Wind Plants Research Articles

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Graph network heterogeneity predicts interplant wake losses

Wind plants generate large-scale wakes, which can affect the performance of neighboring installations. Such wakes are challenging to estimate due to the inherent complexity in modeling wake interactions between large quantities of turbines at various distances. Weighted directed graph networks can inform complex models by linking turbine pairs into chains of upstream and downstream neighbors for a given wind direction. A novel interpretation of the graph network adjacency matrix is proposed where each element of the matrix represents the cumulative impact of upstream turbines on an individual. In this study, wake losses were estimated with an engineering wake model across a range of inflow conditions for nine parametric variations of a system containing two neighboring wind plants. The parametric nature of the study isolates turbine spacing within the plant, separation distance between plants, and wind direction as the main drivers of wake losses. Spatial heterogeneity is computed from the weighted average adjacency matrix of each plant arrangement. The proposed method is orders of magnitude faster than wake modeling and does not require detailed turbine information or atmospheric conditions. The weighted average adjacency matrix provides insight on the spatial organization of wake losses at various scales. Plant heterogeneity is correlated with wake losses within and among plants. Framing wind plant wake interaction in terms of graph network spatial heterogeneity provides an efficient approach for predicting wake losses within and among neighboring wind plants with applications to other complex systems where wake interactions are key factor.

Green hydrogen as a sustainable operations strategy: a socio-economic perspective

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20-4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5% claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development.

Enhancing Regional Wind Power Forecasting through Advanced Machine-Learning and Feature-Selection Techniques

In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions.

COMPUTER MODELING OF TRANSIENT ELECTROMECHANICAL PROCESSES IN A WIND POWER PLANT WITH A MAGNETIC GEARBOX

In this work, a computer Simulink model of a wind power plant has been developed, which uses a magnetic gearbox instead of a mechanical gearbox and also contains a synchronous permanent magnet generator. A separate Simulink model of a magnetic gearbox built on the basis of a modulated magnetic field in the air gap was developed, which al-lows to study the stability of its operation both in steady-state and transient modes. Calculations of various dynamic modes of the wind power plant’s operation were carried out, based on the developed model, such as the starting mode, an instantaneous increase in the wind speed acting on the wind turbine, and an increase in the load of the electric gen-erator. According to the results of the calculations, it is shown that in transient modes, when short-term overloads oc-cur, both rotors of the magnetic gearbox can fall out of synchronous motion for a certain period of time and then, de-pending on the parameters of the gearbox (as well as its other elements), the electromechanical system either reaches a certain operating steady-state mode or loses the ability to transfer mechanical power from the wind turbine to the gen-erator. It has been shown that the use of a more powerful magnetic gearbox, with an increased value of the maximum magnetic torque, allows of a more overload-resistant operation of both: a gearbox and the wind plant as a whole. Ref-erences 9, figures 10.

Hydrogen-Electrodialysis with Bipolar Membrane (H-EDBM): First experiments and preliminary economic evaluation

In the present work Electrodialysis with Bipolar Membranes (EDBM) for brine valorisation is for the first time investigated for the simultaneous production of hydrogen and chemicals. EDBM allows current densities one order of magnitude higher than ED and RED electro-membrane processes, making the technology more attractive to the hydrogen market. In this work, an experimental campaign with the Hydrogen-EDBM (H-EDBM) was conducted for the first time, investigating different configurations consisting of different end-membranes for the electrode compartments. The results showed 98 % ±2 % faradic efficiency for hydrogen production, with a productivity of up to 18.4 mol h−1 m−2 at the current density of 1000 A m−2. In addition, the Bipolar Membranes allow the simultaneous production of chemicals such as NaOH, for which a Current Efficiency of 96 % and a Specific Energy Consumptions (SECs) of 1.78 kWh kgNaOH−1 were obtained. Eventually, the research was completed with a preliminary economic analysis combining the different operations and costs of the tested configurations in order to identify the optimum of the process. The Levelized Costs Of Hydrogen (LCOH) and of NaOH (LCONaOH) were used as performance parameters. The minimum LCONaOH of 0.13 € kgNaOH−1 and LCOH of 2.4 € kgH2−1 were found when bipolar membranes are used as end-membranes and the unit is coupled with onshore wind plant, identifying this configuration as the most promising. Collected results show a great potential for the H-EDBM, thus fully justifying future more in-depth analyses.

