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This study addresses performances and behaviors of the multi-cross-section HAWT blade design with and without fences. The FX66-S-196 V, FX63-137 S and SG6043, supercritical airfoils were used and distributed along the blade radius; further, the NACA4412 single-cross-section HAWT blade having the same dimensions was used to compare the behaviors and overall performances of all the blades. Analyses were then performed numerically using blade elements momentum BEM theory with a self-code (F.90) and CFD as well as experimentally with three multi-cross-section blades designed using SOLIDWORKS and 3D- printed with polylactic acid. the multi-cross-section HAWT blades show good performance compared with the single- cross-section blade, with an approximately 8% increase in power coefficient, i.e., 40 W, for a miniature wind turbine (127 cm diameter 500 W output power) and greater improvements for wind turbines with large diameters. The fences were designed using boundary layer theory and installed on multi-cross-section blades in experimentally determined positions. The fences showed high performance, with a 16% increase in total power coefficient and high flutter stability.

In view of the increasing levels of renewable generation across the world, the operational dynamics of power systems are changing. One of the dimensions of this transformational change is the nature of fault ride through behavior of renewable technologies such as wind and the associated differences compared to conventional generation. This paper investigates the impacts of fault ride through behavior of wind farms on the system stability in detail, using full dynamic model of a low inertia island power system. Recorded PMU measurements are used to establish the variations in wind farm behavior during and after a fault. A detailed sensitivity analysis on the active and reactive power injection behavior of wind farms is carried out to establish the component of fault ride through behavior that is most critical to system stability, using the full model of the Ireland and Northern Ireland Power system (Irish power system). Detailed investigation of the mechanics of a fault in a low inertia, wind generation dominated system is carried out to reveal potential technical scarcities. Finally, mitigations for identified scarcities arising from non-ideal fault ride through behavior of wind farms are presented in terms of Transmission System Operator led novel system services.

Global installed wind power capacity has risen nearly 4.3 times in the last decade, from 120.7 GW in 2008 to more than 591 GW in 2018. On the other hand, installed wind power capacity in Turkey was reported as 7.37 GW in 2018, and it is scheduled to reach 12 GW in 2023. The aim of this paper is to assess the recent growth of wind power generation in Turkey in terms of power generation technologies, wind power potential, techno-economic feasibility, and visibility of onshore wind turbines. In this respect, several metrics such as cumulative installed wind power capacity (MW), total number of turbines, total swept area of turbines (km2), total hub height of turbines (km), number of the turbine per turbine power capacity (1/GW), swept area per turbine power capacity (m2/MW) and hub height per turbine power capacity (m/MW) are developed to assess wind power generation regionally between the years of 2010 and 2018. Results show that wind power generation capacity is on the rise in Turkey. But this growth also implies an increase in the number and size of turbines. Eventually, turbines with higher hubs and rotor diameters have become more abundant and visible in landscapes.

In this paper, a multi-stage model for expansion co-planning of transmission lines, Battery Energy Storages (BESs), and Wind Farms (WFs) is presented considering resilience against extreme weather events. In addition to High Voltage Alternating Current (HVAC) lines, Multi-Terminal Voltage Source Converter (MTVSC) based High Voltage Direct Current (HVDC) lines are planned to reduce the impact of high-risk events. To evaluate the system resilience against hurricanes, probable hurricane speed (HS) scenarios are generated using Monte Carlo Simulation (MCS). The Fragility Curve (FC) concept is utilized for calculating the failure probability of lines due to extreme hurricanes. Based on each hurricane damage, the probable scenarios are incorporated in the proposed model. Renewable Portfolio Standard (RPS) policy is modeled to integrate high penetration of WFs. To deal with the wind power and load demand uncertainties, a Chronological Time-Period Clustering (CTPC) algorithm is introduced for extracting representative hours in each planning stage. A deep learning approach based on Bi-directional Long Short-Term Memory (B-LSTM) networks is presented to forecast the yearly peak loads. The Mixed-Integer Linear Programming (MILP) formulation of the proposed model is solved using a Benders Decomposition (BD) algorithm. A modified IEEE RTS test system is used to evaluate the proposed model effectiveness.

Improving the performance and the production of renewable energy sources, especially the wind energy, is considered an attractive approach to reduce the Cost of Energy (COE) associated to the hydrogen generation process. In this context, flow control strategies have been object of detailed investigation during last years. Originally flow control devices were designed to be implemented in the aeronautical sector. Nonetheless, the optimization of the performance of the wind turbine blades with the introduction and implementation of these devices is being widely investigated these days. An analysis of the influence of implementing Vortex Generators (VGs) and Gurney Flaps (GFs) in Wind Turbine Blades (WTBs) on the Annual Energy Production (AEP) of large Horizontal Axis Wind Turbine (HAWT) is proposed in this paper. For that purpose, the Weibull distributions of annual wind speed data series of a real wind farm have been calculated, and Blade Element Momentum (BEM) based calculations have been performed to evaluate the effect of the flow control devices on the power curve and the AEP of the NREL offshore 5 MW Baseline Wind Turbine. The obtained results show an enhancement of the AEP of the turbine of 2.43% during year 2015 and 2.68% during year 2016. As a result, increments of the generated hydrogen volume larger than 130000 Nm3 are achieved during both years with no considerable additional cost in the design of the wind turbine.

This paper presents a chaos-opposition-enhanced slime mould algorithm (CO-SMA) to minimize energy (COE) cost for the wind turbines on high-altitude sites. The COE model is established based on rotor radius, rated power, and hub height needed to achieve an optimal design model. In this context, an improved variant of SMA, named CO-SMA, is proposed based on a chaotic search strategy (CSS) and crossover-opposition strategy (COS) to cope with the potential weaknesses classical SMA while dealing with nonlinear tasks. First, the COS is introduced to enhance the diversity of solutions and thus improves the exploratory tendencies. The CSS is incorporated into the basic SMA to improve the exploitative abilities and thus avoids the premature convergence dilemma. The proposed CO-SMA is validated on the design of wind turbines with high-altitude sites. Furthermore, the sensitivity analysis based on the Taguchi method is developed to exhibit the impact of the COE model’s optimized parameters. The influence of uncertainty based on the fuzziness scheme of wind resource statistics is also explored to depict a real scheme for the changes that occurred by seasonal time, atmospheric conditions, and topographic conditions. The proposed CO-SMA is compared with the PSO, WOA, GWO, MDWA, and SMA, where the COE values are recorded as 0.052408, 0.052462, 0.052435, 0.052409, 0.052413, and 0.052915, respectively. Furthermore, the proposed CO-SMA records the faster convergence than the others. On the other hand, the Taguchi method reveals that the rated power is the most significant parameter on the COE model. Also, the impact of the fuzziness scheme on COE is exhibited, where the increasing interval of vagueness can increase the value of COE.

