Wind Sources Research Articles

Quantifying resilient urban energy systems: Statistical analysis of climate adaptation, renewable integration, and socioeconomic dynamics

Multi-energy systems (MES) become increasingly important to accommodate renewable energy (RE) since fossil fuels diminish and RE is more prevalent. Power systems are challenged by high penetrations of RE, including frequent wind curtailments and uncertain RE resources threatening their security. A 2-step scheme for MES is proposed in the study to generate reliable operational decisions by incorporating integrated heat-electricity demand response (DR). In the initial phase, dynamic programming was applied to determine the optimal solution for previous linkage sessions. Subsequently, machine learning techniques were trained using historical data and the resulting optimal decisions to make optimal real-time decisions without prior knowledge of future power costs or vehicle usage. An accurately trained deep neural network is capable of significantly reducing charging costs, usually approaching the retrospectively calculated optimum charging price. In the suggested scheme, the operating prices are minimized under a base scenario ensuring that no constraint is violated regardless of the scenario. RE curtailment can be reduced significantly with power-to-gas (P2G) devices that convert excess energy from wind and photovoltaic sources into natural gas. In addition, this paper proposes integrated heat-electric DR as a means of reducing operating prices and enhancing security against uncertainties by employing couplings between different systems. The outcomes show that robust optimization enhances the security of systems, and the unified electric-heat DR can boost the use of RE while improving the economic benefit. It should be noted that this paper investigated under the impact of the climate changes and also socioeconomic dynamics that can change consuming energy in urban systems.

Unraveling energy hub dynamics and political economy toward sustainable urban energy: A multifaceted examination of policy decision making and implementation

In this study, we introduce a visionary approach to sustainable urban energy systems by proposing a multi-carrier power grid or energy hub (EH) that seamlessly integrates electric and gas sources. Our analysis spans the course of four distinct seasons, where we meticulously scrutinize the dynamic demand profiles encompassing power, heat, and cooling power. Our findings reveal significant season-dependent variations in the performance of the EH system, highlighting the propensity for heightened energy consumption during warmer seasons, primarily driven by cooling needs, while a contrasting trend emerges during colder seasons with an emphasis on heating requirements. Furthermore, we investigate the pivotal role of user engagement in demand response programs, elucidating how their active participation results in a notable reduction in overall power consumption, particularly during the warmer months, thereby reshaping energy utilization patterns. To address the intricate optimization challenges inherent in this multifaceted urban energy ecosystem, we introduce the innovative balanced seagull optimization algorithm, which offers an effective solution. Leveraging simulations of the EH system, which harnesses the potential of solar and wind power sources, our study demonstrates compelling outcomes, including amplified EH profits, a decrease in energy system costs, and a reduction in social welfare expenditures. This multifaceted examination underscores the complex interplay between policy decisions, the imperative for sustainable urban energy solutions, and the ever-evolving political-economic landscape. As we navigate toward a more sustainable and resilient urban energy future, this research provides invaluable insights that empower policymakers, urban planners, and stakeholders to make informed decisions for the benefit of society and the environment.

Sodium thermal electrochemical converter coupled with organic Rankine cycle and thermochemical heat storage for power-heat-power application

The inherent intermittency of solar PV and wind sources makes the power supply from renewables unstable. To mitigate this issue, power-heat-power (PHP) systems have been developed in which surplus/cheap electricity can be stored in the form of high-temperature heat, and whenever needed it can be used for electricity generation using a power cycle. The present work focuses on the integration of an innovative combined cycle consisting of a sodium thermal electrochemical converter (Na-TEC) in combination with an organic Rankine cycle (ORC) as a bottoming cycle, with a redox oxide-based thermochemical heat storage (TCHS) system for PHP application. A Multiphysics model is developed to simulate and analyze the performance of the proposed combined cycle. Parametric optimization is performed to achieve optimal system performance. The proposed Na-TEC/ORC achieved a maximum power density of 0.98 W/cm2 and a maximum conversion efficiency of 40 % which is 21.2 % higher in comparison to a standalone Na-TEC device. A maximum exergetic efficiency of 53 % can be achieved while keeping the evaporator temperature at 1150 K and the condenser temperature at 550 K for this cycle. For the maximum conversion efficiency of Na-TEC, a round-trip-efficiency of 36.1 % is calculated for the proposed PHP system provided the TCHS system is 95 % efficient.

