofriendly, cost-effective, and near zero carbon footprint for sustainable power solutions is still of core interest in the research space. Solar, wind and its hybrids can be further explored to meet the ever-increasing load demand of energy consumers, especially in high energy demand facilities including information technology (IT) services. Critical IT infrastructures represent a core backbone in research institutions, hence the need for hybrid energy solutions is pertinent for increased sustainability. This work compared hybrid energy sources of solar and wind against its independent sources using the IT laboratory in Alex Ekwueme Federal University, Ebonyi, Nigeria. An appropriate sizing of individual and a hybrid of wind, solar, and battery energy source was done, after which the input-output voltages across the set-ups was measured during peak and off-peak periods for a 300W laboratory IT load. The experimental result showed the input/output peak and off-peak DC voltages for solar, wind, and its hybrid energy are 12.67V/12.02V / 12.47V/11.61V, 12.53V / 11.90V / 12.48V / 11.87V, and 12.78V / 12.09V / 12.57 / 11.95V respectively signifying a significant increase from the on-load peak period while the on-load off-peak period is seen to be lower. The results also show that a hybrid solar-wind-battery system tested at 5 minutes time intervals gives a superior output voltage regulation and efficiency when compared to the solar or wind individual systems. It was concluded that the hybrid system of solar, wind, and battery energy source is an ideal standalone renewable energy power option for IT laboratory in the university. [ABSTRACT FROM AUTHOR]

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy fault tree analysis technique for reliability evaluation of the wind energy system is presented. The technique combines the operational failures effect and the errors in the fuzzy environment for the wind energy system configuration. In conventional fault tree analysis, the acceptance of the risks probability values is not considered. Besides, it is very difficult to have a precise assessment of the wind system failure rates or the undesired events occurrence probability due to absence of adequate data. Therefore, to overcome these disadvantages, a fault tree analysis based on the fuzzy set theory is presented and applied to the wind energy system. Moreover, if the fault probabilities of the wind energy system fault are not exact values then the fault probabilities are regarded as a fuzzy number and the fuzzy failure rate is known by using fuzzy rules. Also, the fuzzy fault tree analysis is one of the powerful reliability judgment method, which provides the failure modes and its consequences, which are proved on a wind energy system. Furthermore, the risk analysis method is applied based on the fuzzy risk index (FRI) to know the exact impact of every basic event on the top event. Therefore, outcomes show that the fuzzy based fault tree technique combines the probabilities imprecision and engineering inaccuracy are more flexible and adaptive, and it has great use in reliability engineering. [ABSTRACT FROM AUTHOR]

With abundant and non-polluting benefits in nature, sources of renewable energy have reached vast concentrations. This paper first discusses the number of MPPT (Maximum Power Point Tracking) techniques utilized by wind and photovoltaic (PV) to create hybrid systems for generating wind-PV energy. This hybrid system complements each other day and night to enable continuous power output. Then, a new MPPT technique was proposed to extract maximum power using a newly developed hybrid optimization algorithm, namely the Cukoo Fire Fly method (CFF). The CFF algorithm is derived from the integration of the cuckoo search (CS) algorithm and the Firefly (FF) optimization algorithm. In addition, the proposed system comprises a LUO converter controlled by an integrated proportional controller (PI) which includes control of the power delivered to the load. The LUO converter can increase or decrease power depending on load requirements. The results are compared with Harmony Search Algorithm (HAS), Cuckoo Search (CS) and Firefly (FF). Investigation of the proposed system was carried out on the MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

This paper presents the assessment of the operation and control of hybrid AC–DC microgrid (HMG) integrated with alternative energy sources. The system is analyzed by incorporating fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) based controllers in the control scheme of battery. Photovoltaic array and fuel cell system are coupled to the DC bus and an electrolyzer based hydrogen storage system is used for energy storage in the DC subgrid. The output of the wind energy system along with the battery is fed to the AC bus through an inverter. The AC and DC subgrids are integrated through an interlinking converter which is controlled to facilitate power exchange between the subgrids based on the frequency of AC bus and DC bus voltage. Appropriate power management is achieved within the subgrids and between the subgrids by the coordinated control schemes employed in the HMG. The main focus of this article is the analysis of the impact of incorporating conventional proportional–integral controller, fuzzy logic controller and ANFIS based controller in the control scheme of the battery when the subgrids are subjected to power fluctuations. By employing the ANFIS based controller, the transients in the voltage and power output of the battery system are minimized and deviations occurring in the AC bus voltage during load variations are reduced. [ABSTRACT FROM AUTHOR]

