Search Results (7,946)

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility. Full article

This study introduces a novel multi-channel predictive deconvolution method enhanced by Shearlet-based sparse regularization, aimed at improving the accuracy and stability of subsurface seismic imaging, particularly in offshore wind farm site assessments. Traditional multi-channel predictive deconvolution techniques often struggle with noise interference, limiting their effectiveness. By integrating Shearlet transform into the multi-channel predictive framework, our approach leverages its directional and multiscale properties to enhance sparsity and directionality in seismic data representation. Tests on both synthetic and field data demonstrate that our method not only provides more accurate seismic images but also shows significant resilience to noise, compared to conventional methods. These findings suggest that the proposed technique can substantially improve geological feature identification and has great potential for enhancing the efficiency of seabed surveys in marine renewable energy development. Full article

Effective fault identification and management are critical for efficient wind turbine operation. This research presents a novel ${\mathcal{L}}_1$ adaptive-$\mathcal{SMC}$ system designed to enhance fault tolerance in wind turbines, specifically addressing common issues such as pump wear, hydraulic leakage, and excessive air content in the oil. By combining $\mathcal{SMC}$ with ${\mathcal{L}}_1$ adaptive control, the proposed technique effectively controls rotor speed and power, ensuring reliable performance under various conditions. The controller employs an adjustable gain and an integrated sliding surface to maintain robustness. We validate the controller’s performance in the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulation environment using a 5-megawatt wind turbine under high wind speeds. Simulation results demonstrate that the proposed ${\mathcal{L}}_1$ adaptive-$\mathcal{SMC}$ outperforms traditional adaptive-$\mathcal{SMC}$ and adaptive control schemes, particularly in the presence of faults, unknown disturbances, and turbulent wind fields. This research highlights the controller’s potential to significantly improve the reliability and efficiency of wind turbine operations. Full article

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources. Full article

Drone technology has undoubtedly become an integral part of our everyday life in recent years. The business and industrial use of unmanned aerial vehicles (UAVs) can provide advantageous solutions in many areas of life, and they are also optimal for emergency situations and for accessing hard-to-reach places. However, their application poses numerous technological and regulatory challenges to be overcome. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue, the authors developed a novel trajectory planning method for UAVs to optimize energy consumption during flight. First, an “energy map” was created, which was the basis for trajectory planning, i.e., determining the energy consumption of the individual components. This was followed by configuring the 3D environment including partitioning of the work space (WS), i.e., defining the free spaces, occupied spaces (obstacles), and semi-occupied/free spaces. Then, the corresponding graph-like path(s) were generated on the basis of the partitioned space, where a graph search-based heuristic trajectory planning was initiated, taking into account the most important wind conditions including velocity and direction. Finally, in order to test the theoretical results, some sample environments were created to test and analyze the proposed path generations. The method eventually proposed was able to determine the optimal path in terms of energy consumption. Full article

Hybrid renewable energy systems (HRES) integrating solar, wind, and storage technologies offer enhanced efficiency and reliability for grid-connected applications. However, existing control methods often struggle with maintaining DC voltage stability and minimizing power fluctuations, particularly under variable load conditions. This paper addresses this research gap by proposing a novel control strategy utilizing a PD (1+PI) regulator that combines proportional–integral (PI) and proportional–derivative (PD) controllers. Integrated into the HRES with maximum power point tracking (MPPT), the system includes solar panels, a storage unit, and a wind system featuring a permanent magnet synchronous generator (PMSG). The PD (1+PI) regulator plays a critical role in stabilizing DC voltages within the storage system and collaborates with predictive direct power control (P-DPC) to improve current quality by mitigating fluctuations in active and reactive power. Comparative analysis against traditional direct power control methods shows that the proposed strategy reduces voltage fluctuation by 30% and improves energy utilization efficiency by 25%, validating its efficacy in managing energy from diverse sources to meet nonlinear load demands. The results demonstrate that integrating the PD (1+PI) regulator with MPPT and P-DPC approaches enhances power stability and optimizes energy utilization in grid-connected HRES, underscoring the effectiveness of this advanced control system. Full article

In a highly competitive electricity spot market, virtual power plants (VPPs) that aggregate dispersed resources face various uncertainties during market transactions. These uncertainties directly impact the economic benefits of VPPs. To address the uncertainties in the economic optimization of VPPs, scenario analysis is employed to transform the uncertainties of wind turbines (WTs), photovoltaic (PV) system outputs, and electricity prices into deterministic problems. The objective is to maximize the VPP’s profits in day-ahead and intra-day markets (real-time balancing market) by constructing an economic optimization decision model based on two-stage stochastic programming. Gas turbines and electric vehicles (EVs) are scheduled and traded in the day-ahead market, while flexible energy storage systems (ESS) are deployed in the real-time balancing market. Based on simulation analysis, under the uncertainty of WTs and PV system outputs, as well as electricity prices, the proposed model demonstrates that orderly charging of EVs in the day-ahead stage can increase the revenue of the VPP by 6.1%. Additionally, since the ESS can adjust the deviations in day-ahead bid output during the intra-day stage, the day-ahead bidding strategy becomes more proactive, resulting in an additional 3.1% increase in the VPP revenue. Overall, this model can enhance the total revenue of the VPP by 9.2%. Full article

