Wind Scenarios Research Articles

Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization

Wind farm layout optimization is a challenging issue which demands to discover some trade-off solutions considering various criteria, such as the power generated and the cost of the farm. Due to the complexity of the problem, we developed a reinforcement learning-based multi-objective differential evolution (RLMODE) algorithm to address the issue. In the developed algorithm, RL technique is applied to coordinate the parameter of DE algorithm, which can balance the local and global search. A tournament-based mutation operator is used to accelerate the convergence of the RLMODE algorithm. We tested the performance of the proposed RLMODE in two wind scenarios. The spread and spacing indicators of the algorithm are the best; the power generated by the solution from the RLMODE algorithm is the most when compared with some representative optimization algorithms and existing methods.

Energy systems modeling for maximizing renewable energy share in fossil fuel dominated Mediterranean climate

AbstractIncreasing energy production and consumption and the need to save and increase its efficiency will lead us to manage the energy system on both supply and demand sides with higher accuracy. This study presents a new model scheme for Mediterranean climate areas' energy systems due to the importance of energy modeling to meet energy systems' needs. This model aims to provide an interactive energy system for two short and medium‐term time horizons (2025 and 2030, respectively) by targeting three main objectives: total primary energy supply, greenhouse gas emissions, and system cost. North Khorasan province in northeastern Iran has been selected for the case study as an example of mountainous and Mediterranean climates. EnergyPLAN has been used as a modeling tool. Accordingly, after examining the study area's current state, its future needs are forecasted using the support vector machine algorithm. The study area's optimal energy system is modeled within the desired time horizons by applying and comparing four different scenarios, including business as usual (BAU), power plant optimization, and demand response with wind or solar power plants. The results show that the part of the energy demand provided by wind scenario's situation is favorable and can meet the study area's future demands. This scenario will reduce CO2 emissions in 2025 and 2030 by 93.87 and 227.78 kilotonnes per year, respectively, compared to the BAU scenario. It is also estimated that this scenario's implementation will save the total annual cost of 8.27 and 156.06 million euros compared to the BAU scenario in 2025 and 2030.

Personalized Vulnerability Assessment of Customized Low-Rise Wood-Frame Residential Structures under Hurricane Wind Loads: A Flexible Scenario-Based Simulation Approach

Hurricanes are one of the costliest and deadliest weather and climate disasters that impact the United States, largely affecting the residential sector. Residential structures are essential for the health and safety of the community during a hurricane event. However, homeowners typically have to rely on general information and instructions to understand their level of risks. The knowledge of structural vulnerability to hurricanes at the individual residential building level is often limited, which hinders personalized disaster preparation and mitigation decision-making. To this end, a vulnerability assessment method is proposed for individual homeowners to create customized finite-element models that quantify wind damage of low-rise wood-frame residential structures under various scenarios using ANSYS 2020 R1 parametric design language. A case study was conducted to test the proposed methodology and investigate prevailing failure modes under different structural characteristics and wind scenarios. Specifically, the impacts of roof framing features and previous exposure to environmental conditions on the potential damage of residential buildings under hurricane wind loads were studied. Analysis results, including the percentage of failed sheathing, nailed connections and framing members, are used to inform personalized mitigation recommendations. The proposed customized vulnerability assessment may increase homeowners’ situational awareness of disasters and promote rational retrofitting decisions.

Evaluation of green hydrogen production using solar, wind, and hybrid technologies under various technical and financial scenarios for multi-sites in Egypt

The importance of energy storage is increased with the intermittent nature of renewable energy resources (RERs). Green hydrogen is increasingly being employed to address its shortcomings. Furthermore, lowering carbon emissions has accelerated the quest for green alternatives instead of fossil-based fuels. This research focuses on Egypt's capacity to generate green hydrogen using natural renewable resources to serve islanded electrical and hydrogen loads. To obtain the lowest net present cost (NPC), cost of hydrogen (COH), and levelized cost of energy (LCOE), the state-of-the-art prices of the system elements are investigated and optimized using the HOMER professional software. Three situations with solar photovoltaic (PV), wind, and hybrid RERs are evaluated in three separate Egyptian cities. A sensitivity analysis is investigated based on the influence of the system's reliability and hydrogen load variance on the cost. The results of the three locations are compared to indicate the best site for green hydrogen production. The results obtained with the wind scenario located on the coast of the Suez Gulf provided the lowest LCOE, ranged from 0.308 $/kWh to 0.353 $/kWh, and the COH ranged from 3.73 $/kg to 4.13 $/kg.

