Available Wind Energy Research Articles

Artificial Intelligence-Aided Model Predictive Control for a Grid-Tied Wind-Hydrogen-Fuel Cell System

Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

AbstractFollowing its commitment to Paris Agreement in 2015, China has started to explore potential renewable energy solutions with low carbon emissions to mitigate global warming. Though wind energy is one of the most cost‐effective solutions and has been favored for climate policy development around the world, its high sensitivity to climate change raises some critical issues for the long‐term effectiveness in providing sustainable energy supply. Particularly, how wind speed and its energy potential in China will change in the context of global warming is still not well understood. In this paper, we simulate the near‐surface wind speed over China using the PRECIS regional climate modeling system under different RCP emission scenarios for assessing the possible changes in wind speed and wind energy availability over China throughout the 21st century. Overall, the PRECIS model can reasonably reproduce the mesoscale climatological near‐surface wind speed and directions as documented in reanalysis data across most regions of China, while some local discrepancies are reported in the southwestern regions. In the future, the annual mean wind speed would be decreasing in most regions of China, except for a slightly increase in the southeast. The expected changes in wind speed are characterized with different amplitudes and rates under different RCP emission scenarios. The changes in the spatial distribution of wind speed seem to be sensitive for RCP climate emission scenarios, especially in the late 21st century. The spatiotemporal changes in wind energy potential exhibit a similar behavior to those in near‐surface wind speed, but the magnitudes of these changes are larger. In general, the wind power density is expected to increase by over 5% in winter in the major wind fields in China (ie, Northwest, Northcentral and Northeast), while significant decreases (by about 6% on average) are projected for other seasons (ie, spring, summer and autumn). By contrast, the wind energy potential in the northeast would increase over most months in the year, especially in winter and summer. The results of this research are of great importance for understanding where and to what extent the wind energy can be utilized to contribute renewable energy system development in China in support of its long‐term climate change mitigation commitment.

Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry

The optimal management of a fleet of drones is proposed in this paper for providing connectivity to sensors and actuators in Industrial Internet of Things (IIoT) scenarios. The persistent mission without any human intervention on the battery charge is obtained by means of an on-field wind generator supplying a charge station that adopts resonant wireless power transfer. The objective of the fleet management is to provide the best connectivity over the time considering the variability of both the bandwidth request and the wind energy availability. The optimal management is performed by a system controller adopting reinforcement learning (RL) for deciding the number of drones to take off and, consequently, the instantaneous provided bandwidth. A constant charge time of drone battery represents a key element of the system because this enables to strongly reduce the complexity of the system controller task. To this purpose, an adaptive current control for the charge station is introduced to compensate charge time variabilities due to the coupling factor changes caused by misalignments that can occur between a pad and a drone. The results have highlighted that the RL provides good performance improvement in case of green generation. An important aspect arose from this study is the ability of RL to increase the saved energy even if it is not considered as a target of the controller.

Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems

Smart multi-energy systems based on distributed polygeneration power plants have gained increasing attention for having shown the capability of significant primary energy savings and reduced CO2 pollutant emissions due to the high renewable energy sources penetration. Compared to the traditional power plants, the large variability in the end-user demands (electricity, heat and cold energy), coupled with the uncertainty in the solar and wind energy availability, require the adoption of energy storage systems for dampening the intermittency problems and for performing peak shaving. In a multi-energy system, energy storage technologies typically exist in the form of electrochemical energy and thermal energy storage. Costs and technological limits of energy storage systems are the key parameters that influence the optimal design and operation of the system. In this paper, by adopting an in-house developed simulation tool (©E-OPT) based on mixed integer quadratic programming, a sensitivity analysis has been carried out for investigating the techno-economic impact of different storage technologies (i.e. lithium-ion, Vanadium redox, ice and phase change material thermal storage) for optimally designing and operating a multi-energy system. Two case studies, characterized by electricity and peak cooling demand of 1600 kWe and 3000 kWc respectively, have been assessed and the results of master-planning and optimal dispatch discussed for two scenarios, grid-connected and island mode. The results show that multi-energy systems which include combined heat and power units, solar PVs and energy storage systems lead to achieve more than 1.1 $M and 4.5 $M savings in net present value and 15% and 25% reduction in CO2 emissions, when compared with traditional power plant generation.