The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved.

Wind turbines and photovoltaic arrays are the most prominent and widely used intermittent Distributed Generations (DGs). Due to the right-of-way, environmental, economical and other restrictions, the connection of this type of DGs to the preferred point of the distribution network is very difficult or in some cases impossible. Therefore, because of non-optimal locations, they may cause a voltage rise at the Points of Common Coupling (PCCs). In this paper, a coordinated design of Switchable Capacitor Banks (SCBs) with dynamic reconfiguration of the distribution network is proposed to avoid low and high voltage violations. For this purpose, distribution network reconfiguration is implemented to mitigate voltage rise at PCCs and Capacitor Banks (CBs) to solve the low voltage problem. A novel method is presented for determining the optimal size of CBs. The proposed Capacitor Sizing Method (CSM) effectively determines the optimal values of reactive power for the given nodes. The optimal locations of SCB are determined using particle swarm optimization algorithm. The 24-hour reactive power curve optimized by the proposed method plays a pivotal role in designing SCBs. The simulation results show that the implementation of the dynamic network reconfiguration and SCBs placement is required to maintain a standard voltage profile for better employment of DG embedded distribution networks.

International audience; This study is a follow up on a previous one carried out within the frame of the French project SMARTEOLE, during which, a ground-based scanning LiDAR measurement campaign was conducted in the onshore wind farm of Sole du Moulin Vieux. That previous study focused on the wakes of two wind turbines that experienced different degrees of interaction depending on the incoming wind direction, through the processing of LiDAR measurements. The measurement duration (7 months) ensured the statistical convergence of the ensemble-averaged flow fields obtained after holding a categorisation process based on the wind speed at hub height, wind direction, and atmospheric stability, where only near-neutral stability conditions were considered. The present study focuses on integrating the operational data of the wind turbines through SCADA processing to complement the LiDAR wake field observations and to be used as an input for analytical wake models. First, the correlation between the atmospheric stability, deduced from MERRA-2 dataset, and the free-stream turbulence intensity, measured by the wind turbines’ anemometers, is studied for different wind speed ranges. It is observed that the turbulence intensity tends towards a consistent value as the atmospheric stability approaches near-neutral stability conditions, giving confidence into the applied strategy of data categorisation based on MERRA-2 outputs. The influence of the degree of wake interaction on the wake added turbulence, the velocity and power deficits between both turbines is assessed. Clear trends between the wake added turbulence and both the velocity and power deficits are detected. Consequently, two fitting laws are proposed. Then, different analytical wake models and wake superposition methods are fed with the operational data deduced from the processed SCADA data, and are used for predicting the evolution of the velocity deficit within the wake. Some statistical metrics are used for error quantification of the different engineering wake models compared to the scanning LiDAR measurements, used as reference, and Blondel and Cathelain produces the closest results to the field measurements.

The use of natural resources in the production of diesel has led to the increase in the prices and main concern is to focus towards the bio energy to reduce the percentage of pollution level in various reigns of the globe. New type of energy resources like from wind energy, nuclear energy and solar been used to make energy efficient fuel which can be recycled easily and can be reused again. Biofuel serve as the best source of energy as they are cheaper in synthesis. To make production of biofuel by transesterification process more efficient choosing a suitable catalyst plays very significant role. Homogenous catalyst is been performed by various authors, it is been observed that the reusability and recyclability is complicated and expensive. Heterogeneous type catalysts provide efficient yielding results and can be reused again. The work towards the Nano catalyst over the production of biofuel has been done to a limited extent. The exploitation of Nano catalysts, at the boundary between homogeneous and heterogeneous catalyst, provides modern efficient methods to develop energy efficient biofuels.

This paper describes the SPOWTT project. The intention of this project was to understand how sailing by crew transfer vessel (CTVs) to offshore wind farms affects the mental and physical wellbeing of individuals on board. The focus was on quantifying this impact, understanding the key drivers, with an aim to ensuring personnel can arrive to the wind turbines in a fit state to work safely and effectively. Impacts looked at subjective state beyond simply vomiting. Key results include the ability now to predict vessel motions from given Metocean conditions and vessel designs. We also discovered that the impact of vessel motions on seasickness is different for different symptoms and is driven not only by vertical z‐axis accelerations but also by certain frequencies of motion in the y‐axis. Frequencies other than 0.16 Hz were found to be impactful, and x‐axis movements appeared to have a longer‐lasting effect on the day's work. Through the formulation of a new, evidence‐based understanding of seasickness, we have created an operational planning tool, designed to have a direct benefit on the safety and productivity of offshore wind farm operations.

In this study, two wind-solar-based polygeneration systems namely CES-1 and CES-2 are developed, modeled, and analyzed thermodynamically. CES-1 hybridizes a heliostat based CSP system with wind turbines while CES-2 integrates heliostat-based CPVT with wind turbines. This study aims to compare the production and thermodynamics performance of two heliostat based concentrated solar power technologies when hybridized with wind turbines. The systems have been modeled to produce, freshwater, hot water, electricity, hydrogen, and cooling with different cycles/subsystems. While the overall objective of the study is to model two polygeneration systems with improved energy and exergy performances, the performances of two solar technologies are compared. The wind turbine system integrated with the comprehensive energy systems will produce 1.14 MW of electricity and it has 72.2% energy and exergy efficiency. Also, based on the same solar energy input, the performance of the heliostat integrated CPVT system (CES-2) is found to be better than that of the CSP based system (CES-1). The polygeneration thermal and exergy efficiencies for the two systems respectively are 48.08% and 31.67% for CES-1; 59.7% and 43.91% for CES-2. Also, the electric power produced by CES-2 is 280 kW higher in comparison to CES-1.