Natural Ventilation and Indoor Air Quality in Domestic School Building: CFD Simulation and Improvement Strategies

Natural ventilation is a process of replenishing used air from the interior environment with fresh outer air without the use of mechanical equipment. This project aims to investigate the feasibility and performance of implementing natural ventilation in dilapidated classroom designs of actual geometry. Computer models of an existing classroom were created using SOLIDWORKS based on real-scale building floor plans, while the same software was applied to compute flow simulation studies to visualise the airflow pattern, temperature distribution, relative humidity, and the thermal comfort level of occupants in the classrooms. Estimated climate parameters were input in the simulation process to illustrate better the effect of hot and humid climate conditions in Malaysia towards the thermal comfort level of occupants. In this project, a visualised wind tunnel was created to simulate the prevailing wind source that supplies wind to the designed classroom, and the airflow pattern across the designed building was analysed. From the initial simulation results, it was discovered that the airflow into the classroom is uneven, whereby some locations in the building are experiencing extremely low wind breeze, which eventually affects the comfort level of occupants in the room. On the other hand, an internal analysis focusing on the interior comfortability of the classrooms was applied to examine the thermal comfort condition while using the naturally ventilated classrooms. Recommendation optimisation approaches such as the louvre angle and introduction of a windcatcher was presented in this project to improve the natural ventilation performance of the designed classroom.

Optimal integration of hybrid pumped storage hydropower toward energy transition

This study explores the advantages of combining variable renewable energy sources like solar and wind with a pumped storage hydroelectric (PSH) system for grid integration. The hybrid modeling systems considered in this study consist of four distinct schemes and seasons to ensure their adaptability to real-world conditions. Each scheme is defined by specific rules and algorithms, including those without PSH, with PSH, with PSH adjustments, and with an optimized schedule for the PSH storage system. The last scheme of daily simulation with an optimization solver is used to optimize the integration of PSH with a hybrid power system that uses solar and wind energy as primary renewable sources by minimizing the daily operating costs. The optimal value is derived from the minimized operating costs and incoming and outgoing energy accumulation. The dispatch system includes load demand satisfaction considering the intermittent nature of solar and wind sources and demand fluctuations presented with a hybrid system of PV solar, wind turbines, PSH, and other power generators as backup. Limitations of the study include considering various factors influencing the integration of a PSH unit, such as different ecological and geographical conditions, distinct atmospheric data, and varying load and demand patterns, which may result in different outcomes in different situations. The results show that using the developed model to optimally schedule the integration of PSH with renewables and the hybrid system in each season provides significant cost savings and CO₂ emissions reductions while meeting the same load patterns. Namely, it can be inferred from the tested schemes that PSH plays a crucial role in facilitating the transition to sustainable energy sources. This is evident through project financing results, which indicate a favorable positive Net Present Value (NPV) and a relatively short payback period of 5 years. Furthermore, Scheme 4 demonstrates the potential for a substantial 84 % reduction in CO₂ emissions during the summer season, and daily costs can be significantly lowered by over 90 % across all seasons. Overall, two simplified net present value and payback period estimation models have shown feasible project financing for a simulation period of 25 years with an interest rate of 10 % for the combined PSH and hybrid system.

Advances in Hole Transport Materials for Layered Casting Solar Cells.

Huge energy consumption and running out of fossil fuels has led to the advancement of renewable sources of power, including solar, wind, and tide. Among them, solar cells have been well developed with the significant achievement of silicon solar panels, which are popularly used as windows, rooftops, public lights, etc. In order to advance the application of solar cells, a flexible type is highly required, such as layered casting solar cells (LCSCs). Organic solar cells (OSCs), perovskite solar cells (PSCs), or dye-sensitive solar cells (DSSCs) are promising LCSCs for broadening the application of solar energy to many types of surfaces. LCSCs would be cost-effective, enable large-scale production, are highly efficient, and stable. Each layer of an LCSC is important for building the complete structure of a solar cell. Within the cell structure (active material, charge carrier transport layer, electrodes), hole transport layers (HTLs) play an important role in transporting holes to the anode. Recently, diverse HTLs from inorganic, organic, and organometallic materials have emerged to have a great impact on the stability, lifetime, and performance of OSC, PSC, or DSSC devices. This review summarizes the recent advances in the development of inorganic, organic, and organometallic HTLs for solar cells. Perspectives and challenges for HTL development and improvement are also highlighted.

