Search Results (10,354)

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development. Full article

Energy plays an essential role in economic advancement for any nation. However, escalating worldwide energy demands coupled with environmental and climate change issues resulting from the excessive consumption of conventional energy sources highlight the importance of identifying sustainable energy resource alternatives. Jordan, with its very limited fossil-fuel resources, is actively expanding its energy mix by investing in renewable sources, particularly wind energy. Therefore, the current work provides an evaluation of the wind power potential of Gharandal town within Tafila governorate, in southern Jordan, using hourly wind data recorded at 90 m elevation within a one-year monitoring period. The investigation reveals that the Weibull distribution more accurately models the wind speed in Tafila compared to the Rayleigh distribution based on parameters estimated through the maximum likelihood approach. The investigation at 90 m also shows that the annual wind power is 296 W/m², indicating that Tafila has marginal suitability for wind potential (Class 2) under the Pacific Northwest Laboratory classification system and has fairly good and suitable conditions for installing a wind farm per the European Wind Energy Association classification system. Most of the time, the prevailing winds at Tafila originate from the west direction (i.e., 270°), accounting for 23% of all occurrences. Finaly, the Tafila region contains promising areas for wind energy generation, particularly with the implementation of modern wind turbine technologies. Full article

As a clean and renewable energy source, wind energy offers lower development and utilization costs than solar energy, making it the most promising renewable option. However, the carbon footprint of offshore wind power and its external impacts on cross-regional carbon emissions have not been investigated sufficiently. Using the provinces of Guangdong and Jiangsu as case studies, this study employs socioeconomic and environmental statistical data. It applies the environmentally extended multi-regional input–output (EE-MRIO) method to quantify cross-regional environmental spillover effects associated with offshore wind power development. The findings show that China’s power structure has been continuously optimized, with offshore winds achieving leapfrog growth since 2010. Through a “local consumption” model, offshore wind power in Guangdong and Jiangsu has effectively replaced coal-fired generation, substantially reducing carbon emissions locally and in neighboring areas. Jiangsu has reduced CO₂ emissions by 16.72 million tons annually, and Guangdong by about 7.23 million tons annually. Furthermore, offshore wind development drives the green transformation of upstream industries (e.g., steel, non-ferrous metals, and chemicals). It extends carbon-reduction benefits to resource-rich regions such as the Northwest and North China. As major manufacturing hubs, both provinces lowered the embodied carbon intensity of their export products by using clean electricity, thereby indirectly reducing the national carbon footprint through cross-regional trade. This study offers scientific insights to help policymakers optimize offshore wind layouts, facilitate coordinated regional emission reductions, and advance sustainable energy transitions. Full article

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Carbon fiber-reinforced polymer (CFRP) composites are in urgent demand in the aerospace, new energy vehicle, and wind power sectors owing to their superior specific strength, specific modulus, and lightweight potential. However, molding defects, such as voids, dry spots, and delamination, arising from their anisotropy and weak interlaminar bonding, severely constrain their service performance. Advanced molding technologies represent the key to overcoming this bottleneck. This paper systematically reviews typical advanced molding technologies in the field of CFRP composites, including resin transfer molding (RTM) and vacuum-assisted resin transfer molding (VARTM) in liquid composite molding, autoclave molding and compression molding (CM) in prepreg molding, and automated fiber placement (AFP) and material extrusion (ME) in automated molding. From an integrated perspective of “technological evolution–process characteristics–defect mechanisms–optimization strategies,” this review summarizes the technical principles, development trajectories, and core advantages of each process, analyzes the formation mechanisms of typical defects, including voids, dry spots, delamination, wrinkles, warpage, and melt instability, and summarizes multidimensional optimization advances in process parameter regulation, numerical simulation, resin modification, equipment upgrading, path planning, and thermal management. Furthermore, the differences and complementarities among these processes in terms of molding precision, efficiency, cost, and applicable scope are compared. Finally, future development directions, including digital twins, green low-carbon manufacturing, ultra-large integrated structures, multi-process integration, standardized defect characterization, and low-cost collaborative design, are discussed. This paper aims to provide systematic theoretical references and technical support for the optimization and upgrading, process integration, and industrial application of advanced CFRP molding technologies. Full article

With the large-scale integration of renewable energy sources, the stability and control of flexible DC (VSC-HVDC) grid-connected systems have become critical issues. This paper proposes an optimal configuration strategy for the control parameters of grid-forming VSC-HVDC systems considering multiple operational constraints. First, a state-space model of the grid-forming VSC-HVDC system connected to a wind farm is established, and the effects of key control parameters on the small-signal stability are analyzed using eigenvalue and participation factor methods. Then, based on the stability analysis, an optimization model is constructed with the objectives of minimizing the steady-state DC operating voltage under operational constraints and maximizing system damping. To solve the optimization problem, the NSGA-II genetic algorithm is employed. Case studies in MATLAB/Simulink demonstrate that the proposed method effectively enhances the small-signal stability of the system across various operating points, reduces overshoot and settling time during power step changes, and ensures stable operation under the maximum transferable power limit. The results verify the robustness and effectiveness of the proposed parameter configuration strategy, providing a practical approach for the design and tuning of grid-forming VSC-HVDC systems in renewable energy integration applications. Full article