In this article, we propose a model predictive active power control (APC) enhanced by the optimal coordination of the structural loadings of wind turbines (WTs) operating with fully developed wind farm (WF) flows that have extensive interactions with the atmospheric boundary layer. In general, the APC problem, that is, distributing a WF power reference among the operating WTs, does not have a unique solution; this fact can be exploited for structural load alleviation of the individual WTs. Therefore, we formulated a constrained optimization problem to simultaneously minimize the WF power reference tracking errors and the structural load deviations of the WTs from their mean value. The wind power plant is represented by a dynamic 3-D large-eddy simulation model, whereas the predictive controller employs a simplified, computationally inexpensive model to predict the dynamic power and load responses of the turbines that experience turbulent WF flows and wakes. An adjoint approach is an efficient tool used to iteratively compute the gradient of the formulated parameter-varying optimal control problem over a finite prediction horizon. We have discussed the applicability, key features, and computational complexity of the controller by using a WF example consisting of 3x4 turbines with different wake interactions for each row. The performance of the proposed adjoint-based model predictive control for APC was evaluated by measuring power reference tracking errors and the corresponding damage equivalent fatigue loads of the WT towers; we compared our proposed control design with recently published proportional-integral-based APC approaches.

Wind energy technology is based on the ability to capture the energy contained in air motion. Wind power quantifies the rate of this kinetic energy extraction. Wind power is also the rate of kinetic energy flow carried by the moving air. Because the motion is both the source of the energy and the means of its transport, the efficiency of wind power extraction is a balance of slowing down the wind while maintaining a sufficient flow. This chapter quantifies these fundamental concepts and discusses the nature of wind.

This paper proposes a novel modeling approach for optimal sizing of the components of an islanded micro-grid subject to satisfying a reliability index for meeting the loads. The proposed micro-grid incorporates photovoltaic arrays, wind turbines, a battery bank, an inverter, and an electric vehicle (EV) charging station. A demand-side management mechanism based on a deferrable load program is implemented and a model reduction technique is also utilized to mitigate the computational cost. Three different optimization algorithms, namely the whale optimization algorithm (WOA), particle swarm optimization (PSO), and the genetic algorithm (GA) are considered in this study to minimize the total cost of the system. The simulation studies have shown that although the WOA reduces the computational burden and requires much lower iterations compared with PSO and GA, it converges to sub-optimal solutions; therefore, it is not a good option for micro-grid planning purposes. Moreover, the results demonstrate that by charging coordination of EVs and deferring a pre-determined portion of the residential loads, overloading can be avoided and available components can be utilized better, which in turn reduces the sizes of the components and total cost of the system.

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind speed is greater than cut-in speed and power is below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k -means, local outlier factor, combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency, and universality of the proposed method.

International audience

This paper proposes a High Performance Cuckoo Search Algorithm (HPCSA) for determining suitable operation parameters of the optimal wind-hydro-thermal system scheduling (OWHTSS) problem. The objective of the problem is to reach the lowest electricity generation cost of thermal power plants (TPPs) and wind power plants (WPPs) while exactly meeting all constraints of TPPs, WPPs and hydroelectric plants (HEPs). HPCSA is formed by applying improvements on the two main techniques of original Cuckoo Search Algorithm (CSA) to cover CSA’ drawbacks such as searching random solution spaces, always using two random solutions for getting a jumping step and suffering from slow convergence. HPCSA accompany with CSA, Adaptive CSA (ACSA), Snap-Drift CSA (SDCSA) and Water Cycle Algorithm (WCA) are run for solving four test systems in which the largest and complicated system is comprised of four TPPs, four HEPs and two WPPs with the uncertain wind feature. The result comparisons indicate that HPCSA is superior to applied and previous methods, and other modified versions of CSA in the literature in terms of better cost, higher stability, faster search ability and higher success rate. As a result, it leads to a conclusion that HPCSA is a strong metaheuristic algorithm for solving OWHTSS problem.

With the increasing effects of climate change and high costs of energy, many rural Alaska communities are working to implement local alternative energy solutions to improve energy security. Integrating renewable energy systems can reduce reliance on fossil fuels and subsequently improve food, energy, and water (FEW) security. In this study, wind energy modeling techniques using local airport meteorological data were convolved with community loads to determine the most cost-effective combinations of wind turbine technology and dispatchable loads for improving FEW security in a southwestern Alaska village. This approach is different from wind assessments that exclusively analyze wind resources. A 100 kW wind turbine was determined to be suitable for the community, resulting in a capacity factor of 16.7% and levelized cost of energy (LCOE) of $1.15/kWh, with diminishing returns for higher wind turbine capacity. The results from the dispatchability study indicated that dispatchable loads could handle the intermittency of the wind resource with up to 86% of their annual load met. More work is needed to understand the impact of integrating and scheduling dispatchable loads into the grid in practice.

When the number of wind farms increases, the number of wind turbines placed in forested areas will also increase, this is because of the large forest areas in Sweden. When the wind turbines are placed in forested areas the fatigue loads increase drastically, so their maintenance costs increase making them less profitable. In this work clearings in forest are studied to be able to increase the electrical power generated by the wind turbines and decrease the fatigue loads to make them more profitable. The approach in this work is to use Computational Fluid Dynamics (CFD) with the Large Eddy Simulation (LES) turbulence model coupled to an aero elastic solver that solves the dynamic response of the wind turbine. Here Ryningsnas in Sweden is investigated with a homogeneous forest, the current clearing and an extended clearing. The electrical generator power was highest for the current clearing. But the fatigue loads were both higher and lower than the homogeneous forest depending on which part of the wind turbine that was investigated. The extended clearing nearly always had the lowest fatigue loads but unfortunately also the lowest electrical generator power. Further optimization of the clearings and the wind turbine locations in relation to them is needed to find the sweet spot where the fatigue loads are lower and the electrical generator power is higher.