Robust fractional-order load frequency control for hybrid power system concerning high wind penetration

Hybrid power systems concerning wind farms have witnessed significant dynamic characteristic changes due to unpredicted generation output and integration mode conversion between frequency-sensitive wind plants and non-scheduled ones. This work presents a robust fractional-order load frequency control (LFC) method for a multi-area hybrid power system in the presence of wind penetration uncertainties. To characterize the injection effect of wind farms, the power system is described by a model with an uncertain wind penetration factor in a pre-estimated range. Then, an enhancement of Levy’s identification method is developed to convert this model into a fractional-order reduced structure to design the load frequency controller, which is in a form of fractional-order PID with a filter for ease of engineering implementation. The closed-loop stability is analyzed by the stability boundary locus (SBL) method under different wind penetration levels. It is shown that superior frequency response performance and system robustness can be obtained if the controller is designed in terms of the model with the worse-case wind penetration. Illustrative examples including a three-area hybrid power system under a variety of wind penetration variations are given to show the effectiveness of the proposed method.

Overview of preparation for the American WAKE ExperimeNt (AWAKEN)

The American WAKE ExperimeNt (AWAKEN) is a multi-institutional field campaign focused on gathering critical observations of wind farm–atmosphere interactions. These interactions are responsible for a large portion of the uncertainty in wind plant modeling tools that are used to represent wind plant performance both prior to construction and during operation and can negatively impact wind energy profitability. The AWAKEN field campaign will provide data for validation, ultimately improving modeling and lowering these uncertainties. The field campaign is designed to address seven testable hypotheses through the analysis of the observations collected by numerous instruments at 13 ground-based locations and on five wind turbines. The location of the field campaign in Northern Oklahoma was chosen to leverage existing observational facilities operated by the U.S. Department of Energy Atmospheric Radiation Measurement program in close proximity to five operating wind plants. The vast majority of the observations from the experiment are publicly available to researchers and industry members worldwide, which the authors hope will advance the state of the science for wind plants and lead to lower cost and increased reliability of wind energy systems.

Renewable energy-driven desalination for sustainable water production in the Middle East

ABSTRACT Despite significant advancement in conventional desalination technologies, their widespread application is still constrained by high energy demands, high capital costs, and the associated greenhouse gas (GHG) emissions. The main objective of this study is to evaluate the potential and challenges of renewable energy desalination in the Middle East. The study also compared the production of energy and water, and emissions reductions of hypothetical 100 MW renewable energy plants in Kuwait, coupled with reverse osmosis (RO) desalination units. The wind-RO plant is estimated to produce 68 million cubic metres on an annual basis, while the CSP-RO and PV-RO plants produced 44 and 37 million cubic metres of fresh water, respectively. These estimates, however, only account for 5% to 9% of the annual fresh water demand in the reference case. Meeting 100% of the fresh water demand would require a 1000 MW to 2000 MW renewable energy capacity. Overall, the intermittent nature of renewable energy sources is a fundamental barrier to the large-scale transition to renewable energies for desalination. The results of this study indicate that the CSP’s relatively small footprint (compared to wind plants) and higher capacity factor make it an ideal compromise among the proposed plants.

Optimizing the bidding strategy and assessing profitability of over-install renewable plants equipped with battery energy storage systems

The over-installation of renewable energy sources (RES) can enhance the profits of RES producers by increasing the total exporting energy; however, RES power curtailments must be applied under over-production of RES plants to ensure their contracted maximum export capacity limits. Battery energy storage systems (BESSs) can be used to reduce the RES curtailments and therefore enhance the profits of producers. This work develops a bidding strategy as a scenario-based stochastic optimization scheme to maximize the expected profits of producers in electricity markets considering battery degradation and maximum export capacity constraints. Using the proposed scheme, a financial analysis is performed to assess the profitability for over-installing hybrid RES plants by calculating the net present value and internal rate of return of the project under different BESS costs. Simulation results, using real data from a wind and photovoltaic plant, demonstrate the effectiveness of the proposed stochastic scheme in enhancing the profit of producers compared to the corresponding deterministic scheme. The performed financial analysis, using the net present value metric, indicates that a maximum over-installation of 150%–170% and 160%–180% for the wind and photovoltaic plant, respectively, is profitable, while the usage of a BESS is profitable when its cost is under 200,000 €/MWh.

