In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive Power dispatch method in a Wind-integrated Power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive Power generation and active Power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the Wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive Power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

Open jet Wind tunnel testing provides an alternative solution to expensive online Wind Power measurements for researchers and small Wind turbine developers. However, there is a lack of knowledge for high Reynolds number and large diameter jet flows. In this paper, the structure of circular open jet flows is reviewed. Theories and experimental correlations are combined to extend our understanding of fully developed turbulent jets. Analytical relations are derived to calculate the correct Wind Power of the jets. The corresponding reduction in Power generated by small Wind turbines is explained. The effects of various parameters on the Power production of a small Wind turbine, such as the jet diameter, the distance of the Wind turbine from the jet nozzle and the Wind turbine hub diameter, are investigated and the results are discussed.

In this paper the study and layout of an assembly of Wind turbines in a Wind Farm is presented. The arrangement of two Wind farms as an example in Manjil/Iran and Austria reviewed and due to complexity of the problem, referring to experimental methods, using WAsP or WindPro Computer Codes available from RISOE National Research Laboratories of Denmark and www.emd.dk is advised.

Due to the stochastic nature of Wind energy, allocating an appropriate investment incentive for Wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the Wind generators based on their performance exogenously affected by the Wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, Wind generators are rewarded according to dynamic forecasts of the Wind energy potential of the Wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the Wind speed fluctuations in long-term while capturing the auto-correlation of Wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for Wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

The main methodology in every Wind Power prediction model involves converting Wind speed into Power using the Power output curve of the Wind turbine. However, preceding studies that have introduced models for such curves have not considered the impact of Wind direction and its recurring fluctuations over time on predicting Wind turbine Power output. The main focus of these studies has just been on the magnitude of Wind speed and the relationship between Wind speed and turbine Power. The present study models the effect of Wind direction on Wind turbine Power output and uses it to modify the quadratic Power curve equations. Using these modified equations and considering the turbine mechanism to follow the Wind direction, a method is presented for predicting Wind turbine Power output under frequent changes in Wind direction over time. To deal with the lack of access to long term and high-resolution Wind data, registered historical data and probabilistic distribution functions are used to produce lost data with software. To demonstrate the efficacy of the suggested approach, the real data recorded for a 1.5 MW turbine installed in Khaf in Razavi Khorasan, Iran, are used as a case study. Finally, the potential Wind Power and potential income of the four Windy regions in Iran were assessed based on the payment mechanism of the Organization of Renewable Energy and Electricity Efficiency of Iran, assuming the same installed capacity. The effect of Wind direction and its variations over time, which can affect Power output of Wind turbine and income, is the main focus of this section of paper.

Wind Power has been considered a future alternative to fossil energy resources. However, due to its stochastic nature, the integration of Wind Power plants (WPPs) into Power systems poses some reliability problems such as a mismatch between load profile and efficient Wind Power generation. This issue can be alleviated by considering the correlation between hourly load and Wind speed variations in the planning phase. To this end, a reliability-based Wind Power planning procedure is proposed and formulated as a stochastic programming problem. The objective function is the minimization of total costs, including capital investment, operating and maintenance, and customer energy not served costs. A new hybrid method that combines features of the load-duration curve and the K-means clustering algorithm is proposed to model the uncertainty of the input data. A shuffled frog-leaping algorithm is used to solve the proposed model. The simulation results indicate that the amount of adaptation between hours with high loads and those with high Wind speeds markedly affects the selection of Wind sites as optimal locations for WPP installation. Considering this issue can also improve Power system reliability in the presence of WPPs.

The recent state of electrical system comprises the conventional generating units along with the sources of renewable energy. The suggested article recommends a method for the solution of single and multi-objective optimal Power flow, incorporating Wind energy with traditional coal-based generating stations. In this article, the two thermal Power plants are replaced with the Wind Power plants. The techno-economic analysis are done with this state of electrical system. In proposed work, Weibull probability distribution functions is used for calculating Wind Power output. A non-dominated sorting based multi-objective moth flame optimization technique is used for the optimization issue. The fuzzy decision-making approach is applied for extracting the best compromise solution. The results are authenticated though modified IEEE-30 bus test system, which is combined with Wind and thermal generating plants.

As intermittent Wind Power generation becomes more significant in Power generation, it becomes increasingly important to assess its impact on the generation reliability of Power systems. Therefore, it is the objective of this paper to evaluate the impact of Wind Power on the Power system reliability. In this paper, different approaches of Wind Power modeling are explained. Markov chain Monte Carlo (MCMC) and ARMA method are used to model of Wind Power output. Then Fuzzy-Markov method for Wind Power modeling is proposed. The proposed method is capable of modeling Wind farms that have insufficient Wind speed data. Finally, capacity credit of Wind Power is calculated.