Industrial development, increased demand for energy, limited fossil fuels, and the prevention of environmental damage have led to the use of renewable energy to produce power. The combined system includes renewable wind and solar energy systems or other renewable energy. The hybrid system has been widely used in recent decades to provide energy. In this research, an optimal integrated system has been used to generate electricity. The proposed system includes wind, hydropower, and photovoltaic systems. to estimate the maximum power generation and reduce fluctuations in electricity generation, three proposed algorithms have been used and three common solutions have been applied to solve the optimization problem. The performance of the combined system in different weather conditions has also been assessed. The results showed that among the three proposed algorithms, the model improved the Barnacles Mating Optimizer Algorithm (IBMO) has the best distribution and convergence. Among the three common solutions, a common solution has the most logical solution to the optimization problem. And among the different hydrological conditions, the highest electricity generation is related to the wet season. the most important role in the hybrid system related the hydropower so that despite the reduction of electricity produced in the wind and photovoltaic power plants, the hydropower plant compensates for the drop in production of these systems and causes the production flow to continue.

This paper proposes a fault detection framework for the condition monitoring of wind turbines. The framework models and analyzes the data in supervisory control and data acquisition systems. For log information, each event is mapped to an assembly based on the Reliawind taxonomy. For operation data, recurrent neural networks are applied to model normal behaviors, which can learn the long‐time temporal dependencies between various time series. Based on the estimation results, a two‐stage threshold method is proposed to determine the current operation status. The method evaluates the shift values deviating from the estimated behaviors and their duration time to attenuate the effect of minor fluctuations. The generated results from the framework can help to understand when the turbine deviates from normal operations. The framework is validated with the data from an onshore wind park. The numerical results show that the framework can detect operational risks and reduce false alarms.

Wind speed prediction could play an important role in improving the performance of wind turbine control and condition monitoring. For example, by predicting or forecasting the upcoming wind in advance, fluctuations in wind power output in above rated wind speed could be reduced without causing an increase in pitch activity, and anomalies such as an extreme gust could be detected before it reaches the wind turbine, allowing appropriate control actions to take place to minimise any potential damage that could be incurred by the anomalies. A novel wind speed prediction scheme is presented in this paper that comprises mainly two stages, estimation and prediction. Estimation is first carried out using an Extended Kalman filter, which is designed based on a 3 dimensional wind field model and a nonlinear rotor model. Prediction is subsequently performed in two steps, extrapolation and machine learning. The wind speed prediction scheme is tested using data obtained from a high-fidelity aeroelastic model.

In urban areas, vertical axis wind turbines have been installed on both sides of the road and rooftop. Compared with rooftop, vertical axis wind turbines mounted on the side of a building have a larger swept area with longer blades, and the effect of tip vortices can be reduced at the same time. Around the building, there are several high wind speed regions that can provide more wind energy. In this paper, a high-resolution numerical method composed of overset grid and adaptive mesh refinement techniques is adopted to verify the feasibility of vertical axis wind turbines mounted on the side of the building. Firstly, the complex flow field around the building is simplified into the flow field around the cylindrical building. Five typical flow regions with different flow conditions are determined: windward zone, crosswind zone, tailwind zone, recirculation zone and leeward zone. Secondly, wind turbines are installed evenly around the building. The simulation results show that the energy output of wind turbines in tailwind zone can be increased by 120% compared with the reference value. Thirdly, multiple wind turbines are coupled with the building. In order to get the optimal arrangement, power outputs of four arrangements are compared for the best choice. The results indicate that the arrangement with six wind turbines in crosswind zone and tailwind zone can achieve the best performance. Through changing the rotational directions, the forward blades of wind turbines can be close to the wall, and the energy output of all the wind turbines around the building can be improved.

Recycling of random mechanical energy in the environment has attracted great research interest in recent years. Herein, we report a rotating triboelectric nanogenerator (rTENG) based on GO@PVDF composite film. Benefit from the effective accumulation of electrostatic charges on the PVDF layers, the as-prepared rTENG shows high output performance. Its peak open-circuit voltage ( V o c ) and short-circuit current ( I s c ) are 35 V and 7.5 μ A, respectively, which are 3 and 3.5 times than pure PVDF based rTENG at 600 rpm, and the maximum power density calculated is 15.2 mW/m−2. Based on the as-prepared GO@PVDF-based rTENG, some practical applications are established, for example, lighting 12 led light bulbs to full brightness and sensing wind speed, which demonstrates the high feasibility and efficiency of the rTENG and manifests that the rTENG could be used in reusing random energy.

A novel model reference adaptive system for the rotor position/speed estimation and sensorless operation of a brushless doubly fed reluctance generator with maximum power point tracking is presented. Its main advantage over the existing designs are the inherent robustness and stability afforded by the complete parameter independence of the reference model using only the measured currents of the converter-fed (secondary) winding. In addition, the respective stationary frame current components are estimated by the adaptive model using the voltage and current measurements of the grid-connected (primary) winding at line frequency, offering higher accuracy. The simulation and experimental results have demonstrated the excellent real and reactive power controller performance for typical operating characteristics of wind turbines.

Climate change and natural resource depletion are likely to affect the future economic development of a country. The generation of power from oil and gas is among the major causes of reserves depletion and global warming. However, renewable energy is also deemed a clean and green choice for power generation to promote sustainability in engineering. The coastal lines of the Kingdom of Saudi Arabia (KSA) are widely extended, and wind energy appears to be a viable alternative to traditional sources, which needs to be investigated as it is highly desirable to seek energy from renewable energy sources, for instance, wind. This paper is aimed at addressing the wind energy potential along the Red Sea coast of KSA. Afterward, a suitable wind turbine based upon careful structural analysis has been proposed, which would form a basis, especially during the machine selection and design phases. For this purpose, seven different sites located along the coastal line, namely: Al Wajh, Umluj, Yanbu, Rabigh, Jeddah, Haddad, and Gizan, were initially selected to assess the wind energy availability. After that, a suitable turbine is recommended for yielding maximum output. It has been found from the reconnaissance that Al Wajh has sufficient land availability that receives high perennial wind speed, alongside shallow offshore water depth for monopile installation. Hence, this site is recommended for the development of a wind farm. Furthermore, turbines need to be installed at the height of almost 100 m to produce maximum energy to appropriately utilize the available indigenous wind energy. It is pertinent to mention that the superstructure of the turbines is designed based on the local loading conditions (wind, currents, waves, etc.) of the Al Wajh region. Also, the monopile substructures are proposed in the selected area in accordance with the available bathymetry.