This work investigates the application of sliding mode control (SMC) on a doubly fed induction generator (DFIG). In conventional control schemes like PI controllers, the responses are relatively slow, and the transient state is often subjected to sustained oscillation. Further, the PI control achieves lesser invariance behavior against system uncertainties, and the selection of its gain parameters is a skillful task. In contrast, the SMC is well-known for its faster convergence, robustness, and better transient and steady-state behavior. In this study, the nonsingular fast terminal sliding mode control (NSFTSMC) is applied in the speed loop of the rotor side vector control of DFIG. The proposed NSFTSMC scheme results in less speed fluctuation with a change in wind speed, which is maintained by controlling the torque component of the current (iq*). This paper also presents detailed modeling of the DFIG, power converters, and the related control schemes. Moreover, stability analysis of the proposed methodology ensures the practical finite time stability of the overall system. The comparative controller performance and validation are carried out in Matlab/Simulink environment. The proposed control strategy presents much better results than conventional PI-based control.

• Development of generalized probabilistic model for wind speed intermittency by utilizing real-time acquired cloud-based IoT wind speed data for wind energy system in a SA paradigm. • Simplified reliability analysis through multi-state modeling and probability distribution function for wind-integrated systems, departing from conventional convolution-based approaches. • Forecasting of wind speed and wind power generation for two days in advance through a time-series non-linear autoregressive model with exogenous inputs (NARX), and estimation of the operational reliability using the forecasted wind speed and wind power data. The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations. [ABSTRACT FROM AUTHOR]

Optimizing energy structure is important for coping with the increasing environmental problems in iron and steel industry. Wind energy is one of typical zero-carbon, inexhaustible, and accessible energy source for alleviating energy dilemma. In this paper, the electric arc furnace steelmaking processes integrated with wind energy system (EAF-WES) is developed. The input-output data of 1214 and 1536 heats with 30 % and 50 % hot metal ratio are traced and collected. Life cycle assessment and techno-economic analysis of EAF route integrated with traditional energy system (EAF-TES) and EAF-WES are further researched and compared from perspectives of life cycle carbon footprint (LCCF), production cost, wind energy price (WEP), carbon efficiency, carbon reduction benefit (CRB), and carbon tax. The results indicate that EAF-WES enjoys lower LCCF but higher production cost and carbon efficiency than EAF-TES. Considering the price fluctuations, the optimal EAF steelmaking pattern with lowest production cost under different extent of WEP and carbon tax is suggested. When the electricity price is 0.043 and 0.046 $/kWh, the CRB is balanced. It is concluded that EAF-WES is a low-carbon, high-productive, environmentally friendly, and economical route. This research is expected to supply a technical idea for energy conservation and emission reduction in future steel production. [Display omitted] • EAF steelmaking route integrated with wind energy system (EAF-WES) is developed. • Comparative life cycle assessment and techno-economic analysis of EAF-WES is conducted. • Life cycle inventories of EAF route with hot metal ratio of 30 % and 50 % are converged. • EAF-WES with lower hot metal ratio is beneficial for energy conservation and emission reduction in green steelmaking route. [ABSTRACT FROM AUTHOR]

• Advanced situational awareness for operational reliability for wind energy systems (WES). • Comprehensive uncertainty analysis of wind speed and the development of a multi-state model for accurately capturing the variability of wind generator power output. • Time-series forecasting of wind speed and power generation for two days ahead employing a non-linear autoregressive with exogenous input ANN (NARX-ANN) model to project operational reliability. • Implementation of an IoT-based real-time monitoring framework to enhance the projection and management of WES operational reliability. Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively. [ABSTRACT FROM AUTHOR]

This work presents power generation control of a two‐area hybrid restructured power system, initially integrated with renewable energy source (RES) and electric vehicle (EV). For further analysis, additional inclusion of FACTS and energy storage devices in the two‐area hybrid restructured power system is to be analyzed for improved system dynamics. The hybrid restructured power system is a complex nonlinear system that brings light to the major problem of system dynamic control due to insufficient damping under varying loading circumstances. To solve this problem, a robust control approach with rapid acting controllable and storage devices are an immediate need for advanced power system. Motivated from the fact that cascaded fractional order controllers exhibit better performance in comparison to classical controllers, this article proposes an advanced cascaded fractional‐order ID‐fractional‐order PD (cascaded FOID‐FOPD) controller for system performance enhancement of hybrid restructured power system. The maiden application of satin bower bird optimizer technique to optimize the secondary controller gains is employed. The proposed controller's superiority is demonstrated by comparing the results to the other existing controllers. The impact of RES's generation and EVs on system dynamics for cascaded FOID‐FOPD controller is also explored. Furthermore, the sensitivity analysis is done to reflect that optimized gains of cascaded FOID‐FOPD controller are supportive enough for variations in RESs, load perturbations, capacity ratio, and disco participation matrix. [ABSTRACT FROM AUTHOR]

