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In light of the challenges of integrating more renewable energy sources (RESs) into the utility grid, the virtual synchronous generator (VSG) will become an indispensable configuration of modern power systems. RESs are gradually replacing the conventional synchronous generators that are responsible for supplying the utility grid with the inertia damping properties, thus renewable power grids are more vulnerable to disruption than traditional power grids. Therefore, the VSG is presented to mimic the behavior of a real synchronous generator in the power grid through the virtual rotor concept (i.e., which emulates the properties of inertia and damping) and virtual primary and secondary controls (i.e., which emulate the conventional frequency control loops). However, inadequate imitation of the inertia power owing to the low and short-term power of the energy storage systems (ESSs) may cause system instability and fail dramatically. To overcome this issue, this paper proposes a VSG based on superconducting magnetic energy storage (SMES) technology to emulate the needed inertia power in a short time and thus stabilizing the system frequency at different disturbances. The proposed VSG based on SMES is applied to improve the frequency stability of a real hybrid power grid, Egyptian Power System (EPS), with high renewables penetration levels, nonlinearities, and uncertainties. The performance superiority of the proposed VSG-based SMES is validated by comparing it with the traditional VSG approach based on battery ESSs. The simulation results demonstrated that the proposed VSG based on the SMES system could significantly promote ultra-low-inertia renewable power systems for several contingencies.

There is a continuous and fast increase in electric vehicles (EVs) adoption in many countries due to the reduction of EVs prices, governments’ incentives and subsidies on EVs, the need for energy independence, and environmental issues. It is expected that EVs will dominate the private cars market in the coming years. These EVs charge their batteries from the power grid and may cause severe effects if not managed properly. On the other hand, they can provide many benefits to the power grid and get revenues for EV owners if managed properly. The main contribution of the article is to provide a review of potential negative impacts of EVs charging on electric power systems mainly due to uncontrolled charging and how through controlled charging and discharging those impacts can be reduced and become even positive impacts. The impacts of uncontrolled EVs charging on the increase of peak demand, voltage deviation from the acceptable limits, phase unbalance due to the single-phase chargers, harmonics distortion, overloading of the power system equipment, and increase of power losses are presented. Furthermore, a review of the positive impacts of controlled EVs charging and discharging, and the electrical services that it can provide like frequency regulation, voltage regulation and reactive power compensation, congestion management, and improving power quality are presented. Moreover, a few promising research topics that need more investigation in future research are briefly discussed. Furthermore, the concepts and general background of EVs, EVs market, EV charging technology, the charging methods are presented.

With high penetration of renewable energy sources (RESs) in modern power systems, system frequency becomes more prone to fluctuation as RESs do not naturally have inertial properties. A conventional energy storage system (ESS) based on a battery has been used to tackle the shortage in system inertia but has low and short-term power support during the disturbance. To address the issues, this paper proposes a new synthetic inertia control (SIC) design with a superconducting magnetic energy storage (SMES) system to mimic the necessary inertia power and damping properties in a short time and thereby regulate the microgrid (µG) frequency during disturbances. In addition, system frequency deviation is reduced by employing the proportional-integral (PI) controller with the proposed SIC system. The efficacy of the proposed SIC system is validated by comparison with the conventional ESS and SMES systems without using the PI controller, under various load/renewable perturbations, nonlinearities, and uncertainties. The simulation results highlight that the proposed system with SMES can efficiently manage several disturbances and high system uncertainty compared to the conventional ESS and SMES systems, without using the PI controller.

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG.

Owing to the significant number of hybrid generation systems (HGSs) containing various energy sources, coordination between these sources plays a vital role in preserving frequency stability. In this paper, an adaptive coordination control strategy for renewable energy sources (RESs), an aqua electrolyzer (AE) for hydrogen production, and a fuel cell (FC)-based energy storage system (ESS) is proposed to enhance the frequency stability of an HGS. In the proposed system, the excess energy from RESs is used to power electrolysis via an AE for hydrogen energy storage in FCs. The proposed method is based on a proportional-integral (PI) controller, which is optimally designed using a grey wolf optimization (GWO) algorithm to estimate the surplus energy from RESs (i.e., a proportion of total power generation of RESs: Kn). The studied HGS contains various types of generation systems including a diesel generator, wind turbines, photovoltaic (PV) systems, AE with FCs, and ESSs (e.g., battery and flywheel). The proposed method varies Kn with varying frequency deviation values to obtain the best benefits from RESs, while damping the frequency fluctuations. The proposed method is validated by considering different loading conditions and comparing with other existing studies that consider Kn as a constant value. The simulation results demonstrate that the proposed method, which changes Kn value and subsequently stores the power extracted from the RESs in hydrogen energy storage according to frequency deviation changes, performs better than those that use constant Kn. The statistical analysis for frequency deviation of HGS with the proposed method has the best values and achieves large improvements for minimum, maximum, difference between maximum and minimum, mean, and standard deviation compared to the existing method.

In this paper, an improved optimization algorithm is proposed to overcome the original Rao algorithm limitations (i.e., different characteristics in exploration and exploitation) and enhance the performance of the original Rao algorithm. In the improved algorithm, the self-adaptive multi-population and Levy flight methods are utilized in the original Rao algorithm. The improved algorithm is called I_Rao_3. The improved algorithm’s efficiency is confirmed by comparing it to the original Rao algorithm utilizing various standard benchmark test functions. Moreover, the proposed I_Rao_3 algorithm is utilized to improve the frequency response in a hybrid renewable power grid by fine-tuning the proportional-integral-derivative (PID) controller parameters. The targeted system used for this study is a hybrid power grid, which encompasses conventional generating stations (i.e., thermal power plants), renewable power stations (i.e., PV and wind power stations) for the analysis of the load frequency control (LFC) issue. Unlike other previously published works, this study considers the impact of DC links in parallel to AC links to interconnect the two-hybrid renewable power system area. In addition, the nonlinearities effects (i.e., generation rate constraint and a governor dead band) are applied to each area in order to achieve a more realistic study. The superiority of the proposed PID controller-based I_Rao_3 algorithm is endorsed by comparing its performance with many other optimization algorithms.

