Search Results (416)

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article

This paper aims to find an efficient optimization algorithm to bring down the cost function without compromising the stability of the system and respect the operational constraints of the Hybrid Renewable Energy System. To accomplish this, MATLAB simulations were carried out using three optimization techniques: Grey Wolf Optimization (GWO), Teaching–Learning-Based Optimization (TLBO), and Multi-Objective Particle Swarm Optimization (MOPSO). The study compared their outcomes to identify which method yielded the most effective performance. The research included a statistical analysis to evaluate how consistently and stably each optimization method performed. The analysis revealed optimal values for the output power of photovoltaic systems (PVs), wind turbines (WTs), diesel generator capacity (DGs), and battery storage (BS). A one-year period was used to confirm the optimized configuration through the analysis of capital investment and fuel consumption. Among the three methods, GWO achieved the best fitness value of 0.24593 with an LPSP of 0.12528, indicating high system reliability. MOPSO exhibited the fastest convergence behaviour. TLBO yielded the lowest Net Present Cost (NPC) of 213,440 and a Cost of Energy (COE) of 1.91446/kW, though with a comparatively higher fitness value of 0.26628. The analysis suggests that GWO is suitable for applications requiring high reliability, TLBO is preferable for cost-sensitive solutions, and MOPSO is advantageous for obtaining quick, approximate results. Full article

The concept of the circular economy aims to develop systems for reusing, recovering, and recycling products and services, pursuing both economic growth and sustainability. In many countries, legislation has been enacted to create frameworks ensuring environmental protection and fostering initiatives to implement the circular economy across various sectors. The wind energy industry is no exception, with industries and institutions adopting strategies to address the forthcoming challenge of repowering or dismantling a significant quantity of wind turbines in the coming years reaching a total of global wind power capacity by 2024. This also involves managing the resulting waste, which includes materials with high economic value as well as others that have considerable environmental impacts but that can be reused, recycled, or converted. In parallel, the research activity in this field has increased significantly in response to this challenge, leading to a vast body of work in the literature, especially in the last three years. The aim of this paper is to conduct a bibliometric study to provide a global perspective on the current literature in the field, covering the period from 2009 to 2024. A total of 670 publications were retrieved from Web of Science and Scopus, with 57% of them published in the last three years, highlighting the growing interest in the field. This study analyzes the research product, the most relevant journal, the most cited authors and institutions, their collaborative patterns, emerging trends, and gaps in the literature. This contribution will provide an up-to-date analysis of the field, fostering better understanding of the direction of the research and establishing a solid foundation for future studies Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

Water scarcity and the growing demand for sustainable energy solutions have driven the need for renewable-powered desalination. This study evaluates three scenarios for reverse osmosis (RO) desalination powered by photovoltaic (PV), wind turbine (WT), and hybrid PV–WT systems, aiming to minimize the levelized costs of electricity (LCOE) and water (LCOW) while reducing grid dependence. The city studied is Zahedan, Iran, which has high potential in renewable energy. A multi-objective optimization approach using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a popular evolutionary algorithm, is employed to determine the optimal number of PV panels and wind turbines. The results show that the hybrid system outperforms single-source configurations, supplying 34.79 MWh of electricity and 34.19 m³ of desalinated water, while achieving the lowest LCOE (2.73 cent/kWh⁻¹) and LCOW (35.33 cent/m⁻³). The hybrid scenario covers 65.49% of the electricity demand and 58.54% of the water demand, significantly reducing reliance on the grid compared to the PV and WT scenarios. Additionally, it ensures greater energy stability by leveraging the complementary nature of PV and WT. These findings highlight the techno-economic feasibility of hybrid renewable-powered desalination as a cost-effective and sustainable solution. Future research should focus on integrating energy storage to further enhance efficiency and minimize grid dependency. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

