جستجوی مقالات مرتبط با کلیدواژه « wind farm » در نشریات گروه « فنی و مهندسی »

This paper presents a new approach for short-term forecasting of wind farm power generation using artificial neural networks under Python programming language. In this method, weather conditions such as wind speed, wind direction, temperature and air pressure are selected as key features affecting the power production of the wind farm. To achieve a relatively accurate estimate, the root mean squared error of the predicted values is calculated and minimized as the objective function. The speed and accuracy of the proposed algorithm have been evaluated by conducting a case study on a wind farm located in Manjil, Iran. The power production of the wind power plant is predicted for a time horizon of one week and hour by hour using the wind speed, wind direction, temperature and air pressure during 8592 hours (total hours of a year minus hours of a week). The root mean squared error, the highest relative error percentage, the time resolution of the forecasts and the calculation time of the proposed algorithm are compared with other algorithms published in recent years, which shows the effectiveness and high accuracy of the results in a short calculation time. The power production of the wind farm was predicted hour by hour during a week and 168 data points were obtained, the root mean squared error in the optimal scenario is equal to 0.010817. The calculation time of the forecasting algorithm is less than 1 minute, and the maximum relative error in the proposed method is 2.3%, which demonstrates that the uncertainties associated with the power production of the wind farm can be reduced by using this short-term forecasting approach.

In the current research, a renewable system based on wind, solar, and geothermal energy was analyzed with a targeted combination of fuel cell units, heat pump, proton exchange membrane electrolyzer, steam turbine and reverse osmosis. Transient modeling and simulation of the proposed system was done using TRNSYS software. This system was designed and evaluated to meet the needs of a residential complex with 80 residential units and 320 people for electrical energy, cooling, and heating. A case study was conducted for the feasibility of setting up the proposed system in the coastal city of Bandar Anzali in Iran. Optimization of system performance was done by determining two objective functions of production power and system life cycle cost with response surface method and Design Expert software. Five parameters affecting the system performance including the number of wind turbines, number of solar panels, fuel cell capacity, steam turbine capacity, and proton exchange membrane electrolyzer capacity were selected as optimization variables. In its most optimal state, the system can reach a production power of 3036105.022 kWh and a life cycle cost of 781944.254 $. The environmental results showed that by setting up the system in an optimal mode in Bandar Anzali city and producing electricity at the rate of 3036.1 MW per year, it is possible to help expand 3 hectares of green space per year. It also helped reduce carbon dioxide emissions by 619.36 tons of CO2 at a cost of 14,864.74 $.

Nowadays renewable energy sources, such as wind and solar, whether independently or integrated with other resources, are considered in power system, specifically self-scheduling, bidding and offering strategy problems. However, the uncertain nature of these sources has turned out the greatest challenge for their owners, which makes the bidding and offering in the restructured electricity market more complicated because wind energy generation may cause penalty fees for its generation mismatches. Hence, the primary objective of this paper is to suggest a novel bidding strategy framework based on fuzzy random variable for a wind-thermal system in the electricity market for the first time. The uncertainties associated with day ahead energy, spinning reserve market prices and imbalance prices, are characterized by random fuzzy variables and the uncertainties associated with wind power outputs are modeled as a LR fuzzy numbers. The proposed self-scheduling model maximizes the expected profits while it controls the risk by providing different possibility and probability levels for decision makers.A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. A sensitivity analysis is applied to validate the performance of the proposed model. Numerical results reveal that taking advantage of wind power generation alongside with thermal generation will substantially increase the profitability of the integrated generation company.

The environmental concerns of the use of fossil fuels and the financial interests of governments have raised the necessity of installing renewable power plants in the distribution network. In order to make maximum use of these resources, it is necessary to calculate the hosting capacity of the network. The hosting capacity of the distribution network is the maximum allowed capacity for installing distributed generation in the network, according to operating restrictions. Wind farms are one of the renewable resources used in the power system. The presence of wind farms intensifies the uncertainties of network operation. In this article, the theory of Information Gap Decision has been used to model the uncertainties in the production of wind farms and the amount of hourly load to calculate the hosting capacity of the network.  The strategies of energy management of the network have been taken into account in order to increase the capacity. The network energy management strategies, considered in this article, include static var compensators, network reconfiguration, and power factor control of wind turbines. The correctness and the accuracy of the proposed modeling has been studied on 33 bus IEEE networks.

Increasing energy demand and establishing a balance between production and consumption are important challenges for grid operators. Furthermore, environmental and economic constraints prevent the solution of these problems by conventional methods like the consumption of fossil fuels and the construction of new power plants in proportion to the growth of energy consumption. The hybrid system of Compressed Air Energy Storage and Pumped Hydroelectric (CAESPH) due to advantages such as no requirements for fossil fuels and scalability can prevent the loss of excess energy by storing. Another crucial factor in utilizing this system is power generation control. Due to the novelty of the CAESPH, few studies have examined its performance in conjunction with power generation systems. In this paper, the performance of this energy storage system in the integrated state with wind farm and electricity grid was analyzed and evaluated. For this purpose, the wind data of the selected station were software-modeled, then the comprehensive software modeling system was developed and its performance was analyzed with selected parameters for a cycle. Finally, the system behavior in application with the electricity grid and wind farm under different scenarios was software simulated for one week. According to the results, the Round-trip efficiency of the system is about 49% and this amount is about 7% less in the first cycle than in the next cycles. Also found that this system has good potential for integration with wind farms and electricity grids and with proper design can provide the required power consumption without interruption.

