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.

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.

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.

This paper presents an intelligent power management strategy for hybrid wind/ fuel cell/ energy storage power generation system. The dynamic models of wind turbine, fuel cell and energy storage have been used for simulation of hybrid power system. In order to design power flow control strategy, a fuzzy logic control has been implemented to manage the power between power sources. The optimal operation of the hybrid power system is a main goal of designing power management strategy. The hybrid power system is simulated in MATLAB/ SIMIULINK environment and different operating conditions have been considered to evaluate the response of power management strategy.

In the realm of wind power generation, cascaded doubly fed induction generators (CDFIG) play a pivotal role. However, the classical proportional integral derivative (PID) controllers used within such systems often struggle with instability and inaccuracies arising from wind variability. This study proposes an enhancement to overcome these limitations by incorporating a single hidden layer neural network (SHLNN) into the wind power conversion systems (WPCS). The SHLNN aims to complement the PID controller by addressing its shortcomings in handling nonlinearities and uncertainties. This integration exploits the adaptive nature and low computational demand of SHLNNs, utilizing historical wind speed and power data to form a more resilient control strategy. Through Matlab/Simulink simulations, this approach is rigorously compared against traditional PID control methods. The results demonstrate a marked improvement in performance, highlighting the SHLNN's capacity to contend with the intrinsic variabilities of wind patterns. This contribution is significant as it offers a sophisticated yet computationally efficient solution to enhance CDFIG-based WPCS, ensuring more stable and accurate energy production.

This study is focused on assessing the effect of energy storage system (ESS) presence on security improvement of power systems hosting remarkable renewable energy resources. To this end, ESS presence is suitably included in security-constrained optimal power flow (SCOPF) model,the required technical amendments are hence considered. To launch a realistic model, ramping constraints of thermal units are also taken into account which limit the generators from completely responding to power shortfalls. Considering a high penetration level of renewable generations, different scenarios of outages in transmission lines and generators are simulated to measure the line outage distribution factor (LODF) and power transfer distribution factor (PTDF). also, in order to illustrate the economic impact of wind power generation curtailment and load shedding, two penalty parameters VWC and VOLL are considered in the model. Two test systems, including a PJM 5-bus system and an IEEE 24-bus RTS, are put under numerical studies to assess the possible impact of ESS on security improvement of the investigated systems. The obtained results are discussed in depth.

This paper presents a comprehensive research endeavor focused on evaluating the influence of renewable energy, particularly wind power, on power quality within the context of Jordan's electrical grid. The escalating global demand for energy, coupled with the imperative to curb greenhouse gas emissions, has propelled the rapid adoption of renewable energy sources. Against this backdrop, the study aims to meticulously analyze the effects of wind energy projects on power quality parameters such as voltage fluctuations, harmonics, and power factor. Through an extensive methodology comprising data collection, rigorous analysis, and advanced simulation techniques, actionable insights are provided into the seamless integration of renewable energy into existing grid infrastructures. In this work, power quality parameters like Total Harmonic Distortion, flickers, power frequency, Crest factor, and voltage unbalance are measured at Al-Tafilah Governorate, Jordan. The significance of this study lies in its contribution to the development of strategies and guidelines essential for policymakers, engineers, and stakeholders. By fostering a deeper understanding of the interplay between renewable energy and power quality, the findings aim to facilitate the establishment of a sustainable and resilient energy system in Jordan. Beyond mitigating climate change and enhancing energy security, this research underscores the pivotal role of renewable energy in ushering in a greener, cleaner future for generations to come.