In response to the rapid growth of energy consumption in urban cities, rooftop wind turbines (RWT) have emerged as a powerful technique for providing sustainable energy and strategically minimizing the carbon footprint of buildings. However, significant knowledge gaps exist regarding how RWT respond to the complex urban environmental flows modified by the coexisting buildings and trees. This study conducts idealized numerical experiments using an open-source large-eddy simulation model to investigate the interactions between actuator-disc turbines, street trees and buildings. We found that trees taller than the mean building height modifies the existing roof-level strong shear layer by extracting energy from the mean momentum. A significant change in the mean kinetic energy budget is induced, drastically increasing turbulence production of the flow, which leads to lower power output of RWT. Trees lower than the buildings hardly alter the mean flow field but they reduce turbulence production near the roof level. As a result, improved power output (16%) and decreased normalized power fluctuation (5.2%) are observed compared to the control case without trees. The results highlight that it is important to assess effects of different street tree morphologies on the performance of RWT in their design and implementation processes.

The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmosis (RO) desalination unit. In a case study, the system's performance has been analyzed under the weather conditions of the Eastern Province, Saudi Arabia. A numerical model has been developed based on a mixed-integer linear programming (MILP) approach to design and size the proposed system. The developed model is solved on an hourly basis to capture hourly variations of weather conditions with the aim to obtain an efficient design to operate the RO plant and supply freshwater to a small community living in a remote area at minimum cost. The developed model allows finding the optimal number of wind turbines, the number of photovoltaic (PV) modules, and the energy purchased from the national grid. Since the desalination energy consumption depends on the feed water conditions, two energy consumption rates are considered, namely, 2 and 4 kWh/m3. The results show that brackish water can be purified for the two different energy requirements at a cost varying between 1.72 and 1.84 $/m3, respectively.

The pursuit of economic growth leads to an increase demand for energy that, in turn, may cause negative impacts on the environment due to consumption of non-renewable resources. Thus, it is necessary to search for sustainable strategies for the electric energy's conversion, standing out the distributed generation and the renewable energy sources. In this context, this work presents an intelligent computational tool that combines the concepts of sustainable development and distributed generation to detect locations that are suitable for the installation of renewable energy sources such as wind turbines and solar energy systems. This tool incorporates an intelligent system based on fuzzy logic integrated within a geoprocessing software package, providing an indicative methodology to assist the stakeholders of the energy industry in their studies of energetic planning aiming a sustainable development.

Due to the intermittent, fluctuating and random characteristics of wind system, the output of wind power will become unstable with the change of wind, which brings severe challenges to the safe and stable operation of the power system. An effective way to solve this problem is to accurately forecast the wind speed. This paper presents a novel wind speed combination forecasting model based on decomposition. The innovation of the forecasting model is as follows. (a) In view of the unstable characteristics of wind speed, variational mode decomposition algorithm is introduced to decompose the historical wind speed data to obtain a series of stable components with different frequencies. (b) Echo state network with good forecasting ability is selected as the forecasting model of each component. (c) To solve the problem that the forecasting performance of echo state network is greatly affected by the parameters of the reservoir, an improved whale optimization algorithm is proposed to optimize these parameters. The optimized echo state network improves the forecasting effect. (d) The final forecasting results are obtained by adding the forecasting values of each component. (e) The performance of the developed forecasting model is verified by using two actual collected data sets of ultra-short-term wind speed and short-term wind speed. Compared with some state-of-the-art forecasting models, the comparison result curve between the forecasting value and actual value of wind speed, the forecasting error distribution, the histogram of the forecasting error distribution, the performance indicators, related statistical indicators, and Taylor diagram show that the developed forecasting model has higher prediction accuracy and is able to reflect the change laws of wind speed correctly.

A desirable dispatching strategy is essentially important for securely and economically operating of wind-thermal hybrid distribution systems. Existing dispatch strategies usually assume that wind power has priority of injection. For real-time control, such strategies are simple and easy to realize, but they lack flexibility and incur higher operation and maintenance (O&M) costs. This study analyzed the power dispatching process as a dynamic sequential control problem and established a Markov decision process model to explore the optimal coordinated dispatch strategy for coping with wind and demand disturbance. As a salient feature, the improved dispatch strategy minimizes the long-run expected operation and maintenance costs. To evaluate the model efficiently, a Monte Carlo method and the Q-learning algorithm were employed to the growing computational cost over the state space. Through a specified numerical case, we demonstrated the properties of the coordinated dispatch strategy and used it to address a 24-h real-time dispatching problem. The proposed algorithm shows high efficiency in solving real-time dispatching problems.

This paper presents an analysis and optimization of Airborne Wind Energy Systems (AWESs), designed to maximize the Annual Energy Production (AEP) and, in the second part, the economic profit. A gradient-based optimization algorithm is used to perform the preliminary design of the main AWES sub-systems. A global sensitivity analysis is carried out to study how the design process, represented by the optimization problem, is influenced by aleatory and epistemic uncertainties. In particular, Ground-Gen and Fly-Gen AWESs are studied with a unified model to allow for a quantitative comparison. In the first part of the work, an ideal hybrid AWES design with ground and on-board power generation is considered. With this approach, the common characteristics of Ground-Gen and Fly-Gen AWES designs that maximize AEP are found. In the second part, Ground-Gen and Fly-Gen AWES optimal economic designs are analyzed individually. It is found that a fully developed AWES has strong potential to be highly competitive in the energy market, by providing cheap renewable energy. Fly-Gen AWESs are found to be slightly more profitable than Ground-Gen if the airborne unit is not replaced often. The main physical and economical characteristics of optimal designs are highlighted.

