Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG’s load frequency control (LFC) and take the microgrid controller’s control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source’s physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe ±25% changes to the system’s parameters guarantee that the proposed tuning strategy keeps the system stable.

The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC.

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

The characteristics of photovoltaic (PV) are directly affected by partial shading (PS) conditions due to the non-uniform irradiance. The PV system can be compromised based on the shading pattern as well as the shading area. Thus, the need for a solution that can deal with non-uniform irradiance has increased significantly. Consequently, this paper proposes a thorough analysis of the impact of PS patterns on different PV array configurations such as SP, TCT, and BL. The five optimization algorithms PSO, DA, MLS-SPA, IGWO, and BWO, were used to tune the variable step of the conventional P&O technique to extract the maximum power point. The proposed PV array is $4\times 4$ with a fixed location, yet changing electrical connections. The main objective and novelty of this paper is to locate the Global Maximum Power Point (GMPP) of a PV array while the occurrence of different PSC with fast change of hybrid load e.g., (resistive and pump load). The results showed the superior performance of the IGWO algorithm regarding the maximum power tracking and disturbance rejection.

Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters’ (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase.