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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.

The Sinai Peninsula's northwestern area has many structure patterns with different directions and strong variations in basement depth. This work aims to deduce the basement surface’s depth and sedimentary basin’s locations and evaluate the subsurface structural framework to determine the new concession for hydrocarbon exploration in the research area.Geophysical data used in this study compiled between total aeromagnetic intensity (TM) data and wells data as locations and depths were used in 2D magnetic profiles in basement depth determination. The technique used for magnetic data interpretation to determine basement depth and structures include; Reducing to the pole (RTP), Power spectrum (low and high pass filters to determine deep and shallow sources), and Centre for Exploration Targeting (CET) Grid analysis.Therefore, according to these results, the basement depths range from 1500 to 5000 meters and the area has two principal structural trends: NW-SE trends related to extensions that happened in the Delta belt (Hing line) north Pelusium line and NE-SW trends associated with the Syrian arch system compressional force south Pelusium line. The new concession for hydrocarbon exploration is represented in the northwestern part of the study area.

This paper proposes an efficient control strategy to enhance frequency stability of three-area power system considering a high penetration level of wind energy. The proposed strategy is based on a combination of a Proportional Integral Derivative (PID) controller with a Linear Quadratic Gaussian (LQG) approach. The parameters of the proposed controller (i.e., PID-LQG) are optimally designed by a novel natural physical based-algorithm called Lightning Attachment Procedure Optimization (LAPO). The main objective is to keep the frequency fluctuation at its acceptable value in the presence of high penetration of wind energy, high load disturbance and system uncertainties. The superiority of the proposed PID-LQG controller is validated by comparing its performance with optimal Coefficient Diagram Method (CDM) controller, conventional CDM controller, optimal PID controller-based LAPO, and integral controller. Moreover, the exhaustive results completely demonstrate that the proposed controller gives better performance in terms of overshoot, undershoot, and settling time as well as provides reliable frequency stability for interconnected power systems considering high wind penetration and system uncertainties.

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.

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 renewable power systems have become more susceptible to the system insecure than traditional power systems due to reducing of the total inertia and damping properties that result from replacing the conventional generators with Renewable Energy Sources (RESs) as well as decoupling of the RESs from the AC grid using power converters. Therefore, maintaining the dynamic security of renewable power systems is the key challenge for integrating more RESs. This paper addresses a new strategy of frequency control including virtual inertia control based on Virtual Synchronous Generator (VSG), which emulates the behavior of conventional synchronous generators in large power systems, thus adding some inertia to the system control loop virtually and accordingly stabilizing the system frequency during high penetration of RESs. Moreover, the proposed virtual inertia control system-based VSG is coordinated with digital frequency protection for improvement the frequency stability and preservation the power system dynamic security because of the high integration level of the RESs. The effectiveness of the proposed coordination scheme is tested and verified through small and large scales of power systems, Microgrid (µG) and real hybrid power system in Egypt, respectively. System modelling and simulation results are carried out using Matlab/Simulink® software. The simulation results validated that the proposed coordination scheme can effectively regulate the system frequency and ensure robust performance to maintain the dynamic system security with high share of RESs for different contingencies.

With rapid growth of renewable energy sources (RESs) in modern power systems, the microgrids (μGs) have become more susceptible to the disturbances (e.g. large frequency/voltage fluctuations) than the conventional power systems due to decreasing their inertia constant. This low system inertia issue could affect the μGs stability and resiliency in the situation of uncertainties, thus threaten their dynamic security. Hence, preserving μG dynamic security is one of the important challenges, which is addressed in this study. Therefore, this study proposes a novel concept of frequency control incorporating a virtual inertia control-based optimal proportional-integral controller to emulate virtual inertia into the μG control loop, thus stabilising μG frequency during high penetration of RESs. Moreover, the proposed virtual inertia control system is coordinated with digital over/under frequency protection for enhancement of the frequency stability and preservation of the μG dynamic security because of the high integration level of the RESs. The simulation results of the studied μG are carried out using MATLAB/Simulink ® software to validate the effectiveness of the proposed coordination scheme. Results approved that the proposed coordination scheme can effectively regulate the μG frequency and guarantee robust performance to preserve the dynamic security of μG with high penetration of RESs for different contingencies.

