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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.

In fast-growing isolated microgrids (IMGs), load frequency control (LFC) ensures optimal power quality for end users. Stochastic grids, notably with renewable energy resources (RESs), require robust and intelligently designed LFC schemes. Thus, this research presents a novel cascade-loop controller combining a fractional order-proportional derivative with a filter and a fractional order-proportional tilt integral derivative (FPDN-FPTID) to improve LFC for single and multi-area IMGs. Recent dandelion optimization adjusts FPDN-FPTID controller settings. Anti-windup keeps the controller out of the non-linear zone for low inertia IMGs. It concerns various sources' maximum generating rates. The two-area IMG model shows its potential and scalability. Extensive MATLAB/Simulink simulations show that the FPDN-FPTID controller outperforms numerous published controllers, either single or cascade-loop, in minimum error criteria, undershoots/ overshoots/settling times, frequency, and tie-line power deviation following load and RES variations. Finally, the sensitivity study indicates the suggested controller stabilizes the system despite ±25 % parameter changes.

Nuclear power can play an important role to achieve a secure clean energy transition. Output power control is considered an important issue with respect to nuclear power plants (NPPs) especially during load following operation mode. In this study, a scheduled fractional order proportional integral derivative (FOPID) controller is designed in order to track the desired reference power for a nuclear reactor in a NPP. Also, a modified manta ray foraging optimization (MMRFO) algorithm is proposed to tune the five parameters of the FOPID controller. The performance of the proposed algorithm is compared with several optimization techniques using 23 benchmark functions. The comparison shows that MMRFO has the best performance. The simulation of the FOPID controller tuned by the proposed MMRFO algorithm is performed using MATLAB/Simulink and its performance is compared with classic PID controller tuned by the MMRFO algorithm. Two different dynamic simulations during load following operation of a nuclear reactor are carried out. The first case study covers the short time operation of a nuclear reactor and the second case covers the long time operation. The simulation results show an acceptable performance with high degree of accuracy for the proposed FOPID controller tuned by the modified algorithm with very low overshoot, very low steady state error and proper control signal. Also, the stability of the proposed controller is also tested using Lyapunov stability criterion which indicates the stable operation for the proposed controller in the two cases. In addition, a sensitivity analysis has been accomplished which indicates the robustness of the controller.

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.

Commonly Static Synchronous Compensators (STATCOMs) are employed extensively as voltage regulators and VAR compensators in transmission networks in power systems. This research proposes the integration of STATCOMs in distribution networks, particularly in PV grid-connected systems that use distributed energy resources to reduce active and reactive power demand from the grid by supplying variable reactive power from an alternative supply which adapts to the load demand. This decreases the dependence on the utility power supply and promotes the integration of STATCOMs with renewables. Complete case studies between the differences in application of a fixed reactive power compensating condenser and STATCOM for dynamic VAR compensation to loads connected at the point of common coupling in a grid connected Photo-Voltaic (PV) system is presented. The scope of this research is to examine the behavior of dynamic loads, such as an induction motor, at steady state operation when the reactive power demand from the supply increases, as well as the effect of this demand variation on the network that supplies such loads. Complete system modeling and analysis for both scenarios, the fixed reactive power compensator and the STATCOM, supplying various load demands, have been developed. The simulation is based on a 100-kW rated PV grid-connected system to simulate behavior and performance of such study. Adaptive Neuro Fuzzy Inference System with Particle Swarm Optimization was used to extract the maximum power point of the PV array proceeded by a sliding mode controller. MATLAB/ Simulink was developed for modelling and analysis. Various load dynamics with varying solar irradiance and increasing reactive power demand of the associated load were simulated to evaluate various challenges and disturbances on the power supply.

Mathematical design of a new fourth-order harmonic passive filter is presented and its optimal strategy is proposed with regards to various design scenarios. The considered objective function is total demand distortion minimization of the supply current. The crow spiral-based search algorithm (CSSA) is applied to solve the design problem formulated. This CSSA relies on a spiral search mechanism that improves the convergence and balances the exploration and exploitation abilities of the conventional crow search algorithm. Various power quality indices are taken into consideration like the individual and total harmonic voltage distortion values, individual and total current harmonic distortion values, active power loss, actual and displacement power factor percentages, and resonance damping capabilities. These indices were handled as non-linear constraints in the design. Additionally, a comparative investigation of the different damped filters is presented to assess the planned filter performance. The results obtained confirm the success of the filter design proposed in this paper.

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.