Net Zero Energy cost Building system design based on Artificial Intelligence

This paper presents a solution to the energy planning problem in buildings by implementing the net Zero Energy cost Building (nZEcB), a new concept that refers to buildings with zero or almost zero annual energy costs, using Artificial Intelligence (AI) techniques such as bidirectional long short-term memory, ordinary least squares linear regression, K-means, Pearson’s correlation, decision tree, and binary gravitational search algorithm. AI techniques are used to design the optimal structure of a distributed generation system. The system includes wind and photovoltaic renewable energy sources, a battery bank, and an automated capacitor bank for power factor compensation. A case study was conducted in a real public building with an annual consumption of 1.748 GWh, which resulted in the specification of a distributed generation system with a generation of 2.805 GWh per year. This system meets approximately 160.5% of the building’s electrical demand and 99.999% of annual energy costs, i.e., attending the nZEcB concept. The excess production of approximately 60% of the energy is necessary because the exported energy (feed-in tariff) has a lower value than that imported from the network, having to deduct costs with demand, in addition to having to compensate for losses in the battery banks. The project has a payback period of 6.79 years. This novel study demonstrates the feasibility and effectiveness of using AI techniques to achieve nZEcBs more efficiently, economically and sustainably.

Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction

The increasing generation of renewable electrical power, particularly from wind and solar sources, has significantly influenced the national energy and power transmission systems. However, accurate forecasting of wind and photovoltaic (PV) power remains challenging due to the stochastic and highly nonlinear nature of wind speed and solar irradiance. Traditional models often fail to produce accurate power forecasts. To address this challenge, this paper proposes a novel deep learning model based on the Quantile-Transformed Multi-Attention Residual Framework (QT-MARF). The proposed model is built on a Transformer architecture with Residual Net and Multi-Head Attention. QT-MARF utilizes sequential processing through gated residual networks, enabling the model to learn complex patterns and make accurate power forecasts. The model utilizes PV and wind data from Natal, Santa Vitoria, and the Chinese State Grid (CSG). Case studies are conducted to validate the estimation performance of the hybrid models. The proposed QT-MARF demonstrates promising results in terms of accuracy and efficiency, outperforming traditional models in metrics such as Mean Absolute Error (MAE), correlation coefficient (CC), Root Mean Squared Error (RMSE), and R-squared (R2). Comparative analysis with state-of-the-art techniques such as the Inception-embedded attention-based memory fully-connected network (IAMFN) model, CNN-GRU, CNN-LSTM, and RNN highlights the superiority of the proposed model. These findings suggest that the proposed model offers a promising solution for the challenging task of wind and PV power forecasting.