Renewable energy sources (RESs) are growing rapidly and highly penetrated in microgrids (MGs). As a result of the replacement of the synchronous generators with a large amount of RESs, the overall system inertia might be dramatically reduced which negatively affected the MG dynamics and performance in face of uncertainties, leading to weakening of the MG stability, which considers being a serious challenge in such grids. Therefore, in order to cope with this challenge and benefit from a maximum capacity of the RESs, robust control strategy must be applied. Hence, in this paper, a new application of robust virtual inertia control-based coefficient diagram method (CDM) controller is proposed in an islanded MG considering high-level RESs penetration for enhancement the system’s validity and robustness in face of disturbances and parametric uncertainties. The proposed controller’s proficiency has been checked and compared with H-infinite controller using MATLAB/Simulink which approved that the CDM controller achieved superior dynamic responses in terms of accurate reference frequency tracking and disturbance reduction over H-infinite in all test scenarios. Thus, the proposed controller alleviates the difficulties of H-infinite controller such as the experience and necessary abilities to design the form of the weighting functions for the system. Consequently, the frequency stability is improved and approved that the proposed CDM-based virtual inertia controller can significantly support a low-inertia islanded microgrid against RESs and load fluctuations.

In this study, a new implementation of the slime mould algorithm (SMA) is proposed to find the optimal parameters of proportional-integral (PI) controller and enhance the frequency stability in a two-area interconnected power grid combining thermal generator unit and photovoltaic (PV) system. Moreover, the design process of the considered power system takes the maximum power point tracking (MPPT) of the PV into its consideration. Furthermore, a time-domain based on the integral of time multiply absolute error (ITAE) is recognized for the robust design of the PI controller parameters exploiting the proposed SMA. The comparative results demonstrate the robustness of the proposed SMA-PI controller among all control methods (e.g. WOA-PI controller, GA-PI controller, and FA-PI controller) in terms of maximum undershoot, maximum overshoot, and maximum settling time under different operating condition (system parameters variations, and load disturbance).

A modern power system is expected to be dominated by renewables, which either lack or have less rotating masses (i.e., source of inertia) compared to the traditional generation sources. Though, the growth of renewables generation based on power electronics can substantially decrease the inertia levels of renewable power grids and creates several frequency stability issues, resulting in power system degradation. To tackle this issue, this paper presents a recent virtual inertia scheme predicated on electric vehicles (EVs) to mimic the necessary inertia power in low-inertia smart hybrid power systems (SHPSs), thus regulating the system frequency and avoiding system instability. Moreover, to guarantee robust performance and more stability for SHPSs against multiple perturbations, system uncertainties, and physical constraints, this paper also proposes a robust control strategy relying on a coefficient diagram method (CDM) for the load frequency control (LFC) of SHPSs considering high renewables penetration and EVs. The efficacy of the proposed system (i.e., robust LFC with the proposed VIC strategy) is validated by comparison with conventional LFC with/without the proposed VIC system. In addition, the simulation outcomes showed that the proposed system can considerably support smart low-inertia hybrid power systems for many contingencies.

This paper proposes a novel robust controller for frequency stabilization of electrical systems taken into consideration a high renewable energy sources (RESs) penetration. The suggested controller, robust PID (RPID) controller, is combination of a proportional-integral-derivative (PID) controller and a linear quadratic gaussian (LQG) controller. Furthermore, the Improved lightning attachment procedure optimization (ILAPO) technique is applied for determining the optimal setting of the parameters of the introduced RPID controller. A studied power system with RESs is used as a test system to justify the performance of the RPID controller. The superiority of the introduced robust controller is verified through a comparison of its performance under system uncertainties with those of other robust controllers presented in the literature. The results elucidate that the RPID controller can effectually enhance frequency stability and guarantee reliable performance for power grids supplied with high share of renewable energy for all studied scenarios. Consequently, the proposed RPID controller purveys creditable for modern power systems considering RESs.

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 mainly depends on the system parameters and operating points of the control system. This paper aims to enhance the model predictive algorithm of the single-stage topology of a quasi Z-Source Inverter (qZSI). The concept of a multi-objective optimization approach is used besides 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.

In this work, a modified structure of the tilted integral derivative (TID) controller, i.e. an integral derivative-tilted (ID-T) controller, is developed for the load frequency control issue of a multi-area interconnected multi-source power system. Moreover, a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters. The performance of the proposed ID-T controller based on AOA is evaluated through a two-area interconnected power system, each area containing various conventional generation units (i.e., thermal, gas, and hydraulic power plants) and renewables (wind and solar power). Furthermore, system nonlinearities (i.e., generation rate constraints, governor deadband, and communication time delays), system uncertainties, and load/renewables fluctuations are considered in designing the proposed controller. The effectiveness of the proposed ID-T controller based on AOA is verified by comparing its performance with other control techniques in the literature (i.e. integral controller, proportional integral derivative (PID) controller, fractional-order PID controller, TID controller, and I-TD controller). The AOA's optimization superiority has also been verified against a variety of other sophisticated optimization methods, including particle swarm optimization and whale optimization algorithm. The simulation results exhibit that the proposed ID-T controller based on the AOA presents a great improvement in the system frequency stability under several contingencies of different load perturbations, system uncertainties, physical constraints, communication time delays, high renewables penetration.

