Essam El-Din Mohamed Aboul Zahab

Wind turbines (WTs) have highly nonlinear and uncertain dynamics due to aerodynamic complexity, mechanical factors, and fluctuations in wind conditions. Turbulence and wind shear add complexity to modelling, especially in constant power region (region 3). Thus, an effective control design demands a deep understanding of the nonlinearities and uncertainties. This paper suggests a novel model-free reinforcement learning (RL) collective pitch angle controller to operate efficiently in region 3. The proposed controller stabilizes generator speed, maximizes power output, and minimizes fluctuations while accommodating system uncertainties, nonlinearity, and pitch limits. The disparity between WT dynamics due to wind speed perturbations and uncertainties is measured using a gap-metric criterion. The controller design adopts a deep deterministic policy gradient (DDPG) algorithm to train six agents in a medium-fidelity WT environment at different mean wind speeds to ensure the controller's robustness. Initially, imitation learning is used for efficient sample collection to fasten training convergence. Afterwards, the agent learns by interacting with the environment. After the training, the pitch control outputs from multi-trained agents are processed by a fuzzy system to have smooth transitions under different operating conditions. The resulting fuzzy DDPG (F-DDPG) controller is deployed to obtain the optimal pitch control action. The performance of the proposed F-DDPG controller is compared to the gain-scheduled PI (GSPI), Linear-Quadratic-Regulator (LQR), and single-DDPG-agent controllers. The controllers are simulated in high-fidelity onshore and offshore 5-MW WT environments using the OpenFAST/MATLAB simulation tools. The results reveal the superiority of the proposed controller in generalizing its optimal performance in different operating conditions.

Hydrothermal strategy was used to grow molybdenum sulfide (MoS2) micro flower-like structures reinforced reduced graphene oxide (rGO) for supercapacitor (SC) application. Morphological and structural analyses confirmed the well-distribution of MoS2 on rGO sheets. Despite using lithium sulfate (Li2SO4) as a relatively high-voltage window electrolyte, potassium iodide (KI) was incorporated for sturdy iodide/iodine redox interaction. Thus, the fabricated symmetric SCs result achieved superior power density (Pd) of ∼36 kW/kg and exceptionally high energy density (Ed) of 132.9 Wh/kg at 10 and 0.1 A/g, respectively. The maximum estimated specific capacitance (Cs) was 357 F/g for optimum KI ratio. Additionally, SC possesses more than 99 % of retention value after applying 8000 cycles at 10 A/g. These findings implies MoS2/rGO composite and Li2SO4/KI mixed electrolyte configuration as a simple but efficient SC configuration for high energy, power and lifetime.

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.

Improving the performance of distribution networks is a primary target for power system operators. Besides, energy resource limitations and cost-effective distribution of electricity to the consumers encourage engineers, distribution system operators, and researchers to increase the efficiency of electric power distribution systems. Fortunately, many technologies can effectively make such improvements. Active and reactive power compensators such as distributed generators (DGs) and shunt capacitor banks (SCBs) are examples of compensators that can effectively make such improvements in modern radial distribution systems (RDSs), in addition to using recent techniques such as energy storage technologies. Voltage regulators (VRs) can also help these compensators function better in a much more effective techno-economic manner in RDSs, enhance voltage profiles and load stability, and reduce voltage deviations from acceptable values. Unfortunately, rising project investment may result if uneconomic facilities or expensive technologies are used to reduce electric losses significantly. Therefore, economic considerations related to the installed equipment in the networks should be considered. In this regard, the well-known whale optimization algorithm (WOA) is applied in this work to allocate DGs, SCBs, and VRs in a realistic 37-bus distribution system to minimize power losses while conforming with several linear and nonlinear constraints. A cost-benefit analysis of the optimization problem is made in terms of – investment and running costs of the compensators used; saving gained from the power loss reduction, and benefits from decreasing the power to be purchased from the grid; reducing voltage deviations and overloading; and enhancing voltage stability (VS). Three loading scenarios are considered in this work – light, shoulder, and peak levels of load demand. The numerical findings obtained show a noteworthy techno-economic improvement of the quality of power (QoP) performance level of the RDS and approve the efficiency and economic benefits of the proposed solutions compared to other solutions in the literature.

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.

The prevalence rate of photovoltaics (PV)-based generation systems has increased by more than 15 folds in the last decade, putting it on the top compared to any other power generation system from the expandability point of view. A portion of this huge expansion serves to energize standalone remote areas. Seeking improvements from different aspects of PV systems has been the focus of many studies. In the track of these improvements, parallel MPPT configuration for PV standalone systems have been introduced in the literature as an alternative to a series configuration to improve the overall efficiency of standalone PV systems.
However, this efficiency improvement of the parallel MPPT configuration over the series one is not valid for
any standalone application, therefore an assessment procedure is required to determine the most efficient MPPT configuration for different standalone applications. Therefore, in this study, an assessment procedure of parallel MPPT is conducted to demonstrate the suitability of utilizing such a configuration compared to series one, based on load daytime energy contributions. This assessment will help PV system designers to determine which MPPT configuration should be selected for applications under study. Furthermore, a new utilization of parallel MPPT configuration is introduced for operating universal input power supply (UIPS) loads to eliminate the inverter stage, thereby increasing the overall system efficiency and reliability. Finally, a systematic procedure to size the complete system is introduced and reinforced by a sizing example.

