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 article 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 $9 \times 9$ 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.

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.

Power factor (PF) is a measure of how effectively electricity is used. The low PF 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 capacitor and 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 may 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 co-operation with de-tuned filters to mitigate the harmonics. First, a mathematical model has been formulated for the proposed power factor correction system. The 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.

AI-based optimal power management and online control of the storage system of the renewable energy microgrid in conjunction with the main grid that can respond instantaneously to any change in the load demand optimally and economically are the main target of this work. A novel online optimal control methodology depending on crossbreeding between the fuzzy logic and a nature-inspired technique has been proposed to determine the optimal time of carrying out the charging or discharging of the microgrid storage system and the required percentage of both of them in addition to the determination of the source of charging and the destination of discharging dynamically and economically at each time step to meet the varying of the load demand with minimum operating costs. A developed mathematical formula has been introduced to control the dynamic operation of the microgrid’s storage system. It has changed relying on the created fuzzy rules which provides outstanding accuracy for this complicated time-varying process. Furthermore, simultaneous optimal power management between the power generation as well as the storage system of the renewable microgrid and the main grid has been accomplished optimally and economically via a meta-heuristic optimizer named hunger games optimizer. The accuracy of the new methodology has been proved via a fair comparison with the previously published methods after implementing them on the same bases.

Ensuring continuity of service is a primary objective in power systems. In grid-connected microgrids (MGs), islanding poses a significant threat to this continuity. Conventional approaches mitigate islanding by disconnecting the MG immediately after separation from the main grid to prevent overload and ensure safety, but this results in service interruption. This study proposes a dynamic islanding management strategy that maintains uninterrupted service using an optimal dynamic neuro-fuzzy–optical microscope algorithm (OMA). The method integrates a convolutional neural network (CNN), fuzzy logic (FL), and the novel OMA optimizer in a two-stage framework. In the first stage, the CNN detects islanding based on active current and voltage measurements at the point of common coupling (PCC) and their dominant harmonic components, obtained from a hybrid MG model. This model is comprising solar panels, wind turbines, a biomass generator, and a storage system. Signal and image processing techniques prepare the measurements for CNN implementation. Upon islanding detection, the second stage is activated, where FL predicts the penalty factor and OMA optimally manages economic power sharing between the grid and the MG. This integration enables safe load coverage without damaging MG components. Performance benchmarking against Quadratic Interpolation Optimization (QIO) and Hunger Games Optimizer (HGO) demonstrates that OMA achieves higher accuracy, faster convergence, and lower execution time. Validation across five scenarios under normal, islanding, and risky operating conditions confirms the method’s effectiveness, reliability, and economic benefits, achieving a 223.7% revenue improvement over the baseline with the shortest execution time. The proposed approach offers a robust and intelligent solution to the islanding problem, ensuring continuous and cost-effective microgrid operation.