In this paper, a sparse machine learning technique is applied to predict the next-hour wind power. The hourly wind power prediction values within a few future hours can be obtained by meteorological/physical methods, and such values are often broadcast and available for many wind generators. Our model takes into consideration those available forecast values, together with the real-time observations of the past hours, as well as the values in all the power generators in nearby locations. Such a model is consisted of features of high dimensions, and is solved by the sparse technique. We demonstrate our method using the realistic wind power data that belongs to the IEEE 118-bus test system named NREL-118. The modeling result shows that our approach leads to better prediction accuracy comparing to several other competing methods, and our results improves from the broadcast values obtained by meteorological/physical methods. Apart from that, we apply a novel nonparametric density estimation approach to give the probabilistic band of prediction, which is demonstrated by the 25% and 75% confidence interval of the prediction. The coverage rate is compared with that yielded from quantile regression.

Wind solar towers constitute a fairly new scheme for harvesting renewable energy from solar and wind energy sources. In such a tower, solar radiation is collected and hot air is enforced to go fast through the tower, a process called thermal updraft, which fuels a wind turbine to generate power. Using vortex generators at the top of the tower creates a pressure difference, which increases the thermal updraft. In this work, we describe the setup of a wind solar tower system established at Kyushu University in Japan. Then, we demonstrate how data was collected from this system in order to train regression models for thermal updraft prediction. The feature selection process was guided by sensitivity analysis. After that, several machine learning models were investigated and the most suitable model was selected based on quality and time metrics. The linear regression model was particularly examined in detail, and was shown to have a satisfactory high accuracy of thermal updraft prediction graphically and numerically with a coefficient of determination of R2 = 0.981. We also evaluated a reduced prediction model based on the six most essential features, which could be a reduced model description for the WST. This reduced model showed little performance degradation (R2 = 0.974), with significant reduction in the needed effort and resources, as well as data collection requirements.

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

To meet the requirement of the wind energy resource assessment and utilization, the statistical characteristics of wind climate in the China mainland were analyzed in this study by using observation wind dataset for 1971–2017 from the China Meteorological Administration (CMA). A series of key factors closely related to wind power development were analyzed, systematically including the spatial and temporal characteristic of wind speed, the distribution of wind speed at different density grades, the density of the cumulative distribution, strong wind days and weak wind days. The characteristics of wind patterns in different regions are also compares. Results indicate that: (1) the regions with the highest wind speed in China centrally locate in Northwest China, North China and Northeast China with the highest wind speed occurs in spring. The annual variation of wind speed in northern China is bimodal while in southern China it is unimodal. (2) The wind speed probability density distribution shows a significant spatial–temporal difference. The wind speed probability distribution is the most concentrated in spring for almost all regions. For each season, the concentrated and stable wind speed occurs in Xinjiang region in spring. While it appears in the region from Northwest China to the northern part of North China for summer and autumn, and in the southern part of North China and Central China for winter. In the case of equal cumulative probability, the highest wind speed locates in Northeast China and the lowest in Sichuan Basin. (3) For 1971–2017, the wind speed in all regions of China showed a decreasing trend on the whole. But this trend slowed down since 1990s. It is​ worth noting that the wind speed in some regions even increased since 2000. (4) The numbers of strong wind day have been decreasing in all regions, but the numbers of weak wind day demonstrate an evolutionary feature of a growth followed by decrease. In most regions, the numbers of weak wind day reached the maximum in the vicinity of 2000 and then decreased rapidly.

A hybrid system is the most suitable option for supplying the network demand in a renewable resource power plant. In this paper, a hybrid system of fuel cell-wind turbine was assessed based on energy. For a country region with a sustainable wind speed, the system was studied, and its benefits and limitations were evaluated. In this work, based on regional wind data, the specifications of the elements were determined, and a model was simulated, which can assess the power production of a power plant. In the case study region, Kohak Qazvin, by applying the model, wind turbine capacity increased about 2%, and wind turbines and fuel cells contributed 39% and 61% in demand supplying, respectively. Some undesirable environmental effects of renewable energy were evaluated in this paper, including changes in regional ecosystem, thermal pollution production, chemical pollution, and biological pollution due to the use of fuel cell systems and digestive power plants. This paper investigates the environmental effects of using a hybrid wind turbine system, electrolysis system, solid oxide fuel cell and gasifier. The results showed that the hybrid system reduced 34.62% of carbon dioxide production, compared to the single system. Also the price of the electrical energy produced by the wind farm was 20.6 cent/kWh but by applying the hybrid system, the cost reduced to 15.47 cent/kWh.

As the renewable energy sources (RES) production is strongly influenced by multiple geographic factors and highly variable, the need for both energy storage integration and robust real-time power management strategies development is obvious. Wind power represents the largest generating capacity among RES, being at the same time the most fluctuant. The capability to overcome the great disadvantage of wind power variability supports rising its penetration while preserving current operation modes of power systems, so new fashions to achieve this target are of great interest. This paper aims to prove the robustness of a recently introduced power management strategy, able to operate in online conditions, based on simultaneous perturbation stochastic approximation (SPSA) algorithm. To this regard, two different real datasets for wind power profiles with different statistical features are employed. The power management strategy is implemented on a hybrid energy storage system comprising a battery and a flywheel, modeled in Simulink/Matlab. The objectives of the proposed strategy are to reduce the instantaneous power ramp of the profile injected to the grid while smoothening the power profile exchanged by the battery in order to preserve it. Simulations are performed in representative conditions selected on statistical basis. It is demonstrated that the SPSA based power management achieves similar performances in all simulation conditions, proving to be robust. As a performance indicator, the reduction of the power ramp in reference to the 90% CDF threshold is evaluated. It is remarked as an 80% power ramp reduction is obtained towards the grid in both sites. Moreover, the further target is achieved in terms of battery lifetime extension; specifically, the fluctuation of the power profile exchanged by the battery is smoothed by 63% in the first site and 48% in the second, with respect to the flywheel one.