Power industry is incidenting a change from the present electric grid to a more secure, reliable, capable and advanced smart grid. Renewable energy sources such as wind and solar energy systems will be incorporated and potentially in bid scale in order to obtain these aims. Many possible applications of renewable energy sources have been inspected. In this article, reliability enhancement of power system considering the impact of wind and solar energy sources integration into grid with advanced intelligent strategies is focused. Further, different models are utilized to obtain the proposed renewable energy sources operation. Further, a reliability assessment framework is discussed to examine the reliability impact of renewable energy sources integration into the grid. A comprehensive case study and risk analysis has been performed based on the different risk indexes to show the usefulness of the proposed strategies and assessment framework, and to give precious insight on the reliability impact resulting from the wind and solar energy resources integration into the grid. The main advantages of renewable energy sources integration into grid with intelligent techniques are presented here along with the reliability evaluation. The results of the study reveals that the integration of the solar and wind energy systems into the grid enhances overall the reliability of the entire power system.

The indirect vector control (IVC) technique for stand-alone self-excited induction generator (SEIG)-based wind energy system (WES) is presented in this work. IVC regulates the SEIG speed, torque, and DC voltage independently. Further, the proposed technique is analyzed with back-to-back two-level converter such as generator side converter (GSC) and load side converter (LSC) using space vector pulse width modulation (SVPWM) strategy. Two-level and three-level space vector pulse width modulation inverters are used, and the performance comparison between the two schemes is also discussed during step change of wind speed. The three-level inverter exhibits enhancement of SEIG voltage along with current, speed, DC voltage and torque in contrast to those obtained from two-level inverter. IVC for SEIG-based WES is implemented in MATLAB/SIMULINK software, and the experimental environment is set up for 4 kW system. [ABSTRACT FROM AUTHOR]

This paper proposed the integration of the wind energy system (WES) with the grid consists of a wind turbine, a permanent magnet synchronous generator (PMSG), a switch-mode rectifier (SMR) and a three-phase voltage source inverter (VSI). Optimal torque control (OTC) method is applied to the converter on the generator side for maximizing the power coefficient under the change in wind speeds. To synchronize the WES with the grid, another controller is applied to keep dc-bus voltage at constant value and to regulate active and reactive power transmitted into a grid. A system of WES connected into a grid is created and tested in the MATLAB/SIMULINK platform version 2019b. Comprehensive simulation results are used to analyze and verify the excellent performance of the suggested two- control strategy on the machine and grid side. [ABSTRACT FROM AUTHOR]

The wind energy system (WES) has an undesirable characteristic in which its power output varies with wind speed, resulting in fluctuations in the grid frequency and voltage. Especially, in partial load region where the wind speed is below rated, there is a concern in regards to WES output power. This part of the work initially employed exponential moving average (EMA) concept to generate reference power. Later on, an interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller is implemented and designed for good reference tracking and therefore, it can smoothen out the WES output power more effectively. Real time simulations are also developed to show the applicability of the proposed controller using the OPAL-RT digital simulator. Below rated wind speed pattern has considered for real time simulations and results are obtained with different control techniques. The results show that the proposed interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller offers better performance in tracking reference power and hence, it offers good smoothing of output power fluctuations than conventional proportional-integral (PI) and traditional fuzzy logic (Type-1) controllers. The performance of the proposed controller is also estimated using power smoothing and energy loss functions in terms of performance indices. [ABSTRACT FROM AUTHOR]

Purpose. Electricity is a basic energy for life and its consumption increased so we need the discovery of new sources of energy such as wind energy .for this ameliorate the quality of generated wind energy by using the intelligent artificial control, this control is made to optimize the performance of three-phase PWM rectifier working. Methodology. These strategies are based on the direct control of the instantaneous power, namely: the control direct power control (DPC) with classic PI regulator and direct power control with fuzzy logic regulator. The fuzzy characterized by its ability to deal with the imprecise, the uncertain has been exploited to construct a fuzzy voltage regulator. The simulation of these methods was implemented using Matlab/Simulink. Results. A comparison with the results obtained by the classic PI showed the improvement in dynamic performance. This makes the fuzzy controller an acceptable choice for systems requiring quick, precise adjustments and less sensitive to outside disturbances. Originality. The proposed this control strategy using for to obtain a performance adjustment of the DC bus voltage and sinusoidal currents on the network side. Practical value. Fuzzy logic is proven to be effective in terms of reducing the harmonic distortion rate of the currents absorbed, correct adjustment of the active and reactive power and DC voltage and unit power factor operation.