Assessing offshore wind plants for energy and green hydrogen production: A case study in the Mostaganem coastal region, Algeria

This study assesses the feasibility and strategic implications of establishing a 500 MW offshore wind farm utilizing fixed wind turbine technology along Algeria’s western coastal economic zone, with a focus on the Mostaganem region. The study recommends integrating 33 Vestas V-235 wind turbines, each boasting a 15 MW capacity to harness offshore wind resources for electricity and green hydrogen production. Utilizing the Wind Atlas Analysis and Application Program (WAsP), offshore wind potential is assessed based on meteorological data from a land-based station, overcoming the lack of in situ sea level measurements. Results show that the proposed wind farm could yield 1361.63 GWh/year of electricity at a cost of 0.89 $/kWh. Additionally, it would produce 28,571 tons of hydrogen at a cost of 2.12 $/kg. This initiative is anticipated to significantly reduce CO2 emissions, by approximately 595,450 tons/year, highlighting the effectiveness of offshore wind resources in promoting sustainable energy production and advancing green hydrogen as a viable energy carrier in this region.

A Solar-Wind based EV Charging Station Model using MATLAB Simulink

Abstract Transportation is becoming more and more necessary every day. One such form of transportation that has been around for a century is the electric vehicle (EV). It has less of an effect on pollution, climate change, and resource depletion because it requires less resources. Many people find it challenging to charge their electric cars in off-grid locations, which lowers the uptake of EVs. In this scenario, a charging station powered by locally available renewable energy sources like solar, wind, biogas, and tidal energy would be a better option. In this paper an attempt has been made to examine a charging station model from renewable energy sources Solar and Wind. The solar-wind energy-based charging system drastically cuts down on the amount of fossil fuels used to generate electricity, which also minimizes emissions of CO2 and carbon-related pollutants. Wind and solar energy are used in conjunction and are complementary to one another. A charging controller which takes into account State of Charge (SOC), the number of linked EVs, and the arrival time of those EVs is used. The power transfer will be managed by this controller. By carefully considering each level, including the solar plant, wind plant, battery storage system, and controlling technology, the charging station is modeled in the best possible way. Simple model is used to describe each stage of the charging station, and designed with appropriate mathematical formulae. By understanding the demand for charging and pairing it with the appropriate energy storage system and auxiliary devices, this charging model can be used in off-grid locations.

Machine Learning Method for Forecasting Wind Power Using Continuous Wind Speed Data

Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient. regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we carried out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.

WebGIS Open-source Platform for Localizations of New P2G Plants in Sicily

The climatic emergency that involves the globe has led targets of greenhouse gas reduction in the EU and all over the world. In this scenario, recent advances in renewable renewable energy sources (RESs) have focused interest on the diffusion of power supplies that are produced by photovoltaic and wind plants. The non-programmable nature of these energy sources has led recent studies to consider the power-to-gas (P2G) solution as an opportunity for employing the curtailed electric energy by converting it into hydrogen. The localizations of P2G plants depend on several factors regarding power production, distances, and population distributions. The necessity of integrating these factors led this work to study the development of a cost function that is hosted by a webbased GIS (geographic information system) platform, thus allowing for the storage, elaboration, and web fruition of an entire data set that is related to the possible new localizations of P2G plants. The structure is based on open-source technology and creates a solution that is easily employable by specialists. The developed platform is composed of different remotely connected blocks that are solely based on open-source technology and is focused the interest on the territory of Sicily (Italy). GIS software, a RDBMS database, a geospatial server (Geoserver ), a Python optimization module, and a WebGIS visualizer are integrated. This work represents a scientific contribution to the management of energy sources, with a particular focus on policies that are based on hydrogen technology. In fact, different data sets that contain several levels of information that are related to the management and the localization of P2G plants will be even further employed in the future.