Gain Scheduling Proportional Integral for Standalone Wind Energy System

The isolated location in developing countries like India where grid is not readily accessible, the standalone system with renewable or with conventional source is inevitable. Due to the ample availability of solar and wind energy, they are the most suitable and affordable energy sources in the standalone mode. The fixed gain proportional integral controller is usually obtained in such system. These controllers are tuned for specific conditions to obtain the voltage regulation, so optimal tuning of these controller is necessary to acquire desired operation of the overall system. This can be done by scheduling the gain dynamically. This paper focuses on the incorporation of gain scheduling proportional integral controller applied to the standalone wind energy conversion system with change in wind speed conditions. The real data for the wind speeds are taken for the location of Ahmedabad, Gujarat, India to check the performance of wind energy system connected with DC load and analysis is done at average wind speed.

Reducing the impact of wind farms on the electric power system by the use of energy storage

The deployment of wind power is rapidly growing worldwide. Intermittent, unpredictable availability of wind energy destabilizes the work of the whole power system, which causes additional consumption of resources. When fossil fuel power plants are affected by this phenomenon, they are forced to cycle (change their load) more often, which results in higher consumption of fuel. This negative effect, expressed by means of thermo-ecological cost (TEC) can be significant in comparison to the TEC of construction phase of wind turbines. TEC is defined as the cumulative consumption of non-renewable exergy connected with the fabrication of a particular product. Power plant cycling could be minimized by applying an energy storage system responding to variations in wind power availability. In the present work, several scheduling strategies for cooperation of an energy storage system with wind turbines are investigated. The effect is assessed in local and global balance boundaries. Employing energy storage reduces the energy losses in thermal power plants, but at the same time, energy losses appear in the storage itself. However, depending on the strategy of energy storage scheduling, in some cases the overall consumption of primary exergy in the whole system may be lower.

Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level

Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or time series approach. Wind Time Series show non-linearity and non-stationarity properties, and these two properties increase the complexity of the forecasting task using statistical methodologies. In this paper we explore the use of deep learning techniques, which can represent non-linearity, to the wind speed prediction using the largest public wind dataset available, the Wind Toolkit from the National Renewable Laboratory of the US. Several deep network architectures like Multi Layer Perceptrons, Convolutional Networks or Recurrent Networks have been tested on the 126,692 wind-sites and with the results obtained valuable comparisons and conclusions have been obtained. The distribution of the wind sites across the North American Geography has allowed to include in the analysis relationships between terrain, wind forecast complexity and deep methods. With the developed testing workbench and with the availability of the Barcelona Supercomputing Center new architectures are being developed. This work concludes with the feasibility of deep learning architectures for the wind and energy forecasting.

Investigation of wind speed characteristics and its energy potential in Makurdi, north central, Nigeria

This study investigates wind speed characteristics and its energy potential in Makurdi, in order to assess the prospect and availability of wind energy for electricity generation in the region. Wind speed data measured at 10 m were obtained from the Nigeria Meteorology Agency, Lagos. The data spanned from 1961–2014. Weibull distribution function was employed to determine wind energy potential in Makurdi. Results reveal that the period of highest potential for wind harvest was the peak of dry season (i.e. April) and that for lowest yield was the onset of dry season (i.e. November). Wind speed characteristics used for wind energy estimate was determined. Furthermore, high values of Weibull shape and scale parameters obtained in this study indicate the availability of wind speed and the windy nature of Makurdi. The wind power density of 86.85 W/m2 suggests that Makurdi wind can be used for electricity generation. Besides, monthly and annual mean wind speeds were above the minimum values (3.0–4.0 m/s) required for most wind turbines to operate. The wind profile and its characteristics depict Makurdi as a suitable site for wind power generation. Hence, there is huge prospect of wind power generation in Makurdi and its environs.

Ornstein-Uhlenbeck-Lévy Electricity Portfolios with Wind Energy Contracting

To leverage the potential of integrating renewable sources into electricity portfolios the risk and cost trade-off of intermittency needs to be assessed. From the perspective of a Load Serving Entity (LSE), this work present the theoretical implications of energy allocation from two type of markets: bilateral long-term contracts and real-time trading. The purchasing of energy on both markets and from two different sources: wind energy and conventional generation is formulated with a stochastic procurement model (SPM). The unexpected jumps of spot market prices are modeled with a mean-reverting Levy process. The wind energy availability is modeled with multiplicative Brownian motion transformed to a Rayleigh probability density function. The risk assessment is defined by the efficient frontier and a user defined risk level. The SPM is tested numerically. The contracted share of wind power is found to range between 8% and 16%. Moreover, the analysis shows the convergence of SPM to an optimal portfolio irrespectively of the wind farm autocorrelation decay rate.