Comments: The New “Gold Rush” Hunts for Subsurface Hydrogen

_ A gold strike was announced in late October. But in this case the gold was naturally occurring hydrogen in a former coal basin. Known as “white” or “gold” hydrogen, the reported discovery of naturally formed underground hydrogen was made in northeastern France’s Lorraine coal basin, a region between France and Germany. The last coal mine was shut down 20 years ago. Researchers at the French National Centre of Scientific Research (CNRS) were testing a probe designed to analyze gases dissolved in the water of deep underground rock formations, looking for methane, when they detected hydrogen concentrations at depths of 1100 m (14%) and 1250 m (20%). Their calculations estimated the deposits’ potential as between 6 and 250 million metric tons of hydrogen. This wasn’t their first discovery of underground hydrogen in the area. Philippe de Donato and Jacques Pironon made a similar finding “by chance” as part of the Regalor research project in collaboration with Française de l’Energie (FDE), an independent multienergy company, the University of Lorraine, and CNRS. FDE announced the discovery in a press release in May, saying the measurements of hydrogen were made in its previously drilled Folschviller wellsite in the carboniferous aquifer of the Lorraine basin. Launched in 2018, the project’s aim was to confirm an assessment carried out in 2012 by France’s IFPEN petroleum and new energies institute. After analyzing a sample of the soil under the basin, the institute concluded that it contained 370 billion m3 of methane, which represents 8 years of gas consumption in France. De Donato and Pironon said, “The work carried out within the framework of the Regalor project has made it possible to demonstrate that the fluids within the carboniferous formations of the Lorraine mining basin are very significantly enriched in hydrogen, with a measured concentration of 15% at 1093 m depth and estimated at 98% at 3000 m depth.” FDE said in May that it applied for an exclusive mining exploration permit for the exploration of natural hydrogen in the basin. The permit covers an area of 2254 km² in the Grand Est region. The company said a site for a pilot will be identified based on the results obtained and then built to initiate local production and recovery of natural hydrogen in the Grand Est Region. In its October investor update, FDE said measurements will be performed in three of its existing wells by the end of the year to determine the extent of the hydrogen deposit. Reservoirs have also been discovered in the US, Canada, Finland, the Philippines, Australia, Brazil, Oman, Turkey, and Mali. In Mali, the Bourakebougou water well, Bougou-1, was drilled in 1987 in the Taoudeni Basin (https://doi.org/10.1016/j.ijhydene.2018.08.193), a large sedimentary system present mainly in Algeria, Mauritania, and Mali. The well was cemented after a gas explosion occurred during drilling operations at a measured depth of 112 m. Unplugged in 2011 for use as a pilot well for local hydrogen production, gas was reported comprising 98% hydrogen, 1% nitrogen, and 1% methane. Hydrogen was then produced as an energy resource to supply local electricity through a company named Petroma, renamed Hydroma. From 2017 to 2019, the company drilled 24 wells. Among the new breed of gold prospectors are several startup companies including Natural Hydrogen Energy, Koloma, Helios Aragon, Gold Hydrogen, HyTerra, and H2Au. Helios Aragon owns exploration permits in northern Spain’s Aragon region and will begin drilling the Monzon-2 appraisal well in 2024 at a cost of $12 million. Estimates for the well are 1.1 million tons of hydrogen, and the company claims the Monzon field holds 5 to 10 million tons within its permits and more than 100 million tons in the region. Natural Hydrogen Energy and HyTerra claim the “first wildcat well targeting natural hydrogen in Nebraska,” the Hoarty well at Project Geneva. HyTerra also holds leases in the Nemaha Ridge in Kansas. The cost advantages of subsurface hydrogen are frequently cited by the early prospectors. For example, the wells in Mali have the potential to generate hydrogen gas at a cost of 50 cents/kg (https://doi.org/10.1016/j.ijhydene.2018.08.193), which is only one-tenth the cost of producing hydrogen via electrolysis using solar, wind, geothermal, or other renewable energy sources. If commercialization and economies of scale pan out, this may become the gold standard for hydrogen energy. For Further Reading The Curious Case of Geologic Hydrogen: Assessing its Potential as a Near-Term Clean Energy Source (https://doi.org/10.1016/j.joule.2022.01.005) by E.M. Yedinak, US Department of Energy, Advanced Research Projects Agency-Energy. The Occurrence and Geoscience of Natural Hydrogen: A Comprehensive Review (https://doi.org/10.1016/j.earscirev.2020.103140) by V. Zgonnik, Natural Hydrogen Energy LLC.

Examining Infrasound Propagation at High Spatial Resolution Using a Nodal Seismic Array

AbstractInfrasound—acoustic waves in the atmosphere below 20 Hz—is a useful monitoring tool. Topography and atmospheric structure strongly control infrasound propagation, and at common source–receiver distances neither of these effects can be ignored when quantitative source constraints are sought. Detailed spatial measurements of the infrasound wavefield would inform propagation models and improve source estimates. However, the “large‐N” deployment strategy now well‐known in seismology has not yet been realized for infrasound studies. Here, we use the 900‐node seismic array from the 2014 Imaging Magma Under St. Helens (iMUSH) experiment as a proxy for a large‐N infrasound network, by leveraging acoustic–seismic coupled arrivals. The active‐source component of iMUSH consisted of 23 shallowly buried explosions around Mount Saint Helens volcano; these explosions produced epicentral infrasound recorded on the nodes. We find that the bulk presence of ground‐coupled infrasound on the nodes is controlled by wind noise and source–receiver distance, with observed arrivals for eight explosions. Explosions with the most extensive coupling produce complex spatial waveform patterns across the array. These patterns are related to both topographic and atmospheric propagation effects, as well as spatially variable site (coupling) effects. We compare our observations to simple topographic diffraction and high‐resolution wind advection models, and full‐wave numerical simulations. We find strong spatial correlations between (a) coupled arrival strength and modeled topographic obstruction and (b) coupled arrival time and along‐path winds. Our seismoacoustic analyses and results are applicable to other existing and future nodal seismic data sets and can expand the utility of such deployments.