Impact of Extreme Wind and Freshwater Runoff on the Salinity Patterns of a Mesotidal Coastal Lagoon

The interaction between tide, river runoff, and wind in coastal lagoons induces complex salinity gradients, which are remarkable when the meteorological forcing is exacerbated. This work aims to characterize the salinity structure under extreme freshwater and wind events in the Ria de Aveiro coastal lagoon (Portugal). The Delft3D model was implemented and validated in 3D mode and used to perform simulations forced with extreme freshwater and wind scenarios. Results show that forcing conditions determine salinity stratification intensity and location. Generally, stratification increases as the freshwater increases, while the salinity intrusion moves downstream. Extreme wind tends to destroy stratification but fails to promote full-depth mixing, which is also dependent on the wind direction, as shown for the Espinheiro channel. The salinity intrusion is also impacted by wind events, being NW storms responsible for an upstream salt transport along the Mira channel and a downstream transport along the Espinheiro channel, and SW storms for an upstream displacement of the salinity intrusion along the São Jacinto channel. Finally, it is observed that the advection of a freshwater plume from the Vouga River into the middle of the São Jacinto channel under high freshwater scenarios causes an unusual local salinity pattern. This plume can either be pushed upstream or prevented from entering the channel, depending on the wind direction.

Rolling Horizon Robust Real-Time Economic Dispatch with Multi-Stage Dynamic Modeling

A multi-stage robust real-time economic dispatch model (MRRTD) for power systems is proposed in this paper. The MRRTD takes the dynamic form of multi-stage robust optimization as the framework to naturally simulate the operation of equipment that is temporally coupled, e.g., utility-level energy storage systems. For normal systems, the MRRTD can work directly in short time slots with a rolling horizon. For large-scale systems, the MRRTD expands the time-slot scale and generates optimal dispatch policies. With this guidance, the real-time dispatch decision can be swiftly made thereafter. In addition, a dynamic uncertainty set based on deep learning is proposed, which can dynamically refine the covering ability for probable occurred wind power scenarios. To efficiently solve the MRRTD, a novel fast robust dual dynamic programming method is employed. The effectiveness of the proposed model and solution algorithm, especially the improved scalability compared to several other dynamic economic dispatch methods, are demonstrated by simulation results from six benchmark test cases ranging from a modified IEEE 6-bus system to a 6495-bus system.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Cooperative Output Regulation of Large-Scale Wind Turbines for Power Reserve Control

This paper develops a distributed control framework to coordinate large-scale wind turbines for fairly sharing the power reserve request on a wind farm. A cooperative output regulation problem is formulated in a multi-agent framework, where the synchronization of turbine system outputs are established regarding the heterogeneity of turbine models. For this purpose, a homogeneous virtual system is created first and embedded into each turbine to generate the agreed trajectories across the wind farm in a distributed manner. The local power tracking of individual turbines is then enforced by the synthesized output regulator, whose control gains are determined by a variant of the Sylvester equation. The proposed approach issues the generator torque and blade pitch commands in a unified manner and is resistant to wind disturbances. The case studies are carried out through simulations and in a real laboratory-scale distributed communication network. The simulation results demonstrate the effectiveness of coordinated actions of wind turbines in response to the changing demands, under both constant and turbulent wind scenarios with the plug-and-play capability.

What changes can solar and wind power bring to the electrification of China compared with coal electricity: From a cost-oriented life cycle impact perspective

For carbon neutralization, coal and oil need to be replaced with electricity, and this project is deeply dependent on the development of clean energy. Therefore, this study compared the environmental performance and cost of different substitution technologies, including coal, solar, and wind power, to provide useful information for the electrification of China. A cost-oriented life cycle assessment method and driving force analysis were conducted. Results showed that the solar and wind power scenarios showed low burdens with 35.3%–39.9% and 22.3%–42.6% of coal power, respectively. The main reason was that solar and wind power reduced CO2, particulates, and mercury emissions as well as coal consumption, which led to an improvement in human health and resources damage. However, the higher freshwater ecotoxicity, which was mainly derived from heavy metal emission in the supply chain, in the solar power scenarios than coal power should receive attention, as well as water depletion caused by solar wafer and metal electrode production. Meanwhile, metal depletion in wind power scenarios cannot be ignored. Adopting monocrystalline silicon and high-power wind turbines was beneficial to clean electrification. The driving factors analysis indicated that in addition to improving those environment-sensitive processes (e.g., wafer, metal, and glass fiber), coordinating clean energy development and electrification; reducing costs, especially by ensuring operational reliability; and expanding investment scale are crucial for ensuring the effectiveness of policies on carbon neutralization.