The global renewable energy sector now represents the world’s fastest-growing sector, with growth projected to more than double by 2030 and expected to exceed 4600 GW between 2025 and 2030. This is driven by falling costs, increasing consumer awareness, sustainable energy production models, and national and international climate commitments. This review study aims to discuss the transformation initiatives in the renewable energy sector with net-zero targets. A total of 89 studies published between 2020 and 2026 were identified for this literature review. The results indicate that digital transformation has the potential to significantly optimize the performance of the renewable energy sector by resolving its sustainability issues. This study discusses the waste types and waste management strategies in the renewable energy sector. It also highlights the indicators, barriers, and drivers of sustainable performance in the renewable energy sector by integrating advanced technological solutions in manufacturing, supply chain management, maintenance, monitoring, and the management of renewable energy equipment. The study findings demand global commitment and policy coordination in achieving the goals of decarbonization. The literature insights highlight future core research fields and can guide international organizations, industrial policymakers, and academic scholars towards a better and more sustainable future. Full article

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately $1.1\%$ and $4.4\%$ over the CFCEP baseline, with zero post-repair constraint violations in every run. Full article

Water hyacinth (Eichhornia crassipes) is among the world’s most invasive aquatic macrophytes, yet quantitative models of shoreline preference remain absent for Lake Victoria. This study developed a distance-based quantitative framework for spatial distribution and decay modelling to quantify seasonal nearshore accumulation dynamics in Winam Gulf, Kenya, using Sentinel-2 imagery. A Support Vector Machine classifier with polygon-mean feature extraction achieved 94–96% accuracy, supported by strong spectral separability (Jeffries–Matusita distance > 1.9 in six bands). During peak dry season, water hyacinth covered 405.81 km² (27.1% of gulf area) and occurred significantly closer to shore than open water (mean preference = 687.9 m; 95% CI: 616.6–753.7 m; p < 0.001). Water hyacinth was 3.10 times more likely than open water to occur within 100 m of shoreline, with 48% of biomass concentrated within 2 km. A power-law decay model of odds ratio with shoreline distance provided superior fit (R² = 0.870, F = 10.06, p = 0.047) compared to exponential decay (R² = 0.477, p = 0.378). Critically, pronounced nearshore preference occurred only during dry-season conditions (+687.9 m to +1946.6 m), while wet–dry transition periods showed no significant preference (−124.2 m; p = 1.00), supporting wind-driven Stokes drift as the dominant transport mechanism and enabling seasonal prioritization of nearshore management interventions. Full article

Microgrids are increasingly recognized as transformative and crucial constituents within advanced smart grid systems. This study introduces a decentralized energy management approach for interconnected microgrids that leverage renewable energy sources such as wind and solar, alongside distributed energy generators and storage mechanisms. An energy coalition manager (ECM) plays a key role in facilitating each microgrid’s integration to optimize power exchanges, enhance data communication, and reduce costs. The alternate-direction multiplier method is adapted to address optimization challenges, incorporating modifications to develop a censored version that enhances communication efficacy. This refined approach involves the exchange of information among neighboring entities, evaluated against a preset threshold. Through this precise comparison, ECMs strategically reveal their local variables to ensure convergence towards an optimal solution. A detailed case study was conducted to assess the performance, efficiency, and scalability of both methodologies comprehensively. Full article

To improve the low-carbon economic operation of integrated energy systems under wind power uncertainty, this paper develops an optimal scheduling model for an integrated energy system coupling oxygen-enriched combustion with hydrogen–ammonia–carbon utilization pathways. The proposed framework integrates oxygen-enriched combustion, electrolysis-based hydrogen production, methanation, hydrogen fuel cells, ammonia synthesis, urea synthesis, captured CO₂ utilization, reward–penalty ladder-type carbon trading, and IGDT-based wind power uncertainty scheduling. A deterministic scheduling model is first established to minimize the total operating cost, and Information Gap Decision Theory is then introduced to formulate risk-averse and opportunity-seeking scheduling strategies under wind power uncertainty. Simulation results show that, compared with the post-combustion carbon capture scenario and the conventional coal-fired scenario, the proposed system reduces the total operating cost by 3.37% and 8.03%, respectively, and reduces the wind curtailment cost by 40.2% and 57.0%, respectively. Compared with the post-combustion carbon capture scenario, carbon emissions are reduced by 17.7%. The hydrogen–ammonia–urea chain generates approximately 15.68 × 10⁴ CNY of urea revenue and improves carbon resource utilization. Under an IGDT deviation factor of 0.03, the risk-averse strategy increases the total operating cost by approximately 10.30 × 10⁴ CNY to enhance operational robustness, while the opportunity-seeking strategy reduces the total operating cost by approximately 10.30 × 10⁴ CNY and decreases carbon emissions by 19.6 t. These simulation results verify the effectiveness of the proposed scheduling framework under the designed case study. The proposed framework can improve the low-carbon economy, renewable energy accommodation, carbon resource utilization, and adaptability to wind power uncertainty of the studied integrated energy system. Full article