A novel distributed approach for event-triggered economic dispatch of energy hubs under ramp-rate limits integrated with sustainable energy networks

This paper investigates a new consensus-oriented distributed approach for the event-triggered (ET) economic dispatch problem (EDP) over a smart grid under ramp-rate limits (RRLs) integrated with green power sources (GPSs) such as solar and wind energy for demand response strategies over hybrid energy power systems. To address the RRL condition, the authors have transformed the RRLs as minimum and maximum bounds on the derivative of generation for a generator. Then, a Karush–Kuhn–Tucker (KKT) condition and a more practical approximate KKT condition are developed for determining the optimality conditions. A practical ET protocol is proposed over a topology between generators by application of the proposed approximate KKT condition. In contrast to existing distributed optimization methods, this paper provides both optimally condition and distributed optimization scheme for dealing with RRLs integrated with sustainable hybrid energy systems. In addition, a computationally efficient ET mechanism, eliminating Zeno behaviour, has been considered for dealing with the efficient utilization of communication resources. This study incorporates the real-time input data from thermal production plants and GPSs for experimental analysis. RETScreen software having data-set of over 6,700 local meteorological stations is applied to obtain input data for GPSs. Furthermore, Weibull and Beta distribution functions have been applied for dealing with uncertainty in wind and solar energy sources. Two case studies are examined (with and without GPSs), and simulation results demonstrated the suitable performance of the proposed distributed ET EDP approach.

Evolution of solar wind sources and coronal rotation driven by the cyclic variation of the Sun’s large-scale magnetic field

Context. The strength and morphology of the Sun’s magnetic field evolve significantly during the solar cycle, with the overall polarity of the Sun’s magnetic field reversing during the maximum of solar activity. Long-term changes are also observed in sunspot and geomagnetic records; however, systematic magnetic field observations are limited to the last four cycles. Aims. Here, we investigate the long-term evolution of the Sun’s magnetic field, and the influence this has on the topology and rotation of the solar corona. Methods. The Sun’s photospheric magnetic field was decomposed into spherical harmonics using synoptic Carrington magnetograms from (1) the Wilcox Solar Observatory, (2) the Michelson Doppler Imager on board the Solar and Heliospheric Observatory, and (3) the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. The time evolution of the spherical harmonic coefficients was used to explore the variation of the Sun’s magnetic field, with a focus on the large-scale modes. Potential field source surface extrapolations of the photospheric field were computed to follow topological changes in the corona. Results. The sources of the Sun’s open magnetic field vary between the polar coronal holes and activity-driven features such as active regions, and equatorial coronal holes. Consequently, the mean rotation rate of the solar wind is modulated during each cycle by the latitudinal variation of open field footpoints, with slower rotation during minima and faster (Carrington-like) rotation during maxima. Conclusions. Coronal rotation is sensitive to cycle to cycle differences in the polar field strengths and hemispherical flux emergence rates. The mean rotation of the corona varies similarly to the ratio of quadrupole to dipole energy. Cycle 23 maintained a larger fraction of quadrupolar energy in the declining phase, which kept the sources of the open magnetic flux closer to the equator, extending the period of faster equator-ward connectivity. The ratio of quadrupole to dipole energy could be a useful proxy when examining the impact of differential rotation on the coronae of other Sun-like stars.