In power converter control, predictive control has several merits, such as simple concept and fast response. However, the necessity to use the weighting factor inside the cost function makes the control design complex in the case of regulating multivariables where the value of the weighting factor is obtained by a nontrivial process. Also, it primarily depends on the system parameters and operating points of the control system. This paper aims to enhance the model predictive algorithm of the singlestage topology of a quasi-Z Source Inverter (qZSI). The concept of a multi-objective optimization approach is used in addition to the sub-cost function definition to remove the weighting factors. By using the sub-cost function definition, the inductor current is pushed away from the main loop of the predictive algorithm. Thus, no weighting factor is needed to manage the priority of the inductor current. The other two control targets, which are the capacitor voltage and load currents, will be controlled by the multi-objective optimization approach without using any weighting factors. A detailed theoretical analysis of the proposed technique will be given and validated based on simulation results.

Under the current global circumstances, the urgent need to exploit renewable energy sources (RESs) is increasing. Increased penetration of RESs in hybrid distributed generation systems (HDGSs) poses a challenge due to the unsettled nature of RESs on frequency stability (FS). So, coordination between RESs is essential to sustaining FS. To boost FS in HDGSs, this study presents an adaptive coordination control (ACC) solution regarding RESs, a fuel cell (FC)-based energy storage system (ESS), and an aqua electrolyzer (AE) for producing hydrogen. In the studied system, the surplus energy of RESs is employed to supply electrolysis by AE to store hydrogen energy inside FC. The proposed ACC solution employs fuzzy control to dynamically adjust the RES energy ratio allocated to AE (the surplus energy “K n ” is a fraction of the overall RES-generated energy). The studied HDGS includes photovoltaic (PV) plants, wind turbines (WT), AE coupled with FCs, ESSs (e.g., flywheels and batteries), and diesel generators (DG). The suggested solution changes K n according to the frequency deviations to maximize the benefits of RESs alongside damping frequency variations. This study examined the proposed solution under different loading situations and compared it to previous research that took K n as a fixed value.

This article proposes an intelligent control strategy to enhance the frequency dynamic performance of interconnected multi-source power systems composing of thermal, hydro, and gas power plants and the high penetration level of wind energy. The proposed control strategy is based on a combination of fuzzy logic control with a proportional-integral-derivative (PID) controller to overcome the PID limitations during abnormal conditions. Moreover, a newly adopted optimization technique namely Arithmetic optimization algorithm (AOA) is proposed to fine-tune the proposed fuzzy-PID controller to overcome the disadvantages of conventional and heuristic optimization techniques (i.e., long time in estimating controller parameters-slow convergence curves). Furthermore, the effect of the high voltage direct current link is taken into account in the studied interconnected power system to eliminate the AC transmission disadvantages (i.e., frequent tripping during oscillations in large power systems–high level of fault current). The dynamic performance analysis confirms the superiority of the proposed fuzzy-PID controller based on the AOA compared to the fuzzy-PID controller based on a hybrid local unimodal sampling and teaching learning-based optimization (TLBO) in terms of minimum objective function value and overshoots and undershoots oscillation measurement. Also, the AOA’s proficiency has been verified over several other powerful optimization techniques; differential evolution, TLBO using the PID controller. Moreover, the simulation results ensure the effectiveness and robustness of the proposed fuzzy-PID controller using the AOA in achieving better performance under several contingencies; different load variations, the high penetration level of the wind power, and system uncertainties compared to other literature controllers adjusting by various optimization techniques.

The dependence on photovoltaic (PV) solar systems has increased dramatically to cover the increasing progress of world energy demand. Therefore, accurately specifying the parameters of PV modules is essential for evaluating the behavior and impact of integrating PV systems into electrical systems. In this context, a modified electric eel foraging optimization (MEEFO) is suggested for determining the parameters of solar PV modules. The proposed technique incorporates three improvement strategies: the fitness distance balance (FDB) strategy, fractional-order calculus (FOC), and quasiopposition-based learning (QOBL). These strategies enhance both exploitation and exploration capabilities while helping to prevent local optimization and premature convergence commonly observed in traditional EEFO. First, the proposed MEEFO is evaluated via two benchmark functions, including the basic and CEC 2019 benchmark functions. The results are then compared with those of other novel methods in terms of accuracy, convergence characteristics, and overall performance. The suggested MMEFO is then employed to identify the parameters for the single, double, and triple diode models of various PV cells/modules, including R.T.C. France, PVM752, STM6-40/36, PWP-201, and STP6-120/36. In addition, various meteorological data, such as changes in radiation and temperature, exist. The simulation findings demonstrate that MEEFO outperforms other techniques and is a reliable and superior method for accurately estimating PV module parameters. The application of MEEFO yields the lowest root mean square error (RMSE) values for the considered single, double, and triple diode models of R.T.C. France. Similarly, for STP6-120/36, the RMSE values are 1.660060E−02, 1.66006E−02, and 1.66089E−02, respectively. Additionally, for PWP-20, the RMSE values are 2.425075E−03, 2.42511E−03, and 2.42510E−03, respectively.