Large-scale atmospheric circulation patterns are the primary drivers of wind power variability on power networks at timescales of hours to days. This paper proposes a methodology that allows power system operators and planners working on networks with high levels of wind generation, to conduct probabilistic power flow (PPF) analyses by defining network ‘operating scenarios’ – i.e. the probability density functions of generators, and correlations between generators representative of a future system state – based on concurrent classified atmospheric states. The most significant contribution made by this paper is in illustrating how PPF operating scenarios derived from clustering historic generation data as a function of a set of classified atmospheric states reduces simulation uncertainty within a PPF analysis. It is anticipated that the proposed methodology may provide network planners with more appropriate operating scenarios for PPF analyses when compared to an unclustered base state, and may assist network operators in converting wind power point-forecasts into probabilistic forecasts whereby the spatial correlations between generators are incorporated. This methodology is illustrated through a case study considering 11 geographically disperse wind generators on the South African transmission network.

For minimizing the operational cost of an integrated hybrid system, which provides electricity, heating, cooling and water of a residential complex, a new energy management method presented here. The proposed energy management method uses the principles of Reinforcement Learning (RL) based on Deep Q Network (DQN) algorithm to find the operational strategy for the hybrid system. Unlike the stochastic presentation of RL with Markov Decision Process (MDP) in literature, a deterministic approach proposed in this study for energy management of a hybrid system. This presented approach does not require online trial and error or historical data of system performance to determine the operational strategy. This developed RL-DQN method used for energy management of a hybrid system with photovoltaic collectors, ET collectors, wind turbines, CHP units, a water heater, a hot water storage tank and batteries. Thus for a residential complex with variable electrical and thermal loads during a year (8760 h), RL-DQN energy management method estimated two unknown variables in operational strategy, i.e., the partial load of the CHP unit and the amount of heat generation by LPG-fueled water heater. The results of RL-DQN energy management also compared with results of two rule-based methods. The results show a reduction of the operating cost to 10983 $/year by RL-DQN which is lower 6.5% and 30.4% than that for two above mentioned rule-based methods. Lower CO2 production (2.8% and 62.8%) also observed in comparison with that for two above mentioned rule based methods. With the proposed EM method, 80.1% of the required electricity supplied with renewable energies and 82.2% of the thermal load supplied without direct consumption of fossil fuels. The benefit of using predicted load and renewable energies of future hours also investigated for EM. Results show that employing the predicted information decrease the operational cost by about 3.5 percent

As decentralized energy supply systems are becoming more and more important for the global energy supply, – apart from a heightened focus on energy rationalization and energy efficiency – increasing the role of variable renewable energy sources, such as solar and wind energy, in energy consumption is gaining more and more significance. Since energy projects tend to be meant for the long term and involve considerable financial investments, it is indispensable to be aware of the country-specific regulatory background when establishing photovoltaic (PV) systems. The last few years have witnessed a trend that new PV power plants are mostly built using traditional crystalline PV technologies, which are prone to irreversible PV module damage due to shading effects. During the operation of PV power plants, anomalies causing loss of income and even fire hazard in extreme cases may occur. Thus, the identification of the problematic parts of the system is of utmost importance. This paper presents the energy relationships of shading by the example of a Hungarian PV system. The goal of this study is to introduce a methodology that can be used internationally to categorize the operational characteristics of the strings of PV power plants on the basis of monitoring data, which allows the assessment of the annual energy loss. The innovative novelty of the model is that its use can provide practical help for the operators of PV systems around the world, since the solution is easy to adapt to real-time supervisory and management platforms and it makes the localization of problematic strings possible, thusly allowing a more focused inspection of PV power plants. The novel practical benefit of the model is that by its use it becomes possible to detect any energy loss resulting from the spacing distance of the strings of PV power plants or faulty operation (the negative shading effects of trees and other objects, faulty inverter operation) by using a simpler calculating mechanism. The early detection of problems is essential for the protection of the PV modules, the subsequent reconstruction of the strings or even solving issues under guarantee. In addition, by assessing the annual energy loss caused by shading, it becomes possible to detect any negative change in the economic indicators of the investment.

Global warming has been a critical issue in recent years. Many leaders and politicians have talked about replacing fossil fuels with Renewable Energy Resources (RES). Some of them even went further and are talking about running a country with 100% RES soon. Is that economically viable? What would be the estimated cost of such a system? In this paper, a city in west Texas with a 100 MW peak load has been assumed to operate with the wind, solar, and Battery Energy Storage System (BESS) totally disconnected from the grid for a cost evaluation. Real generation and load time series data are used to achieve an optimum combination of the installed capacity of those resources by minimizing the total overnight cost, respecting a maximum of 24 h of faults in which the system cannot provide the load during the period. A Monte Carlo simulation is applied to the previous results to evaluate the influence of faulty devices and increase the robustness of the system. The results for both cases are analyzed and compared. Finally, the total cost of install capacity will be compared with other non-renewable resources.

As the wind farm sector grows and becomes an established renewable energy source, it introduces new materials, technologies and processes that expose workers to increased and unique occupational risks. In this paper, we performed a generic review of scientific and industry literature on online scientific databases and search engines to identify the extent to which occupational health hazards and risks specific to wind farms have been considered. Our review revealed noise, electromagnetic fields, shadow flicker, epoxy and styrene and physical stress have been the focus of limited research, mainly including self-reported data from offshore wind farm employees. Factors such as vibration, welding fumes and other possibly harmful substances, weather conditions and biological hazards have not been addressed by studies, although their presence and combinations could be of concern. Therefore, there is a need for further research on unique and combined risks and hazards faced by workers across all lifecycle stages of wind energy production. This would improve knowledge and provide the opportunity to manage health hazards in current and newly constructed installations and inform future regulatory and other preventative measures.

Multi-step forecasting of wind speed time series, especially for day-ahead and longer time horizons, is still a challenging problem in the wind energy sector. In this paper, a novel analog-based methodology to perform multi-step forecasting in univariate time series, named dynamic time scan forecasting (DTSF), is presented. DTSF is a fast time series forecasting methodology for large data sets. Thus, the proposed method is optimal for forecasting renewable energy features such as wind speed, in which standard statistical and soft computing methods present limitations. A scan procedure is applied to identify similar patterns, named best matches, throughout the time series. As opposed to euclidean distance, more flexible similarity functions, using polynomial regression models, are dynamically estimated and Goodness-of-fit statistics are used to find the best matches. The observed values following the best matches and the fitted similarity functions are used to predict k-steps ahead, as well as forecasting intervals. An ensemble version of the method, named eDTSF, combines different predictions using different set of parameters thus, further improving forecasting performance. Remarkably, eDTSF achieved competitive results for multi-step forecasting of wind speed time series, even in situations of very high variability, as compared to eleven selected concurrent forecasting methods.