The variable source of the plasma sheet during a geomagnetic storm

Both solar wind and ionospheric sources contribute to the magnetotail plasma sheet, but how their contribution changes during a geomagnetic storm is an open question. The source is critical because the plasma sheet properties control the enhancement and decay rate of the ring current, the main cause of the geomagnetic field perturbations that define a geomagnetic storm. Here we use the solar wind composition to track the source and show that the plasma sheet source changes from predominantly solar wind to predominantly ionospheric as a storm develops. Additionally, we find that the ionospheric plasma during the storm main phase is initially dominated by singly ionized hydrogen (H+), likely from the polar wind, a low energy outflow from the polar cap, and then transitions to the accelerated outflow from the dayside and nightside auroral regions, identified by singly ionized oxygen (O+). These results reveal how the access to the magnetotail of the different sources can change quickly, impacting the storm development.

Multi-objective optimal power flow of thermal-wind-solar power system using an adaptive geometry estimation based multi-objective differential evolution

Sustainable energy is a key component of sustainable development. The current grid can be supplied by fossil fuel generators and renewable energy sources (RESs)-based generators, such as solar photovoltaic (PV) and wind power generators. In an electrical network, power generation from several sources must be optimally coordinated to ensure efficient and economical operation. However, the intermittent and uncertain nature of RESs complicate the operation of power systems. In this study, an adaptive geometry estimation-based multi-objective differential evolution (AGE-MODE) method is proposed for multi-objective optimal power flow in a hybrid power system of thermal, wind, and solar energy sources (MOOPF-TWS). In the proposed approach, wind and solar PV power outputs are predicted based on Weibull and lognormal probability distribution functions, respectively. Therefore, the generation costs for solar and wind power can be divided into direct costs, penalty costs for underestimation, and reserve costs for overestimation. Furthermore, the emissions, voltage deviation, and real power loss are considered in particular cases. AGE-MODE is applied to modified IEEE 30-bus and 57-bus systems, where different case studies are simulated with combinations of two-, three-, and four-objective optimizations in MOOPF-TWS problems. Comparisons between AGE-MODE and other recently developed multi-objective methods demonstrate its effectiveness in resolving MOOPF-TWS problems, particularly for cases with more than two objectives.

An intelligent optimal charging stations placement on the grid system for the electric vehicle application

In smart cities, electrified vehicle plays a vital role. Due to the number of electric vehicles increasing rate, the optimised deployment of the charging station without maximum loss and voltage imbalance is required. Many existing strategies studied for the optimal charging station deployment result in higher power utilisation, power loss, harmonic distortion and voltage imbalance. Therefore a novel Dove-based Recursive Deep Network (DbRDN) was planned to implement. The DG grid system is initially created by integrating hybrid wind, solar and hydropower sources. Subsequently, the DbRDN is designed for the optimal location for the placement of the EV charging station by analysing load and line data. Moreover, the efficiency of the developed system is evaluated at both the balanced and unbalanced conditions and the outcomes are computed in terms of power loss, harmonic distortion, voltage imbalance, error and accuracy. The results are compared with prevailing techniques to validate the improvement score.

Comprehensive Analysis of Kinetic Energy Recovery Systems for Efficient Energy Harnessing from Unnaturally Generated Wind Sources

Alternative energy is a rapidly expanding research area primarily driven by concerns over pollution caused by inefficient conventional energy sources. However, many developing nations rely heavily on these conventional sources. In response, numerous researchers have focused on developing kinetic energy recovery systems (KERS) to capture and utilize the energy lost due to inefficiency. These KERS can be implemented in various scenarios, such as near railroad tracks, industrial flue stacks, cooling towers, and air conditioning outlets. The primary objective of this paper is to critically and comprehensively evaluate the research conducted on the development of these systems. The review reveals that the wind speed in the studied cases ranged between 15 and 22 m/s, providing a consistent and theoretically maximum potential higher than any location worldwide. Furthermore, the impact of these systems on the Betz limit, as well as their drawbacks and crucial advancements necessary for practical implementation, have been thoroughly assessed. This paper contributes to the existing body of knowledge by presenting a comprehensive analysis of the research conducted on KERS development. It highlights the potential of these systems in harnessing untapped energy sources and identifies key areas that require further attention for successful practical application.