This article presents a modeling and analysis approach for a hybrid photovoltaic wind turbine (PV-WT) hydrogen production system. This study uses the TRNSYS simulation platform to evaluate the system under coastal climate conditions in Perth, Australia. The system encapsulates an advanced alkaline electrolyzer (ELE) and an alkaline fuel cell (AFC). A comprehensive 4E (energy, exergy, economic, and environmental) assessment is conducted. The analysis is based on hourly dynamic simulations over a full year. Key performance metrics include hydrogen production, energy and exergy efficiencies, carbon emission reduction, levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The TRNSYS model is validated against the existing literature data. The results show that the system performance is highly sensitive to ambient conditions. A sensitivity analysis reveals an energy efficiency of 7.3% and an exergy efficiency of 5.2%. The system has an entropy generation of 6.22 kW/K and a sustainability index of 1.055. The hybrid PV-WT system generates 1898.426 MWh of renewable electricity annually. This quantity corresponds to 252.7 metric tons of hydrogen production per year. The validated model shows a stable LCOE of 0.102 USD/kWh, an LCOH of 4.94 USD/kg, an energy payback time (EPBT) of 5.61 years, and cut CO₂ emissions of 55,777.13 tons. This research provides a thorough analysis for developing green hydrogen systems using hybrid renewables. This study also offers a robust prediction model, enabling further enhancements in hybrid renewable hydrogen production. Full article

Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a “green-carbon” offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the “green-carbon” offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO₂ produced by waste incineration is absorbed and reused. Finally, the “green-carbon” offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

To modernize electrical power systems on isolated islands, countries around the world have increased their interest in combining green energy with conventional power plants. Wind energy (WE) is the most adopted renewable energy source due to its technical readiness, competitive cost, and environmentally friendly characteristics. Despite this, a high penetration of WE in conventional power systems could affect their stability. Moreover, these isolated island power systems face frequency deviation issues when operating in hybrid generation mode. Generally, under contingency or transient conditions for hybrid isolated wind–diesel power systems (WDPSs), it is only the diesel generator that provides inertial support in frequency regulation (FR) because wind turbines are unable to provide inertia themselves. Frequency deviations can exceed the pre-defined grid code limits during severe windy conditions because the diesel generator’s inertial support is not always sufficient. To overcome this issue, we propose a control strategy named emulation inertial and proportional (EI&P) control for Variable-Speed Wind Turbines (VSWTs). VSWTs can also contribute to FR by releasing synthetic inertia during uncertainties. In addition, to enhance the effectiveness and smoothness of the blade pitch angle control of WTs, a pitch compensation (PC) control loop is proposed in this paper. The aim of this study was to provide optimal primary frequency regulations to hybrid wind–diesel power systems (WDPSs). Therefore, the hybrid WDPS on San Cristobal Island was considered in this study. To achieve such goals, we used the above-mentioned proposed controls (EI&P and PC) and optimally tuned them using the Student-Psychology-Based Algorithm (SPBA). The effectiveness of this algorithm is in its ability to provide the best optimum controller gain combinations of the proposed control loops. As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. Moreover, the proposed PC control, which was further simplified using exhaustive searches, resulted in a reduction in blade pitch angle control complexity. To validate the results, the proposed approach was tested under different sets of perturbations (sudden loss of wind generator and gradual increase in wind speed and their random behavior). Furthermore, hybrid systems were tested simultaneously under different real-world scenarios, like various sets of load or power imbalances, wind variations, and their combinations. The Simulink results showed a significant improvement in FR support by minimizing frequency deviations during transients. Full article

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid energy solutions to ensure reliability, sustainability, and economic viability. However, optimizing the design of hybrid renewable energy systems, particularly those incorporating both hydrogen and battery storage, remains challenging due to system complexity and fluctuating energy trading conditions. This study addresses these gaps by proposing a novel framework that combines the Chimp Optimization Algorithm (ChOA) with a rule-based energy management strategy (REMS) to optimize component sizing and operational efficiency in a grid-connected microgrid. The proposed system integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), hydrogen storage, fuel cells (FC), and battery storage (BAT), while accounting for seasonal variations and dynamic energy trading. Each contribution in the Research Contributions section directly addresses critical limitations in previous studies, including the lack of advanced metaheuristic optimization, underutilization of hydrogen-battery synergy, and the absence of practical control strategies for energy management. Simulation results show that the proposed ChOA-based model achieves the most cost-effective and efficient configuration, with a PV capacity of 1360 kW, WT capacity of 462 kW, 164 kWh of BAT storage, 138 $H_2$ tanks, a 571 kW ELZ, and a 381 kW FC. This configuration yields the lowest cost of energy (COE) at $0.272/kWh and an annualized system cost (ASC) of $544,422. Comparatively, the Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) produce slightly higher COE values of $0.274, $0.275, and $0.276 per kWh, respectively. These findings highlight the superior performance of ChOA in optimizing hybrid energy systems and offer a scalable, adaptable framework to support future renewable energy deployment and smart grid development. Full article