With the development of smart grid, capacity of wind power that connects to the grid increases gradually, which makes the continuous and stable operation of wind turbine (WT) critically important. Therefore, by considering gearbox structure and operating condition, a diagnosis approach for coupling faults of WT gearbox is proposed based on multitask parallel convolutional neural network with reinforced input (RI-MPCNN). The overall information array of gearbox that fuses wavelet packet transform of vibration signals, domain knowledge of gearbox components and operating condition s used as RI-MPCNN input. Then, RI-MPCNN that has parallel sub-convolutional neural networks (sub-CNNs) and multiple classifiers realizes the diagnosis of coupling faults of multiple components simultaneously. Meanwhile, a reinforced input is added to each sub-CNN to improve the diagnosis accuracy of each component. It is notable that the proposed approach not only fuses the overall gearbox information at system level, but also realizes fault diagnosis at component level. In the approach evaluation based on two case studies, the proposed approach can improve diagnosis accuracies by about 3 and 20% compared with the existing methods, respectively.

Introduction. The thermal calculation of a volumetric structure using the finite element method is considered. According to the plans of the Ministry of Energy of the Russian Federation, a powerful wind energy industry will be created in the country in the coming years. In this regard, calculations in the production of building structures of wind power plants are currently becoming a challenge. The production of such fiberglass structures is a complex thermochemical process, including the polymerization of the binder under strictly specified thermal conditions. The work objective is to develop a method for three-dimensional finite element calculation of the non-stationary heating mode of a complexshaped composite structure.Materials and Methods. The determination of the temperature fields of a complex-shaped structure made of inhomogeneous materials causes using numerical methods and, first of all, the finite element method. The finite element modeling of the behavior of composite materials under molding is still incomplete. For its partial solution, the well-known heat conduction equation is adapted for a specific problem based on the first law of thermodynamics. New finite element models describing the thermal fields in the structure during its manufacture are proposed. The accuracy of modeling thermal processes is specified. Numerical simulation of heating is carried out.Results. The solution to the problem was performed in the multifunctional software complex ANSYS with the implementation of the calculation method in the parametric programming language APDL. The temperature fields of the blade elements of wind power plants at the stage of their manufacture were calculated, which made it possible to identify the characteristic features of the production process of these structures and to obtain recommendations for clarifying the process of their gluing.Discussion and Conclusions. The results obtained can be used in thermal calculations of elements of complex layered structures made of composite materials in wind power, mechanical engineering, aircraft, shipbuilding, instrumentation, etc.

The coupling of mesoscale and microscale simulation methods represents a significant challenge of wind energy research. Conventional approaches used to address this problem often combine Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) equation elements, which has two significant disadvantages. First, Wyngaard’s Terra Incognita challenge cannot be addressed because there is no reflection of the amount of flow resolution in such equations. Second, (because of the lack of theoretical guidance) there is a relatively large number of influence factors that can affect such simulations. The study of implications of such options continues for decades, and (following the same approach) it is likely that this will go on for a long time. Substantial progress in this regard requires a novel simulation concept that provides strong theoretical guideline via solving Wyngaard’s Terra Incognita challenge. The paper shows that the latter can be accomplished by applying new simulation concepts developed for engineering flows to the problem considered. Essential advantages compared to existing mesoscale to microscale couplings are described.

Vertical axis wind turbines can harvest wind energy from every direction, and they are suitable for the complex flow conditions in urban areas. The flow field around buildings consists some high speed regions, and the blockage effect can provide higher wind velocity. Meanwhile, they can be installed at a certain altitude with no interference to pedestrians and vehicles. In this paper, we investigate the characteristics of wind turbines in an array by arranging them between two buildings. For this aim, a high-resolution numerical simulation method is adopted to simulate the accurate flow field and force coefficients. The high-resolution numerical simulation method is composed of adaptive mesh refinement and overset grid techniques. Firstly, there are two array types with wind turbines uniformly arranged in a line, which is perpendicular to the free stream. The result shows that array type A with asymmetric wake achieves a greater mean power coefficient. It reveals that the wake matching phenomenon of array type B causes a loss of wind energy between each couple. Secondly, five column positions between the two buildings are arranged in different positions. The five positions correspond to different flow conditions, and they belong to three typical processes: contraction acceleration process, uniform velocity process, and expansion deceleration process. When mounting array type A in contraction acceleration or expansion deceleration regions, the velocity profile is non-uniform along with the array. The power coefficients of wind turbines in one array are significantly different from each other. The array in the contraction acceleration region reaches the maximum mean power coefficient. Thirdly, in order to evaluate the influence of wind directions in urban area, there are four cases with different wind directions. The simulation results show that the array at α ∞ = 15 ∘ obtains the maximum mean power coefficient. In summary, the mean output power of the wind turbine array in urban areas is always greater than that of a single wind turbine.

This article explores the transition to renewable energy for all purposes in developing countries. Ethiopia is chosen as a case study and is an exemplary of developing countries with comparable climatic and socioeconomic conditions. The techno-economic analysis of the transition is performed with the LUT Energy System Transition model, while the socio-economic aspects are examined in terms of greenhouse gas emissions reduction, improved energy services and job creation. Six scenarios were developed, which examine various policy constraints, such as greenhouse gas emission cost. The Best Policy Scenarios cost less than the Current Policy Scenarios and generate more job. The results of this research show that it is least costing, least greenhouse gas emitting and most job-rich to gradually transition Ethiopia's energy system into one that is dominated by solar PV, complemented by wind energy and hydropower. The modelling outcome reveals that it is not only technically and economically possible to defossilise the Ethiopian energy system, but it is the least cost option with greatest societal welfare. This is a first of its kind study for the Ethiopian energy system from a long-term perspective. Publishers version