This paper presents a comprehensive study on optimizing wind farm efficiency by controlling wake effects using the WFSim dynamic simulation model. Focusing on five key factors—yaw wind turbine position, yaw angle, wind farm spacing, longitudinal wind turbine spacing, and yaw rate—we qualitatively analyze their individual and combined impact on the wind farm’s wake behavior and mechanical load. Through a quantitative approach using the orthogonal test method, we assess each factor’s influence on the farm’s overall power output. The findings prioritize the following factors in terms of their effect on power output: yaw wind turbine position, yaw angle, wind farm spacing, longitudinal spacing, and yaw rate. Most significantly, this study identifies optimal working conditions for maximizing the wind farm’s average power output. These conditions include a wind turbine longitudinal spacing of 7.0D, a wind farm spacing of 15.0D, a yaw angle of 30°, and a yaw rate of 0.0122 rad/s, with the first and second rows of turbines in a yaw state. Under these optimized conditions, the wind farm’s average power output is enhanced to 35.19 MW, marking an increase of 2.86 MW compared to the farm’s original configuration. Additionally, this paper offers an analysis of wake deflection under these optimal conditions, providing valuable insights for the design and management of more efficient wind farms. Full article

Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters’ capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid’s efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time. Full article

Nowadays, intermittent energy resources, such as wind turbines, and direct current (DC) transmission have been extensively integrated into power systems. This paper proposes an identifying method for weak lines of novel power systems with intermittent energy resources and DC lines integration, which aims to provide decision making for control strategies of novel power systems and prevent system blackouts. First, from the perspective of power system safety and stability, a series of risk indicators for the risk assessment of vulnerable lines is proposed. Then, lines in the system are tripped one by one. The calculation method for the proposed risk indicators is introduced. The impact of each line outage on system safety and stability can be fairly evaluated by these proposed risk indicators. On this basis, each risk assessment indicator is weighted to obtain a comprehensive risk assessment indicator, and then the risk caused by each line outage on the system can be quantified efficiently. Finally, the test system of a modified IEEE-39 bus system with wind farms and DC lines integration is used to verify the applicability of the proposed method, and the effectiveness of the proposed method is also demonstrated by comparing with existing methods. Full article

The availability of energy-bearing resources is a key determinant of the development strategy of the world’s energy systems. In the case of Poland, the wind energy potential of the Baltic Sea provides the basis for the development of offshore wind energy in the country. The processes of transforming solutions towards green technologies require appropriate legislation, significant financial outlays, as well as public support for this dimension of activities. The latter strand requires continuous measurement to dynamically model the energy transition strategy. In the author’s opinion, the available literature does not sufficiently explain this theme in relation to Polish conditions. Hence, it was considered reasonable to investigate the impact of offshore wind energy development in Poland on public opinion in a selected region of Poland, in order to diagnose the current scale of support for the changes taking place, and to identify the main expectations and fears related to this activity, which was assumed as the main objective of the study. The added value of the survey is the analysis of changes in public opinion over time. The methodology used for the research was a study of the scientific literature, with analysis of the results of own and secondary research conducted in Poland. In terms of in-depth research, statistical survey techniques supported by the PQstat programme were used. The results of the survey confirmed significant public support in the surveyed area for offshore wind energy development in Poland (68%). The overall percentage of support for offshore development increased by 5% y/y. Economic considerations for the support of the activities in question with the potential vision of lowering energy prices in the domestic market were confirmed with a result of 65%. It was further confirmed that a key aspect of support for the offshore development strategy in the surveyed region of Poland is the potential for development of the region in relation to offshore farm investments, with a focus on the labour market, with indications of 53% for both themes. Interestingly, there was no concern in relation to the risk of landscape change in an undesirable direction in 2024. Full article

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs. Full article

Synthetic nitrogen fertilizer is the backbone of modern agriculture, helping to feed ~50% of the world’s population. However, the current industrial production, distribution, and use of nitrogen fertilizers are built on an unsustainable foundation of fossil resources, and are energy-intensive, environmentally polluting, and inefficient in their usage. With the rapidly declining cost of renewable electricity, such as solar and wind, it is time to develop and implement the decentralized production and application of nitrogen fertilizer with nonthermal plasma technologies. Such locally sourced production at the farm site, using only air and water as feedstock, circumvents the need for the extensive capital investment and infrastructure required for synthetic nitrogen fertilizer production and storage, as well as the complex and costly distribution networks. It will be adaptive to the intermittency of the solar/wind electricity supply, leave no carbon footprint, and also have the advantage of being easily switched on/off, immediately responding to weather changes and local conditions, such as soil, climate, crops, and farming business models, for precision agriculture. Full article

Graphene platelets (GPLs) are gaining popularity across various sectors for enhancing the strength and reducing the weight of structures, thanks to their outstanding mechanical characteristics and low manufacturing cost. Among many engineering structures, wind turbine blades are a prime candidate for the integration of such advanced nanofillers, offering potential improvements in the efficiency of energy generation and reductions in the construction costs of support structures. This study aims to explore the potential of GPLs for use in wind turbine blades by evaluating their impact on material costs as well as mechanical performance. A series of finite element analyses (FEAs) were conducted to examine the variations of mechanical performances—specifically, free vibration, bending, torsional deformation, and weight reductions relative to conventional fiberglass-based blades. Details of computational modeling techniques are presented in this paper. Based on the outcomes of these analyses, the mechanical performances of GPL-reinforced wind turbine blades are reviewed along with a cost–benefit analysis, exemplified through a 5MW-class wind turbine blade. The findings affirm the practicality and benefits of employing GPLs in the design and fabrication of wind turbine blades. Full article

With the increasing use of data-driven modeling methods, new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through the literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. The literature shows the advantages of data-driven methods: a reduction in computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitudes for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection, while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation datasets, with Gaussian process regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine. Full article