When power system equipment fails due to disasters, resulting in the isolation of parts of the network, the loads within the isolated system cannot be guaranteed a continuous power supply. However, for critical loads—such as hospitals or data centers—continuous power supply is of utmost importance. While distributed energy resources (DERs) within the network can supply power to some loads, outages may lead to compensation and fairness issues regarding the unsupplied loads. In response, this study proposes a methodology to determine the appropriate power contract price for key customers by estimating the unsupplied power demand for critical loads in isolated networks and incorporating both outage compensation costs and voltage stability penalties. The microgrid under consideration comprises DERs—including electric vehicles (EVs), fuel cell electric vehicles (FCEVs), photovoltaic (PV) plants, and wind turbine (WT) plants—as well as controllable resources such as battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). It serves both residential load clusters and critical loads associated with social infrastructure. The proposed methodology is structured in two stages. In normal operating conditions, optimal scheduling is simulated using second-order conic programming (SOCP). In the event of a fault, mixed-integer SOCP (MISOCP) is employed to determine the optimal load shedding strategy. A case study is conducted using the IEEE 123 bus test node system to simulate the outage compensation cost calculation and voltage penalty assessment processes. Based on this analysis, a contract price for key customers that considers both disaster-induced outages and voltage impacts is presented. Full article

Many decommissioned wind turbines (WTs) present significant recycling management challenges. Improper disposal wastes resources and generates additional carbon emissions, which contradicts the Sustainable Development Goals (SDGs). This study constructs a sine cosine algorithm (SCA)–ITransformer–BiLSTM deep learning prediction model, integrated with dynamic material flow analysis (DMFA) and a multi-dimensional Energy–Economy–Environment–Society (3E1S) sustainability assessment framework. This hybrid approach systematically reveals the spatiotemporal evolution patterns and circular economy value of WTs in China by synthesizing multi-source heterogeneous data encompassing policy dynamics, technological advancements, and regional resource endowments. Results demonstrate that China will enter a sustained wave of WT retirements post-2030, with an annual decommissioned capacity exceeding 15 GW. By 2050, new installations and retirements will reach a dynamic equilibrium. North and Northwest China are emerging as core retirement zones, accounting for approximately 50% of the national total. Inner Mongolia and Xinjiang face maximum recycling pressures. The recycling of decommissioned WTs could yield approximately CNY 198.5 billion in direct economic benefits and reduce CO₂ equivalent emissions by 4.78 to 8.14 billion tons. The 3E1S framework fills critical gaps in quantifying the comprehensive benefits of equipment retirement, offering a theoretically grounded and practically actionable paradigm for the global wind industry’s circular transition. Full article

To facilitate both the installation and the removal of floating offshore wind turbines (FOWTs), a novel tension-leg dual-module offshore wind turbine system has been proposed. This system primarily consists of a DTU 10 MW wind turbine (WT) module and a supporting tension-leg platform (TLP) module. Considering both mechanical and hydrodynamic coupling effects of the dual-module system, this study focuses on its dynamic responses during both the installation and the removal of the WT module under typical sea states. The effect of different installation vessel positions and key parameters of the clamping device on the dynamic response of the system during the WT module removal has been clarified. Based on the findings, preliminary recommendations are provided regarding the optimal positioning of the installation vessel and the optimal design parameters of the clamping device. Furthermore, an auxiliary sleeve has been proposed to facilitate the WT module removal. The results indicate that the application of the auxiliary sleeve can significantly improve the dynamic response of the system. The results of this study can serve as a reference for the design, installation, and removal of floating offshore wind turbines. Full article