One of the worst negative phenomena faced by photovoltaic (PV) array is the operation under the shadow phenomenon, which significantly affects the generated power. Multiple local maximum power point (MPP) and unique global MPP are generated from the shaded array. Therefore, regular dispersion of the shadow falling on the PV array surface is a vital issue to extract the GMP via reconfiguration of the shaded modules in the array. This paper proposes a recent approach based on Multi-objective grey wolf optimizer (MOGWO) to reconfigure the shaded PV array optimally. The main objective of the proposed MOGWO is providing the optimal structure for the switching matrix to minimize the row current difference and maximize the output power. The benefits of the proposed strategy is performing a dynamic reconfiguration process which closes to the reality. The proposed method is validated across 9 × 9 PV array with six shade patterns. MOGWO schemes results are compared with TCT and modified SuDoKu based on several statistical metrics. The comparison reveals the superiority of MOGWO in tackling the multi-peak issue in the P-V characteristics with harvesting the highest power levels. INDEX TERMS Multi-objective grey wolf optimizer; grey wolf optimizer; Total-cross-tied; PV reconfiguration, partial shading .

A hybrid microgrid system (HMG) is a new avenue that offers an optimal, reliable, and cost-effective solution for utilizing localized renewable energy resources over individual DC or AC microgrids. Nonetheless, the performance of the HMG varies greatly depending on the availability of renewable resources, desired services to provide, and demand system parameters. These parameters have a high impact on decision-making, reduced costs, and improved system reliability. Therefore, in this work, a reliable and robust developed multi-objective optimizer based on the hunger game search optimizer (HGSO) is proposed to attain HMG scheduling energy management schemes over a long-time horizon of 96 hours under uncertain real-time prices. The proposed strategy's main targets are retaining uninterruptible power to the load with minimal operating costs and minimal emission from the storage systems with achieving a high renewable factor. Moreover, a case study is discussed for including the battery degradation cost in the optimization process. These targets expressed via four objective functions for a HMG include grid-connected with photovoltaic and wind as renewable energy resources, besides battery, fuel cell, and supercapacitor as a storage system. The integrated system has been designed to supply the power demand for different load profiles in Egypt and the United Arab Emirates. The proposed multi-objective hunger game search optimizer (MOHGS) is compared with the recent state-of-the-art optimizers, including multi-objective versions of marine predators algorithm (MOMPA), slime mould algorithm (MOSMA), golden-eagle optimizer (MOGEO), grasshopper optimization algorithm (MOGOA), multi-verse optimizer (MOMVO), antlion optimizer (MOALO), and grey wolf optimizer(MOGWO) to evaluate the performance of the proposed power management system based MOHGS. The scheduled HMG performance is compared with the baseline system to clarify the essential outcomes for the proposed energy management approach. The obtained results confirm the proposed systems' reliability in reducing the power loss, saving the lifetime of the proposed energy storage elements, and minimizing the emissions by 43 % and 34.1 %. Furthermore, the proposed approach saves money for the customers by 184% and 4427% throughout the two studied locations via selling power for the grid compared to the baseline approach. The proposed approach achieves RF values of 86.5% and 94.2%; meanwhile, the baseline approach offers 79.3% and 93.6% for the studied locations, respectively. INDEX TERMS Energy management; Micro grid; Hunger Game Search; Multi-objective Hunger Game Search.

Power factor (PF) is a measure of how effectively electricity is used. The low power factor causes considerable power losses along the power supply chain. In particular, it overloads the distribution system and increases the power plant's burden to compensate the expected power losses. Most of the existing PF correction techniques are developed based on placing centralized capacitors, assuming that power systems are static. However, the power systems are dynamic systems such that their states change over time, necessitating dynamic correction systems. In the emerging smart grid systems, real-time measurements can easily be taken for voltage, current and harmonics. Then, the measured data can be transmitted to a PF controller to reach the desired PF value. However, the problem that will arise in real-time applications is how to determine and adjust the optimal capacitor size that can balance the power factor. In this regard, we propose a real-time correction system based on multi-step capacitor banks to improve PF in cooperation with de-tuned filters to mitigate the harmonics. First, a mathematical model has been formulated for the proposed power factor correction system. The mathematical model can be employed to determine the optimal operational settings of the multi-step capacitor and the reactor value that optimize the reactive power while considering the desired PF value and restricting the harmonics. Second, a genetic optimization approach is applied to solve the proposed mathematical model as it can provide accurate solution in a short computational time. A Monte Carlo simulation approach is considered for validating the proposed PF correction system. The simulation results show that the average PF of the randomly generated test instances has improved from 0.7 to 0.95 (35% increase). Furthermore, we conducted real experiments using a PF testbed for experimental validation. The results are found to be consistent with the simulation results, which validate the effectiveness and applicability of the proposed correction system. Furthermore, the saved kVA in one day is estimated to be 26% of total kVA.

A power system suffers from losses that can cause tragic consequences. Reactive power presence in the power system increases system losses delivered power quality and distorted the voltage. As a result, many studies are concerned with reactive power compensation. The necessity of balancing resistive power generation and absorption throughout a power system gave birth to many devices used for reactive power compensation. Static Var Compensators are hunt devices used for the generation or absorption of reactive power as desired. SVCs provide fast and smooth compensation and power factor correction. In this paper, a Fuzzified Static Var Compensator consists of Thyristor Controlled Reactor (TCR) branch and Thyristor Switched Capacitors branches for reactive power compensation and power factor correction at the load side is presented. The system is simulated using Simulink using a group of blocks and equations for measuring power factor, determining the weightage by which the power factor is improved, determining the firing angle of TCR branch, and capacitor configuration of TSC branches. Furthermore, a hardware prototype is designed and implemented with its associated software; it includes a smart meter build-up for power monitoring, which displays voltage, current, real power, reactive power and power factor and SVC branches with TRIAC as the power switching device. Lastly, static and dynamic loads are used to test the system's capability in providing fast response and compensation. The simulation results illustrated the proposed system's capability and responsiveness in compensating the reactive power and correcting the power factor. It also highlighted the proportional relation between reactive power presence and the increased cost in electricity bills. The proposed smart meter and SVC prototypes proved their capabilities in giving accurate measurement and monitoring and sending the data to the graphical user interface through ZigBee communication and power factor correction. Reactive power presence is an undesired event that affects the equipment and connected consumers of a power system. Therefore, fast and smooth compensation for reactive power became a matter of concern to utility companies, power consumers and manufacturers. Therefore, the use of compensating devices is of much importance as they can increase power capacity, regulate the voltage and improve the power system performance.

the important issue in the extraction of the unknown parameters in the photovoltaic model is the mathematical models. Therefore, a five equivalent circuit is analyzed in this work. The unknown parameters are five for single diode model, seven for double-diode model, eight for modified double-diode, and nine for three-diode model and ten for modified three-diode parameters. The performance of the modified three-diode model and three-diode model after estimate all parameters without assumption for any variable are compared with the performance of the both single diode model, double-diode model and also the modified double-diode of the same solar cell. Cuckoo search optimization algorithm (CSOA) is proposed for the estimation of the unknown parameters for the five solar cell equivalent models. The root mean square error, the absolute error for current and power, and the maximum power point absolute error are analyzed to the performance of the five solar cell models.