An Analysis for Repowering Prediction of Jamgodarani Wind Farm Using MATLAB

The development of megawatt class turbines has led to the improvement of wind turbine technology and design. Re-powering in wind energy is a concept to replace and reduce the number of installed old wind turbines of lower capacity by modern turbines of higher capacity. It intends to generate the highest possible constant output power under all wind speed conditions. This research paper portrays economic viewpoints of the repowering procedure for different wind farms. The availability of wind energy in many regions of India encouraged a developing interest in the field of renewable energy and as a consequence this procedure was generated. Before 1998 all the equipments used in relevant research sites were outdated and low powered. But now modern wind turbines with higher efficiency are available to generate more power using considerably reduced infrastructure. This research work proves that repowering is a productive attempt for such old wind farms by using MATLAB (Matrix Laboratory) programming. Wind energy technology is favored due to the benefits of environment friendliness, relativity less installation time and economic competitiveness. A detailed study of necessary performance indices to determine the reliability and performance of a particular wind farm for repowering is done in this research work. An old wind farm located at Jamgodrani Hills, which is in a central part of India, is selected to study the implementation of repowering. This wind farm was commissioned in 1990 with a capacity of approximately 13.05 MW consisting of 58 wind turbines of 225 kW capacity each.

Optimized electrolyzer operation: Employing forecasts of wind energy availability, hydrogen demand, and electricity prices

One of the main advantages of fuel cell based mobility over other sustainable mobility concepts is the flexible production of hydrogen via electrolysis. To date, it is unclear how electrolysis at hydrogen refueling stations should be operated in order to achieve the lowest possible costs despite the constraints of hydrogen demand. This study proposes and evaluates an intelligent operating strategy for electrolysis capable of exploiting times of low electricity prices while participating in the spot market and maximizing wind energy utilization when combined with a wind farm. This strategy is based on a simulation model considering imperfect forecasts (e.g. of wind availability or energy prices) and non-linear electrolyzer behavior. Results show that this approach reduces hydrogen production costs by up to 9.2% and increases wind energy utilization by up to 19%, respectively.

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.

Gain Scheduling Algorithm-Based Control of Renewable Energy Systems for Hybrid Standalone DC Grid

In remote locations in India, where the grid is inaccessible, standalone systems can be employed. Due to the abundant availability of solar and wind energy, they are the preferred energy sources in the standalone systems. A battery backup is mandatory as these sources are largely affected by the environmental conditions. Usually, fixed gain proportional-integral controllers are employed to regulate the output voltage of renewable energy and battery systems. Optimal tuning of these controllers is necessary for the desired operation. However, these controllers can be optimally tuned for a specific set of conditions. In order to maintain the voltage regulation in the standalone system over a wide range and under unpredictable atmospheric conditions, the gains must be scheduled dynamically. This paper proposes a hybrid DC local grid where gain scheduled proportional-integral controllers are implemented for the coordinated control of solar, wind, and battery storage under the different operating conditions. The mathematical model of the hybrid DC local grid along with the control system, incorporating proposed GSPI controller for wind, solar, and battery energy systems, is presented in this paper. The performance of the hybrid DC local grid with the proposed GSPI controller is analyzed for the operating conditions experienced in the city of Ahmadabad, Gujarat, India.

Substitution of coal power plants with renewable energy sources – Shift of the power demand and energy storage

Because of their Global Climate Change contributions, it is desirable to reduce the amount of the global CO2 emissions. One of the ways to accomplish this is the substitution of coal with renewable energy sources, most notably wind and solar. However, the availability of wind energy and of insolation does not follow the diurnal and annual demand patterns of electric power. The large-scale substitution of coal with wind and solar significantly shifts the demand for the rest of the power producing units. When the contribution of wind and solar exceeds approximately 25% of the total annual energy produced, there are time periods within a year when excess electricity is produced that must be wasted/dissipated. This presents a severe constraint for the substitution of coal-generated electricity with renewables. At such production levels diurnal or seasonal storage of energy becomes necessary and hydrogen storage offers the best alternative. Based on the hourly, electricity demand of a region in North Texas, which has very high availability of wind and solar energy and is considered prime region for renewables, extensive calculations are made for: (a) the solar and wind rated power that are necessary for the substitution of part or all the power currently supplied by a coal-fired power plant; and (b) the storage requirements for this substitution. Significant seasonal and diurnal energy storage, on the order of 250,000 m3, is required for the total substitution of coal in the region. The calculations also reveal that the substitution of coal with the renewable energy sources may be optimized for minimum energy storage capacity.