Site selection modeling of hybrid renewable energy facilities using suitability index in spatial planning

The research focuses on how hybrid renewable energy facilities can be addressed within the spatial planning system by adopting an approach that minimizes the problems related to the sustainability of energy. The study aims to develop a spatial model based on parameters for the site selection processes of hybrid renewable energy facilities relying on wind, solar, and biomass energy sources within the spatial planning system in Turkey. The integration of fuzzy logic approach and Geographic Information Systems (GIS) tools has been proposed to establish a spatially applicable methodological framework. The methodology focuses on the neighborhood scale in terms of planning level and covers the Karatay district, where the hybrid renewable energy potential is high. The research findings indicate that the GAMMA operator used in the methodology provides the most optimal areas with a value of γ = 0.90 and generates site selections for hybrid renewable energy facilities based on the source with the highest suitability index in single-source utilization. Additionally, the operator determines the best alternative in terms of resources in different hybrid scenarios. In this context conducted in Karatay district, it was found that the highest suitability index in terms of hybrid applications is associated with biomass-solar energy combinations.

Hybrid Wind/PV E-Bike Charging Station: Comparison of Onshore and Offshore Systems

The concept behind this research article is advancement towards utilizing renewable energy sources of wind–solar to generate electrical energy for E-bike (electric bike) charging stations. To optimize the design and operation control of the wind–solar E-bike charging station system, the development of modelling this hybrid power generation system, consisting of solar and wind energy combined with battery storage, is proposed and will be studied in this paper. A university campus setting is utilized for the case study by comparing offshore (Huangdao) and onshore (Laoshan) sites. The proposed research will focus on annual energy production (AEP) and system cost analysis. The proposed work’s main objectives are to analyze the wind/solar properties of the installation’s location using the last 20 years’ data, calculate the AEP for wind turbines and solar PV, and estimate how many E-bikes can be charged day/year with reliable operation. We have calculated that the hybrid power available is 27.08 kWh/day offshore and 22 kWh/day onshore. This research study concludes that on average, based on AEP, in the case of offshore, 5110 E-bikes can be charged per year and in the case of onshore, 4015 E-bikes can be charged per year. We have also calculated the COE (cost of energy) for 20 years for the proposed project, which is $0.62/kWh onshore and $0.46/kWh offshore.

Comparative Study and Dynamic Performance Analysis of DFIG based Wind Energy Conversion System

This paper presents a novel control strategy for wind energy conversion system for improving the dynamic stability of the system. In off-grid applications, the load voltage and frequency should be managed in steady-state and transient circumstances. For rural areas with unpredictable wind and load circumstances, electricity quality and control are crucial tasks. In order to improve power quality, dynamic stability and power management in direct drive standalone wind energy systems, this research provides a coordinated proportional resonant (PR) controller. Under different operating situations, such as variable wind with step increases and decreases in wind velocity, balanced and unbalanced load conditions, the dynamic performance of a doubly-fed induction generator (DFIG) is evaluated. In order to meet the load requirement, the suggested PR control technique with battery energy controller also provides efficient power balancing between the wind and battery sources. A fair performance comparison is made between the proposed control strategy and conventional vector control strategy by using the MATLAB/SIMULINK platform under varying wind and load circumstances. The proposed control approach shown better dynamic stability, improving the power quality of the system and better synchronization.

Implications of Climate Change on Wind Energy Potential

This study examines the crucial role of wind energy in mitigating global warming and promoting sustainable energy development, with a focus on the impact of climate change on wind power potential. While technological progress has facilitated the expansion of the industry, it is crucial to continue making advancements to reduce the life-cycle emissions of wind turbines and ensure their long-term sustainability. Temporal discontinuities present a significant challenge for renewable energy sources. This study highlights the potential of hybrid systems to provide consistent energy output from wind sources. It also examines the variability in wind patterns caused by climate change, acknowledging that outcomes vary depending on geographic contexts, modeling approaches, and climate projections. Notably, inconsistencies in wind speed projections from downscaled general circulation models introduce uncertainties. While specific regions, such as North America, project an increase in wind speeds, others, such as the Mediterranean, face a potential decrease. Of particular note is the forecast for a potential long-term increase in wind speeds in Northern Europe. In conclusion, the wind energy industry displays considerable potential for growth, driven by technological advancements. However, the complexities resulting from climate change necessitate further research. Such insights are crucial for informed energy policy formulation and sustainable industry progress.