Hybrid power generation systems are an effective solution for matching energy production with electrical load demand. In this study, we examine the viability of a grid-connected hybrid PV/Wind system for meeting the electricity demand of the Lafarge cement factory in Al-Tafilah, Jordan, using HOMER Pro software. The results indicate that the optimal configuration consists of a $6.1 \mathrm{M}\mathrm{W}$ wind turbine and a $22.8 \mathrm{M}\mathrm{W}$ PV array, producing $71.94 \mathrm{G}\mathrm{W}\mathrm{h}$ annually, with wind and PV contributing $31.3\%$ and $68.7\%,$ respectively. The system achieves a 100% renewable fraction while maintaining a high level of reliability, with unmet load and capacity shortage limited to $0.057\%$ and $0.1\%,$ respectively. The economic evaluation reveals a levelized cost of energy (LCOE) of $0.13 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$ and a net present cost (NPC) of $\mathrm{U}\mathrm{S}\mathrm{D} 25.827 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$, representing a $27.8\%$ reduction in LCOE compared to the national grid tariff. In this study, we present a novel large-scale PV/Wind system for the cement industry in Jordan, based on real data, with enhanced techno-economic performance. The innovation of this research lies in the development and optimization of a large-scale grid-connected hybrid PV/Wind system for the cement industry in Jordan, utilizing actual industrial load data and site-specific renewable energy resources. Unlike previous PV-dominated studies, the proposed system integrates a significant contribution of wind energy to improve system reliability and renewable energy penetration, reduce dependency on the national grid, and improve the overall techno-economic performance under actual industrial operating conditions. Full article

As renewable energy sources become increasingly integrated into interconnected power networks, system operating points (OPs) undergo frequent and unpredictable shifts. However, conventional delays in updating equivalent model parameters during these OP transitions often compromise modeling accuracy. To address this challenge, this study proposes an online dynamic OP clustering method for interconnected grids featuring wind and photovoltaic generation. First, an equivalent model for renewable-integrated interconnected grids is established. Subsequently, a dynamic OP clustering strategy is developed; this strategy combines an offline construction phase utilizing joint probability distributions and data clustering with an online update mechanism that dynamically adjusts cluster boundaries via membership calculations. This approach enables real-time clustering, effectively minimizing equivalence errors and adapting swiftly to ongoing network variations. Simulation results based on the China–Mongolia interconnected power grid demonstrate that the proposed method significantly outperforms traditional static approaches in both equivalence accuracy and computational adaptability. By delivering precise, real-time network equivalents, this approach provides robust support for practical grid operations, including online security assessment, optimal power dispatching, and transient stability analysis, thereby contributing to the long-term stability and sustainability of modern power systems. Full article

This study presents a forecast-driven Advanced Forecasting Model (AFM) and Virtual Power Plant (VPP) framework for a hybrid renewable energy system comprising utility-scale solar PV, wind generation, and a Battery Energy Storage System. Long Short-Term Memory neural networks provide real-time short-term forecasts to dynamically schedule power flows based on battery state-of-charge, grid import limits, and system constraints. Solar irradiance forecasting achieved MAE = 10.674 W/m², RMSE = 16.348 W/m², and MAPE = 14.18%, while wind speed forecasting achieved MAE = 0.880 m/s, RMSE = 1.115 m/s, and MAPE = 22.01%. Two dispatch scenarios were evaluated over a 72 h window: a reactive baseline and the proposed AFM/VPP strategy. The AFM reduced total grid imports by 57.48% (1466.34 MWh to 623.47 MWh), increased renewable utilization, and minimized curtailment. Financial analysis indicates an accelerated break-even (Year 6 vs. Year 9), a higher net present value, and cumulative 20-year profits exceeding R26.01 billion despite marginally higher capital expenditure. Emissions analysis shows annual CO₂ reductions from 123,680 t to 61,841 t, yielding 1.236 million tons of avoided emissions over 20 years. These results confirm that forecast-driven dispatch enhances operational efficiency, economic performance, and environmental sustainability, establishing a scalable approach for VPP operation in renewable-rich energy systems. Full article