A low-complexity evolutionary algorithm for wind farm layout optimization

The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy algorithms are efficient in finding the optimal layout but their exploration ability is poor. In contrast, evolutionary algorithms own better global search ability but require huge computational effort to find the optimal layout. Considering that all wind turbines are deployed in the same flat area (i.e. the coordinates of each wind turbine are of dimension 2, and each dimension of all turbines has the same search range), a low-complexity grey wolf optimization technique using a 2-D encoding mechanism (GWOEM) is proposed in this work. which is low-complexity and accelerates search efficiency. The proposed GWOEM effectively combines the advantages of the greedy algorithms and the evolutionary algorithms, and improves the search efficiency while maintaining accuracy. To validate the performance of the proposed GWOEM, a comparative analysis is performed based on two wind scenarios. Simulation results show that the proposed GWOEM is competitive with other state-of-the-art techniques. Compared with TDA, ADE, ADE-GRNN, and DEEM, the average power output in the two wind scenarios considered herein increases by 44.41%, 34.45%, 34.97%, and 37.06%, respectively. In terms of execution time, GWOEM is shorter than TDA, ADE, and DEEM except for ADEGRNN which increases by 12.18%.

Study of cooperative wake control for multiple wind turbines under variable wind speeds/directions

In the wind farm control field, wind turbines are normally manipulated to maximize the individual power production which is named the greedy control. However, this greedy control method can lead to massive losses of total wind farm power production, mainly caused by the wake interference between multiple wind turbines. To this end, the cooperative wake control, which seeks the maximum total power production by coordinating each individual wind turbine at the global optimum operation point, can greatly improve the wind farm output performance. In this paper, we investigate the effectiveness of two different cooperative wake control strategies, i.e., instantaneous control and wind-interval based (WIB) control under variable wind speeds/directions scenario. These two cooperative control strategies are achieved based on the power de-rating operation to the upstream wind turbines. Taking three in-line wind turbines as an example, the control parameters of the two upstream wind turbines are cooperatively optimized while the downstream third wind turbine operates at the maximum power coefficient. To account for the multiple wind turbines wake interference, an artificial neural network (ANN) wake model characterized by the fast computational efficiency and great accuracy, in combination with the best wake superposition model chosen to quantify multiple wake effect, is proposed for the control optimization. By comparing to the baseline greedy control, it shows that both cooperative control strategies are effective in improving the power production of the wind farm. More specifically, the WIB control can maintain the power production at the same level of instantaneous control with a maximum difference less than 3%, while it reduces the operating difficulty to a large extent which greatly facilitates its application under realistic more complex wind scenarios.

Improved model-free adaptive control considering wind speed and platform motion for floating offshore wind turbines

To cope with the problems of cumbersome modeling and strongly disturbances in the control of floating offshore wind turbines (FOWTs), an improved model-free adaptive control (MFAC) strategy considering both wind speed disturbance and floating platform motion response to wave disturbance is proposed. In this strategy, a detailed mathematical model of the FOWT is avoided and the feedback controller is designed based on improved MFAC using only the input–output data to dynamically linearize the FOWT. Strongly disturbances caused by random wind and floating platform motion response to wave are compensated by a feedforward controller. The proposed method and the comparison method including the baseline controller are simulated under different wind scenarios using FAST. The results show that the proposed controller regulates the generator speed more smoothly and closer to the rated value, suppresses the motion of the floating platform more effectively, and can significantly improve the performance of FOWTs.

Unveiling Properties of the Nonthermal X-Ray Production in the Gamma-Ray Binary LS 5039 Using the Long-term Pattern of Its Fast X-Ray Variability

Gamma-ray binary systems, a subclass of high-mass X-ray binaries, show nonthermal emissions from radio to TeV. While efficient electron acceleration is considered to take place in them, the nature of the acceleration mechanism and the physical environments in these systems have been a long-standing question. In this work, we report on long-term recurrent patterns in the short-term variability of the soft X-ray emission of LS 5039, one of the brightest gamma-ray binary systems. The Neutron star Interior Composition Explorer (NICER) observed LS 5039 four times from 2018 to 2021. By comparing them with the previous Suzaku and NuSTAR long-exposure observations, we studied the long-term evolution of the orbital light curve in the soft X-ray band. Although the observations by NICER and Suzaku are separated by ∼14 yr, i.e., more than 103 orbits, the orbital light curves show remarkable consistency after calculating their running averages with a window width ≳70 ks. Furthermore, all of the light curves show short-term variability with a timescale of ∼10 ks. Since the column density did not vary when the flux changed abruptly, such a short-term variability seems to be an intrinsic feature of the X-ray emission. We propose that the short-term variability is caused by clumps (or inhomogeneities) of the companion star wind impacting the X-ray production site. The observed timescale matches well with the lifetime of the clumps interacting with the pulsar wind and the dynamical timescale of the relativistic intrabinary shock in the pulsar wind scenario.