This paper proposes a new model to mimics accurately the real dynamic and steady-state power generation profiles of a photovoltaic (PV) power plant connected to the power system. The model is targeted at load frequency control (LFC) studies, which takes into account the effect of the dynamic behavior of the system frequency on the PV output power. The artificial neural network using the radial bias function (ANN-RBF) is adopted to model the nonlinear behavior of the PV power plant only considering the frequency deviation as one of the inputs at different operating conditions. Also, the ANN prevents going into complex mathematical analysis to get the dynamic behavior of the output power from the PV plant. The input layer of the ANN-RBF receives the temperature, humidity, irradiation level, and frequency deviation, while the output layer represents the output power from the PV power plant. The main idea to include the frequency deviation in the PV modeling is to represent the dynamic behavior of the PV power that is injected by the PV inverter that is synchronized with the power system using the utility grid angle, i.e. system frequency. The ANN-RBF has been trained using actual data collected from a PV power plant in Aswan, Egypt. The ANN dataset contains significant variations of operating parameters to generalize the output modeling of the PV array. Moreover, the shortages in the previous models for the PV system used in the LFC studies, which are the first-order model, second-order model, and noise-based model, are discussed in detail. The simulation results show that the proposed model of the PV system based on the ANN-RBF is more suitable for the LFC studies than the well-known models based on the first-order representation as it takes into account the dynamic behavior of the system frequency during the load disturbance.

As the world’s supply of fossil fuels shrinks, there is a great need for clean and affordable renewable energy sources (RES) in order to meet growing energy demands. Furthermore, the conventional plants based on fossil fuel have serious environmental and financial problems, and therefore, the dependency of the distribution networks on the RES such as solar power systems for generating electrical power is significantly promoted. In the past few decades, solar energy systems have been received great attention as an important type of RES. Nowadays, solar energy sources constitute appropriate commercial options for small and large power plants. The two mainstream categories of solar energy systems utilized for this purpose are concentrated solar power (CSP) and photovoltaic (PV). This chapter presents a brief introduction about the role, important need, and advantages of renewable energies for today and the future, especially solar energy such as PV and CSP systems. In addition, it introduces a survey for all types of CSP technologies. As well as, it presents a literature review for the LCOE and cost reduction of CSP and PV systems, CSP modeling, and the application of ANN technologies in various SF systems. Further, it presents the problem definition, objectives, and outlines of this thesis.

This chapter presents a coordination of secondary frequency control (i.e., load frequency control) and superconducting magnetic energy storage (SMES) technology using a new optimal PID controller in a real power system (e.g., Egyptian power system (EPS)) considering high wind power penetration (HWPP). This coordination scheme is proposed for compensating the system frequency deviation, preventing the conventional generators from exceeding their power ratings during load disturbances, and mitigating the power fluctuations from wind power plants. The EPS considering HWPP was tested by the MATLAB/Simulink simulation to prove the effectiveness of the proposed coordinated control strategy. The convention plants of the EPS are decomposed into three dynamics subsystems: hydro, reheat, and non-reheat power plants. Moreover, the physical constraints of the governors and turbines such as generation rate constraint (GRC) of power plants and speed governor dead-band are taken into consideration. The results show the superior robustness of the proposed coordination against all scenarios of various load profiles, and system uncertainties in the EPS considering HWPP. Furthermore, the results were verified by comparing it with both the optimal LFC with/without the effect of conventional SMES, which is without modifying the input control signal.

Today’s power systems have a high-level penetration of renewable energy sources (RESs). Therefore, the modern power systems become more susceptible to the system insecurity than conventional power systems due to lack of system inertia that results from replacing the conventional generators with RESs and frequency fluctuations that result from the intermittent nature of the RESs. Hence, this chapter presents a new strategy of frequency control including virtual inertia control based on virtual synchronous generator (VSG), which emulates the behavior of conventional synchronous generator in large power systems, thus adding some inertia to the system control loop virtually and accordingly stabilizing the system frequency during high penetration of RESs. Moreover, this chapter proposes an efficient coordination scheme of frequency control loops including the proposed virtual inertia control system-based VSG and digital over/underfrequency protection for maintaining the dynamic security of the renewable power systems because of the high integration level of the RESs. System modeling and simulation results are carried out using MATLAB/Simulink® software. Results approved that the proposed coordination scheme can effectively regulate the system frequency and guarantee robust performance to preserve the dynamic security of renewable power systems with high penetration of RESs for different contingencies.