Wind energy potential evaluation for power generation in selected districts of Jharkhand

ABSTRACTThis study evaluated the availability of wind energy for electricity production at different locations, i.e., Ranchi, Jamshedpur, Devghar, Lohardaga, and Chaibasa, in Jharkhand, India. Due to the rapidly rising demand for power in Jharkhand, there is a requirement of an alternative renewable source of energy to lower the dependence on its limited fossil fuel resources. The studied locations were found to be unsuitable for wind to electricity generation on a large scale at 10 m height above the ground. However, small-scale wind turbines can be used to extract energy from low-speed wind, preferably at a height above 10 m from the ground.

Availability estimation of a multi-state wind farm in fuzzy environment

ABSTRACTWind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.

Assessing the Impact of Climate Conditions on Harnessing Coastal (Offshore) Wind Energy

This study analyses the relationship among various climate factors and their effect on the wind power density available in a coastal region. Climate data from several offshore wind farms in the East Asian region is extracted to perform the calculations and analysis. Reasons for variations of climate parameters such as average wind speed, average temperature, air density, average pressure, average precipitation, average humidity and their influences on wind power generation are explored. It is expected that this study will help the investors to perform a feasibility study on the availability of wind energy at a certain nearshore area before setting up wind farms.

Future changes in the climatology of the Great Plains low-level jet derived from fine resolution multi-model simulations

The southerly Great Plains low-level jet (GPLLJ) is one of the most significant circulation features of the central U.S. linking large-scale atmospheric circulation with the regional climate. GPLLJs transport heat and moisture, contribute to thunderstorm and severe weather formation, provide a corridor for the springtime migration of birds and insects, enhance wind energy availability, and disperse air pollution. We assess future changes in GPLLJ frequency using an eight member ensemble of dynamically-downscaled climate simulations for the mid-21st century. Nocturnal GPLLJ frequency is projected to increase in the southern plains in spring and in the central plains in summer, whereas current climatological patterns persist into the future for daytime and cool season GPLLJs. The relationship between future GPLLJ frequency and the extent and strength of anticyclonic airflow over eastern North America varies with season. Most simulations project a westward shift of anticyclonic airflow in summer, but uncertainty is larger for spring with only half of the simulations suggesting a westward expansion. The choice of regional climate model and the driving lateral boundary conditions have a large influence on the projected future changes in GPLLJ frequency and highlight the importance of multi-model ensembles to estimate the uncertainty surrounding the future GPLLJ climatology.

Wind energy location prediction between meteorological stations using ANN

<div> <p>Wind Energy is one of the important sources of renewable energy. There is a need to prepare the availability of wind energy in the area where there is no measured wind speed data. For this type of situation, it seems to be necessary to predict the wind energy potential using such as wind speed using artificial neural network (ANN) method. Soft computing techniques are widely used now days in the study of wind energy potential estimation. In this study the wind energy potential between neighborhood meteorological tower stations is predicted using Artificial Neural Network technique. One of the most suitable areas of Tamil Nadu for wind power generation is some locations in the districts of Tirunelveli, Thoothukudi, Kanyakumari, Theni, Coimbatore, and Dindigul. Along the southeast coastline of Tamil Nadu there are no valleys and mountains besides the mountains are situated away from the sea coast in many regions. Therefore, these regions are exposed to northerly winds that are not as strong as the southerly winds.</p> </div> <p> </p>

Selection of an optimal lattice wind turbine tower for a seismic region based on the Cost of Energy

Over the past few decades, wind has emerged as one of the major sources of green and renewable energy for being a cost effective solution and offering a substantial reduction in greenhouse gas emissions. Currently, onshore wind energy is undergoing a rapid development and expansion at an annual rate of approximately 27%. As such, more and more wind farms are being established in earthquake prone areas resulting from the availability of wind energy in these regions. This paper investigates the optimal selection of lattice wind turbine towers in seismic regions based on the Cost of Energy (COE). Although, various different tower types and materials (e.g., steel and concrete-steel composite monopole) are available, this study focuses on steel lattice towers for providing a cost effective solution and for posing a challenging structural optimization problem. To the knowledge of the authors, design optimization of lattice wind turbine towers subjected to combined earthquake, wind and gravity loading has not been performed before. Ten tower height-wind turbine size combinations are generated by placing turbines with 100 to 400 kW power generation capacity on towers having 24 to 42.6 m height, and a cost optimal solution is obtained for each combination. Taboo search algorithm is used to minimize the total cost. The wind and earthquake loads on the towers are obtained for a specific case study location. The COE for each combination is calculated using three different wind probability distributions. Instead of using simplified structural models, all the details of a rigorous structural analysis, e.g., geometric nonlinearity, are included in the finite element models. Realistic loading conditions, including wind and earthquake, are considered.