A Design of the Real-Time Simulation for Wind Turbine Modeling with Machine Learning

Power system operators have recently introduced some AI-based techniques in load prediction, fault diagnosis, scheduling, and maintenance. Operators require a grid analysis that includes wind turbines to mitigate impacts by environmental factors. Among them, a method for modeling wind turbines that reflects the dynamic characteristics and their output characteristics is receiving attention. Recently, a data-based power curve modeling method has been adopted for a simplified model that characteristics as much as possible. However, the simplified EMT simulation is difficult to reflect the output characteristics according to nonlinear wind conditions accurately. This paper proposes a wind turbine model based on artificial neural network techniques using real supervisory control and data acquisition (SCADA) data from a wind farm. The proposed strategy derive the similar to real output value through the trained wind turbine model in various wind scenarios. For the verification of the proposed strategy, the case study was conducted using a real-time digital simulator (RTDS).

A data analytics framework for anomaly detection in flight operations

In the air transport system, there has been a continuous effort to develop policies, tools, and methodologies that increase and standardize safety levels across the entire commercial aviation market, while also enhancing operational efficiency. Furthermore, there is a current focus on proactive approaches for aviation performance management. Within this context, data mining initiatives such as anomaly detection have become more prominent. Dealing with anomalies is a natural step for achieving the goals regarding operational safety and efficiency, as anomalies are often related to hazardous and inefficient operations. In this work, we propose a systematic flight data analytics framework for anomaly detection in flight operations in order to provide a comprehensive and reusable pipeline for model building, application, and explanation. The solution is designed to be applicable to both online and offline regimes and at multiple scales, while also building on domain-expert analysis. We demonstrate the framework applicability in two scenarios of routine flight operations monitoring considering both airline and air traffic management perspectives. In the first one, the framework is applied to aircraft performance data within an unsupervised learning setting with a density-based clustering approach for anomaly detection in landing operations at Minneapolis–Saint Paul International Airport (KMSP). The results are compared with those obtained with exceedance-based methods used in the current practice, revealing the detection of operationally significant anomalies beyond the benchmark. In the second case study, we apply the framework on flight tracking data within a supervised learning setting with the development of an autoencoder classifier for offline anomaly detection in terminal airspace arrival operations at Sao Paulo/Guarulhos International Airport (SBGR). Additionally, supervised learning models are developed for anomaly explanation. The autoencoder classifier was able to detect operationally significant anomalies, while the explanatory models provided novel insights about contributing factors to the anomalies identified. For instance, we learned that anomalous arrival trajectories are more likely to be associated with landing operations on runway 27 under wind scenarios, with an increase in the odds ratio of 62% and 58% for tailwinds and headwinds, respectively. In addition, we also observed a positive association between anomalies and wind gusts situations.

Wind dynamic and energy-efficiency path planning for unmanned aerial vehicles in the lower-level airspace and urban air mobility context

Unmanned aerial vehicles (UAVs) have been extensively used in urban environments for logistics, parcel delivery and surveillance, and the development of air taxi services. Given the dynamic nature of urban air mobility in terms of decision-making time limit, wind dynamics and other external factors, one should consider their safe and efficient operations in an urban context. Therefore, we propose an energy-efficient path-planning model for UAVs under large and complex urban environments and wind dynamics. The proposed method adopted Voronoi Diagram to decompose the complex urban environment into a simplified network model, given the presence of no-fly zones and restricted areas as obstacles. One could obtain the feasible initial path by solving the network model using the Dijkstra shortest path algorithm concerning the distance matrix. Given the nonlinearity of energy consumption along a path. We further model the UAV energy consumption and propose an efficient particle swarm optimisation (PSO) metaheuristic algorithm to achieve better solution quality. Compared to traditional PSO, the proposed algorithm achieved a 5% to 22% improvement under different wind scenarios by simulating real-life situations. In conclusion, the proposed method can achieve safe UAV operations with sufficient separation and less energy consumption.