In recent years, The use of renewable energy has grown exponentially due to its many benefits, including no need for fuel and no harm to the environment. However, the use of renewable energy sources in power systems has caused new problems for these systems. In this regard, the study of the dynamic behavior of power systems, including farms or wind turbines, has been the focus of many researchers. In wind farms, one of the types of squirrel cage induction generators (SCIG) or double-fed induction generators (DFIG) is generally used. But both types of induction generators are used in a combined wind farm (CWF). But it should be noted that in order to improve the dynamic behavior of combined wind farms, it is necessary to use compensators in the system. STATCOM (static synchronous compensator) is widely used among the types of compensators. A STATCOM can quickly control the bus voltage connected to it by injecting or absorbing reactive power into the system. In this article, first, a power system connected to a hybrid wind farm is selected, in which a STATCOM is used in one bus. Then, a supplementary PID controller is used in the STATCOM structure to effectively dampen the fluctuations of the system variables in the face of errors. Also, the heuristic particle swarm optimization (PSO) method is used to determine the optimal values of the coefficients of the PID controller. But since determining the optimal coefficients of the PID controller using the PSO algorithm is a time-consuming task, an artificial neural network (ANN) is also used to estimate the controller parameters in time when the working conditions of the system change. The results of the performed simulations are presented and the correctness of the proposed method is confirmed. All simulations are done in MATLAB/Simulink software.

In this paper, first the complete geometry of the Vestas wind turbine has been created, and an unstructured mesh around the blades is produced. Velocity profiles have been extracted by assuming a single wind turbine in the assumed computational area and inlet velocity of 6.7 m/s, and it was found that the maximum reduction in velocity has occurred at the nacelle height approximately. Also, velocity recovery at a distance of 2 to 6 times of rotor’s diameter (2D to 6D) is far greater than the distance of 10 to 20 times of the rotor’s diameter (10D to 20D). The velocity reduction has been recovered in 10D downstream of a wind turbine up to 50% and still exists in 20D downstream. It was shown that the wake effect occurs at a maximum distance of 1D besides the turbine. According to the results, the efficiency of the wind farm is 62.0263% if the distance between the turbines is 5D and the input speed to the computational area is 10 m/s. The calculated velocity profile and efficiency obtained from the assumed wind farm come to the conclusion that the optimal distance for placement of wind turbines is 5D downstream and 1.5D lateral.

In recent decades, pollution has caused many problems such as climate change in Iran as well as the world. By considering the Iran's condition and location in Middle East, and the worldwide oil-based economic pressures, it is mandatory to focus on other industries to change the economic pulse of the country. So, stepping into the field of renewable energy is vital. Since Iran is subjected to numerous environmental contaminants, as well as habitats destruction and the loss of agricultural lands in some areas of the country due to the drought, which leads to migration and suburbia phenomenon, it is inevitable to switch to renewable energies, including solar, wind energy, and develop this industry. This study seeks to evaluate and rank the areas in Iran that have not been identified for solar and wind energy electricity generating despite their high potential. After the initial evaluation of the location by considering factors such as latitude, longitude, wind speed and solar radiation, the locations are prioritized with one of the multi-criteria AHP (Analytic Hierarchy Process) weighting methods based on the solar and wind farms criteria such as return on investment, political and economic impacts, wind intensity, radiation angle, etc.). Finally, the best ranking among the top selected areas including Nimroz-Hamoon (Sistan), Behbahan and Shooshtar (Khuzestan), Kerman and Yazd is determined.

Nowadays, with regard to the reduction of fossil fuels and the management of their use, wind power is now rising as one of the most efficient renewable energy sources. Moreover, wind farms, which include sets of turbines in a vast farm, have become more developed and their designs and optimizations have risen recently. In this paper, the wind turbines are located in the wind farm and the effects of their positions and arrangements on the production power of the whole system are investigated. For this purpose, a square farm is considered and, having information on how the winds and wind speeds in each case (based on the Weibull distribution function), will extract the optimum location of wind turbines in the farm using optimization algorithms. The main goal of this research is to increase the total amount of power extracted from the wind farm based on the changes in the locations and arrangements of the turbines. This optimization problem is subjected to some constraints, such as the maximum number of turbines in the farm, the minimum distance between turbines and the overall size of the farm. To solve this optimization problem, the GA and PSO algorithms are used and the results of these methods are compared. The results of this study indicate that the power produced from all the turbines located in the wind farm has increased compared to previous works.

Due to the increase in wind power production and the increasing development of wind farms its impact on power systems cannot be neglected and modeling of wind farms has been converted to an important topic in power engineering. When studying the effects of a wind farm on the power system, Instead of modeling the wind farm in detail, the performance of the wind farm at the point of the common coupling can be considered as an equivalent model includes an equivalent generator and an equivalent turbine. This approach in addition to simplify the system analysis would considerably reduce the simulations burden. In order to improve the accuracy of this equivalent model the mechanical parts of the wind turbines can be replaced with more than one equivalent turbine using clustering algorithms. Traditionally these algorithms consider the wind speed as the feature for clustering. In this paper it is proposed to classify the wind turbines by power coefficient of each turbine instead of the wind speed. The simulation results obtained using this approach is verified against the conventional approach and also the detailed model. Simulations are done in MATLAB. The results indicate that the accuracy of the equivalent model obtained using proposed approach, is considerably improved.