Photovoltaic (PV) panels installation has become one of the major technologies used for energy production worldwide. Knowledge and competitive prices are the main reasons for the spread usage and expanded exploiting of PV systems. Accordingly, this creates several challenges for manufacturers and customers, mainly, the quality of PV panels to withstand environmental conditions during service lifetime. Hence, the quality of PV panels is a vital aspect. By thinking of PV power plants, it appears that some factors should be considered, like the developing microcracks (µcracks). An issue like that increases the chances of having power loss during the operation phase. Notably, µcracks develop in different shapes and orientations; the variation depends on what causes them. This study is a presentation and summary of data collected from different projects in Jordan to describe the effect of each µcracks shape on power loss, aiming to give decision makers an indication to decide whether to replace the faulty panels or not, depending on their own conditions and projects sizes. Hence, in this study, it was found that the µcracks have impacted power loss differently and recorded power reduction of percentages of 0.82–3.21% for poly-crystalline technology. Variation in power degradation depends on the module situation; whether it is stocked in facility or operated on-site. In the mono-crystalline technology case, the power losses varied between 0.55% and 0.9%, with the exception of some samples from both technologies that have effects other than microcracks, which affected power severely. Furthermore, a general overview is provided for µcracks before installation.

This paper investigates utilizing a heterogeneous group of Thermostatically Controlled Loads (TCLs) for long-term demand response applications. The steady-state services are achieved through manipulating the stored thermal-energy with minimal impact on devices’ switching rates and the operating duty-cycles. The Markov chain abstraction method has been developed in literature for aggregating the TCLs at fixed temperature set-point. In this work, an Extended Markov Model (EMM) is proposed to account for the dynamics involved in modifying various set-point magnitudes in both directions. The EMM is formulated online based on linear mapping and fast restructuring to Markov chains developed offline at fixed set-points, where a training process is used to construct each Markov chain. Set-point adjustments force devices to operate in a synchronized pattern, causing the aggregated power to oscillate or traverse extreme conditions. Therefore, model predictive control with direct ON/OFF switching capability is proposed to apply the set-point change sequentially and control devices’ movement toward the new operating set-point. The performance of the proposed modeling and control techniques are compared against existing methods which rely on the direct ON/OFF control solely rather than adjusting the thermal-energy level.

This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (E_D) and the total wind energy (E_T) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m² to nearly 1250 kWh/m². In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.