Time-domain equivalent model for harmonic simulations of wind and photovoltaic plants

Equivalent models of wind power plants or photovoltaic systems reduce the computational burden relative to detailed models (DMs) during power quality studies. However, existing time-domain equivalent models are ineffective for predicting harmonic currents because they do not consider switching behavior from power converters. This paper proposes a novel time-domain equivalent model, the aggregated harmonic model (AHM), to perform harmonic current prediction in large plants. The AHM uses the National Renewable Energy Laboratory method to equalize the collector system and applies the superposition principle to include the dead time and switching effects. Simulations were executed using the DM and AHM of actual plants. Field measurements were performed to validate the AHM. The results show that the proposed model produced well-matched curves for current waveforms, frequency responses, and harmonic spectra. Consequently, this model is an accurate tool for transmission system operators.

Long-term complementary scheduling model of hydro-wind-solar under extreme drought weather conditions using an improved time-varying hedging rule

The frequent occurrence of extreme drought weather poses serious challenges to the complementary scheduling of renewable energy, including uncertain production processes, ineffective scheduling rules, and complex model solving, which further reduces the reliability of the power grid. To address these issues, a long-term complementary scheduling model of hydro-wind-solar (LCMHWS) considering hedging rules is proposed. First, to fully eliminate the uncertainty in monthly variables, Monte Carlo simulation and K-means clustering are sequentially absorbed into the model to process historical sequences and provide deterministic scenarios in which variable characteristics are conformed as input factors. Second, an improved time-varying hedging rule with strong drought resistance capability is proposed to optimize the complementary process of hydro-wind-solar and maximize the objectives of power generation and final energy storage. Last, NSGAII and membership function are adopted to search for the optimal Pareto solution for the uncertain multi-objective model. LCMHWS is validated for 7 hydropower and nearby wind and solar plants on the Lancang River in China. The results indicate that, while meeting the guaranteed output of the system, LCMHWS can increase the power generation and final energy storage by 14 % and 3 % under extreme drought conditions, respectively, and also smoothed the output process of power grid.

Quantification and spatial distribution of the bioenergetic potential from slaughterhouses effluents biogas generation across Brazil

This study describes the estimation of biogas energy use from the anaerobic treatment system of cattle, swine, and poultry slaughter units in Brazil. The simulation includes spatial distribution of the results and geographical comparison with other energy matrices and clean sources of electricity generation. The paper is divided into four sections: 1) the database formation, which included a survey and geocoding of cattle, swine and poultry slaughter units across the country; 2) a literature review covering the period from 2000 to 2020 to obtain the average Chemical Oxygen Demand (COD) for each of the effluents; 3) a simulation of electricity generation in the slaughter units, according to their classes and origins; and, 4) the preparation of national distribution maps of slaughter units, national bioenergy potential, by state and by animal origin, comparison maps of Electric Slaughter Units (ESU) using hydroelectric, photovoltaic and wind power plants, as well as the comparison map between the biomass energy use (biogas, sugarcane bagasse and black liquor). The slaughter units bioenergetic potential presented on the maps that the density in the distribution of these units adopts the effluent treatment proposal. Moreover, biogas is considered a decentralized energy matrix, but the density of slaughter units within the work proposal opposes this concept. The results indicated the formation of a corridor with a high potential for electricity generation, coinciding with regions that experience electrical stress in Brazil. In numbers, the potential would be enough to supply a population of up to 900,000 inhabitants, emphasizing ESU as a potential alternative for electricity using biogas. This is because the Brazilian meat market is somewhat consolidated, important worldwide and continuously generates effluents, which can add up to 28% to electricity from biomass. The greatest bioenergy potential was obtained in the Midwest region (1792 MW), followed by the South (1486 MW), Southeast (1278 MW), North (923 MW) and Northeast (236 MW) regions of the country. In terms of clean energy, when comparing the distribution of slaughter units with photovoltaic, wind and hydroelectric plants, a large territorial gain is observed (more uniform distribution in the country) and in relation to the electrical use of biomass, biogas use from ESU would promote it as the second largest matrix representing about 31% of national energy.