A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study

The issue of renewable energy curtailment poses a crucial challenge to its effective utilization. To address this challenge, mitigating the impact of the intermittency and volatility of wind and solar energy is essential. In this context, this paper employs scenario analysis to examine the complementary features of wind and solar hybrid systems. Firstly, the study defines two types of complementary indicators that distinguish between output smoothing and source-load matching. Secondly, a novel method for generating wind and solar output scenarios based on improved Generative Adversarial Networks is presented and compared against the conventional Monte Carlo and Copula function methods. Lastly, the generated wind and solar scenarios are employed to furnish complementary features. The testing results across eight regions indicate the proposed scenario generation method proficiently depicts the historical relevance as well as future uncertainties. This study found that compared to the Copula function method, the root mean square error of the generated data was reduced by 4% and 3.4% for independent and hybrid systems, respectively. Moreover, combining these two resources in most regions showed that the total output smoothness and source-load matching level cannot be enhanced simultaneously. This research will serve as a valuable point of reference for planning and optimizing hybrid systems in China.

Vertical Axis Wind Turbine Layout Optimization

Vertical Axis Wind Turbines (VAWTs) are not mature enough yet for offshore wind farms, but they offer benefits compared to conventional Horizontal Axis Wind Turbines (HAWTs). Higher power densities, reduced wakes, lower center of mass, and different power and thrust curves make VAWTs an interesting option to complement existing wind farms. The optimization of wind farm layouts—finding the optimal positions of wind turbines in a park—has proven crucial to extract more energy from conventional wind farms. In this study, we build an optimizer for VAWTs that can consider arbitrarily shaped layouts as well as obstacles in the area. We adapt a recent model for the wakes of VAWTs considering a Troposkien design. We can then model and optimize a large VAWT park in a real wind scenario and assess for the first time its performance operating Troposkien VAWTs. In addition, we present a novel model for wind farm optimization that considers the clockwise and counterclockwise rotation of turbines. This optimization exploits the asymmetric wakes of VAWTs, thus increasing the total energy production. We benchmark our optimization on realistic instances and compare VAWTs and HAWTs wind farm layouts, showing that VAWTs can achieve higher density and power production than HAWTs in the same area. Finally, the wake loss reduction is compared to the literature.

A Unified Formulation for the Computation of the Six-Degrees-of-Freedom-Motion-Induced Errors in Floating Doppler Wind LiDARs

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating ZephIRTM 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed.

Numerical Study on the Effect of Atmospheric Wind on the Fire Severity in Informal Settlements with Different Dwellings’ Wall Thermal Characteristics

AbstractThere is a persistent risk of large-scale fire conflagrations in informal settlements, which can threaten hundreds of people simultaneously. Although the literature implies that wind conditions have a significant impact on these fires, little is known about how wind conditions affect the dynamics and spread of flames in informal settlements. In order to comprehend the impact of wind conditions (speed and direction) on the time to flashover and fire severity in informal settlement dwellings with different wall thermal characteristics, a numerical study was conducted utilizing the Fire Dynamics Simulator (FDS), a Computational Fluid Dynamics (CFD) code. For six different wind speeds (1 m/s, 5 m/s, 10 m/s, 15 m/s, 20 m/s and 25 m/s) and two wind directions (side and back wind). Simulations were conducted with full-scale informal settlement dwellings burning wood cribs, analyzing the fuel mass loss rate, hot gas temperature, global equivalence ratio, radiative heat flux outside the door, and time to flashover. In addition, the influence of wall thermal properties was examined for thermally-thin steel-clad and asbestos cement-clad dwellings (thermally-thick). Regardless of wind direction, it was noticed that an increase in wind speed significantly shortened the time required to attain flashover. This was shown to be the result of the wind accelerating the burning rate of the wood cribs and, as a result, the faster temperature rise of the hot gas. Radiative heat fluxes observed outside the door increased with the wind speeds. The direction of the wind had a small effect on the investigated fire characteristics, with the side wind scenarios exhibiting somewhat longer timeframes to flashover. Thermally-thin walled informal settlement dwellings exhibited a greater fire severity, with higher fuel mass loss rates, hot gas layer temperatures, and higher external radiant heat fluxes, as well as shorter timeframes to flashover. These findings indicate that both wind speed and thermal wall characteristics have a substantial impact on the severity of fires in informal settlements and can enhance the risk of fire spread.