Renewable energy sources (RESs) are growing rapidly and highly penetrated in microgrids (μGs). However, there are some impacts resulting from integrating RESs such as power fluctuations caused by the intermittent nature of RESs, and lack of system inertia resulting from replacement of synchronous generators with RESs. Hence, in order to cope with this challenge and benefit from a maximum capacity of the RESs, this chapter presents a new frequency control strategy based on a virtual inertia control to emulate virtual inertia into the μG control loop, thus stabilizing μG frequency during high penetration of RESs. Moreover, the proposed virtual inertia control system based on an optimal proportional–integral (PI) controller is coordinated with digital over/underfrequency relay to improve the frequency stability and maintain the dynamic security of the μG considering high penetration of RESs. The studied system simulation results are conducted using MATLAB/Simulink® software to validate the efficacy of the proposed coordination scheme. Results endorsed that the proposed coordination scheme can efficiently regulate the μG frequency and ensure robust performance to maintain the dynamic security of μG with high penetration of RESs for various contingencies.

This paper presents a coordination strategy of Load Frequency Control (LFC) and digital frequency protection for an islanded microgrid (MG) considering high penetration of Renewable Energy Sources (RESs). In such MGs, the reduction in system inertia due to integration of large amount of RESs causes undesirable influence on MG frequency stability, leading to weakening of the MG. Furthermore, sudden load events, and short circuits caused large frequency fluctuations, which threaten the system security and could lead to complete blackouts as well as damages to the system equipment. Therefore, maintaining the dynamic security in MGs is one of the important challenges, which considered in this paper using a specific design and various data conversion stages of a digital over/under frequency relay (OUFR). The proposed relay will cover both under and over frequency conditions in coordination with LFC operation to protect the MG against high frequency variations. To prove the response of the proposed coordination strategy, a small MG was investigated for the simulation. The proposed coordination method has been tested considering load change, high integration of RESs. Moreover, the sensitivity analysis of the presented technique was examined by varying the penetration level of RESs and reducing the system inertia. The results reveal the effectiveness of the proposed coordination to maintain the power system frequency stability and security. In addition, the superiority of the OUFR has been approved in terms of accuracy and speed response during high disturbances.

This study proposes a coordination of load frequency control (LFC) and superconducting magnetic energy storage (SMES) technology (i.e. auxiliary LFC) using a new optimal PID controller-based moth swarm algorithm (MSA) in Egyptian Power System (EPS) considering high wind power penetration (HWPP) (as a future planning of the EPS). This strategy is proposed for compensating the EPS frequency deviation, preventing the conventional generators from exceeding their power ratings during load disturbances, and mitigating the power fluctuations from wind power plants. To prove the effectiveness of the proposed coordinated control strategy, the EPS considering HWPP was tested by the MATLAB/SIMULINK simulation. The convention generation system of the EPS is decomposed into three dynamics subsystems; hydro, reheat and non-reheat power plants. Moreover, the physical constraints of the governors and turbines such as generation rate constraints of power plants and speed governor dead band (i.e. backlash) are taking into consideration. The results reveal the superior robustness of the proposed coordination against all scenarios of different load profiles, and system uncertainties in the EPS considering HWPP. Moreover, the results have been confirmed by comparing it with both; the optimal LFC with/without the effect of conventional SMES, which without modifying the input control signal.

This study presents a digital coordination strategy of load frequency control (LFC) and Over/Under Frequency Relay (OUFR) protection for an isolated microgrid (MG) considering high penetration of renewable energy sources (RESs). In such MGs, the reduction in system inertia due to the replacement of traditional generating units with a large amount of RESs causes undesirable influence to MG frequency stability, leading to weakening of the MG. Furthermore, sudden load change and short circuits caused large frequency fluctuations which threaten the system security. In order to handle these challenges, this study proposes a specific design of the digital OUFR, which will operate for both conditions of over and under frequency in coordination with digital PID controller based on mapping technique in discretization process to protect the MG against high-frequency variations. To prove the effectiveness of the proposed digital coordination strategy, a small MG was investigated for the simulation considering load change, varying the penetration level of RESs and the system inertia. The results reveal the robustness of the proposed coordination to maintain the MG frequency stability and security. In addition, the superiority of the digital OUFR has been approved in terms of accuracy and speed response during high disturbances.

This paper presents a Model Predictive Control (MPC) technique for frequency stabilization of renewable power systems with inherent nonlinearities. To prove the effectiveness of the proposed technique, the Egyptian Power System (EPS), which includes both conventional generation units (i.e., non-reheat, reheat and hydraulic power plants) and wind turbines is tested using Matlab/SIMULINK® software. Furthermore, the performance of the proposed MPC technique is compared with an optimal Proportional-Integral (PI) controller-based Particle Swarm Optimization (PSO) algorithm under different load profiles and system uncertainties. The simulation results emphasize the superior robustness of the proposed MPC-based LFC in comparison to the optimal PI controller.