Comparative life cycle assessment of integrated renewable energy-based power systems

Integrated renewable-based power cycles should be employed to produce more sustainable electricity. This is a comparative life cycle assessment (LCA) of three combined power plants, encompassing: case 1 involving combined geothermal and wind, case 2 featuring combined geothermal and solar, and case 3 integrating wind and solar systems. The base case perovskite solar cell (PSC) modelling assumes a 3-year lifespan and a power conversion efficiency of 17 %. However, diverse scenarios are evaluated through a sensitivity assessment involving enhancements in lifetime and efficiency. The base case evaluation emphasizes that the phases with the most significant negative environmental effects which includes the drilling of geothermal wells, construction of wind plants, and manufacturing and installation of PSCs. The midpoint findings indicate that boosting the power conversion efficiency of PSC from 17 % to 35 % yields a notable decrease in environmental impact. Moreover, extending the lifetime from 3 to 15 years led to reduction in CO2 emissions from 0.0373 and 0.0185 kg CO2 eq/kWh to 0.026 and 0.0079 kg CO2 eq/kWh in cases 2 and 3, respectively. Assessing worst and best-case scenarios highlights significant declines in certain impact categories. In case 3, terrestrial ecotoxicity (TE), photochemical oxidant formation (POF), human toxicity (HT), marine ecotoxicity (ME), and marine eutrophication (MU) saw reductions exceeding 88 % compared to worst-case results. The environmental effects observed in cases 2 and 3 stem from toxicity and metal depletion, mainly linked to the PSC. Endpoint results revealed that when considering a PSC lifespan of 10 years or more, the detrimental ecosystem impacts of cases 2 and 3 become less severe than those of case 1. Uncertainty assessment has been done for different cases and impact categories. The study's results are also novel in which it evaluated the innovative PSC technology when integrated with other renewable resources, contrasting it with other integrated plants.

The impact of local wind and spatial conditions on geometry blade of wind turbine

AbstractThe research addresses the pressing need to understand wind conditions for optimal wind energy utilization, a crucial aspect of achieving global energy sustainability. While previous studies have focused on assessing wind energy potential and terrain roughness, gaps persist in understanding how wind and spatial conditions affect blade geometry. Therefore, this study aims to investigate the impact of local wind conditions on wind turbine blade geometry, integrating nonuniform wind inflow due to wind shear into the theoretical model. Through the paper, it explores blade design for varying inflow conditions and analyses the relationship between terrain type and blade designs. A novel approach to wind turbine blade design, utilizing a theoretical model that combines the local momentum theorem with insights from propeller vortex theory was adopted. Through the analysis, the authors demonstrate that variations in terrain significantly influence blade geometry, underscoring the necessity for tailored turbine designs based on local conditions. The findings reveal discrepancies in blade geometry across different turbine capacities and terrain types, offering novel insights into wind turbine performance optimization. By tailoring blade geometry to specific locations enhances resource utilization, offering insights for energy policymakers. The research provides validated methods for optimizing turbine geometric parameters, opening avenues for increased local investment in wind energy and promoting distributed energy development. Illustrated with wind plants examples, the paper concludes by outlining future research directions in this field.

Land-based wind plant wake characterization using dual-Doppler radar measurements at AWAKEN

Wind plant wakes have been shown to persist for tens of kilometers downstream in offshore environments, reducing the power output of neighboring plants, but their behavior on land remains relatively unexplored through observation. This study capitalizes on the unique and extensive field data collected for the American WAKE ExperimeNt (AWAKEN) project underway in northern Oklahoma. X-band dual-Doppler radars deployed at this site measure wind speed and direction at 25-m and 2-min resolution within a 30-km range, capturing the interactions between three neighboring wind plants. These measurements show that the wake of one wind plant extends at least 15 km downstream under easterly wind and stable atmospheric conditions. Though the wake wind speed increases within the first 10 km, it plateaus at 90% of the freestream wind speed. The spanwise velocity distribution within the wake initially shows the clear signature of the wind plant layout, which is smoothed as it propagates downstream, indicating spanwise momentum transfer is a key mechanism in wind plant wake development and recovery. These findings have important implications for wind plant siting decisions and resource assessments, and provide insights into atmospheric interactions at the wind plant scale.