The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system.

Power level control is a critical issue in nuclear power stations due to its nonlinear dynamics. One of the most commonly used controllers is fractional order proportional–integral–derivative (FOPID). The FOPID is an enhanced and modern controlling system that has two additional added parameters. In this paper, comparison between particle swarm, gray wolf and ant lion optimization techniques is performed to determine the FOPID controller parameters. The nuclear reactor is a pressurized water reactor which is a fifth order nonlinear reactor model and is simulated using MATLAB software based on the point kinetic model. The integral square error (ISE) performance index is used to evaluate the performance of the three optimization techniques. The simulation results show that ant lion optimization for tuning the FOPID controller parameters gives the best performance and integral square error index better than the two other optimization techniques.

This paper proposes an adaptive dynamic Mho distance relay based on a phase comparator scheme for protecting interconnected transmission networks compensated with a Thyristor Controlled Series Capacitor (TCSC). The proposed relay uses an impedance index factor to initiate the fault detection subroutine. The RMS of the positive sequence current of the faulted loop and the TCSC terminal current are compared for TCSC zone identification. A phase comparator for ground and phase distance elements is proposed, relying on the positive sequence voltage as a polarized memory quantity, while the operating and polarizing quantities are developed using estimated TCSC impedance to mitigate its negative impact. The proposed scheme is easy in implementation and independent on synchronized data transfer, as minimum communication requirements are needed. To evaluate the performance of the proposed scheme, extensive simulation studies were carried out on an IEEE9 bus system compensated with TCSC for different firing angles covering four modes of TCSC operations, different fault types, and fault locations. In addition, an IEEE-39 bus network, as a large interconnected system, is tested for validation purposes. The achieved results designate the precision of the proposed scheme. Moreover, the results indicate its effectiveness for fault resistance tolerance, close-in three phase faults, and stable power swing phenomenon compared with conventional relays.

A two-fold adaptive dynamic quadrilateral relay is developed in this research for protecting Thyristor-Controlled Series Compensator (TCSC)-compensated transmission lines (TLs). By investigating a new tilt angle and modifying the Takagi method to recognize the fault zone identifier, the proposed relay adapts its reactive reach and resistive reach separately and independently. The investigated tilt angle and identified fault zone use the TCSC reactance to compensate its effect on the TL parameters and system homogeneity. Excessive tests are simulated by MATLAB on the non-homogenous network, IEEE-9 bus system and further tests are carried out on IEEE-39 bus system in order to generalize and validate the efficiency of the proposed approach. The designed trip boundaries are able to detect wide range of resistive faults under all TCSC modes of operations. The proposed approach is easy to implement as there no need for data synchronization or a high level of computation and filtration. Moreover, the proposed adaptive dynamic relay can be applied for non-homogeneity systems and short as well as long TLs which are either TCSC-compensated or -uncompensated TLs.

This article extensively investigates the calculations of the compensation factor of the thyristor-controlled series compensator (TCSC), which are used to accurately evaluate the negative impacts of the TCSC on the performance of conventional distance relays. To broadly evaluate the distance protection
performance, the TCSC was adapted to the IEEE 9-bus system as one of the interconnected transmission networks that are increasingly spreading to improve service reliability, reduce reserve capacity, and enhance
system efficiency. In addition, IEEE 39-bus system, as a large interconnected system, is also examined to generalize the TCSC impact on different interconnected systems. To determine the precise impact,
the impedance of the TCSC was calculated based on its practical design parameters. The impedance of the TCSC was examined as a function of transmission line impedance and ring angle. Both Mho and Quadrilateral distance relays were tested using the MATLAB/Simulink environment for different types of faults, fault locations, fault resistances, and ring angles for capacitive, inductive, and blocking modes of TCSC operation. In addition, distance relay performance was evaluated during power swing phenomenon in the presence of the TCSC. Simulation tests indicated the negative impacts of the TCSC on distance relay operation, which are not limited to over-reach and under-reach in faulty conditions but also to maloperation in dynamic disturbances that cause power swing phenomena on the protected line.

The electricity companies enforced some legislation on the electricity consumption contracts because the electricity network cannot bear the increasing demands of consumers. Therefore, a new controlling system is required to improve the frequent variations of the power system operating point (OP). Consequently, the flexible AC transmission systems (FACTS) controllers should be able to integrate with recent OP. Coordination of FACTS controller is more sophisticated due to various OP and uncertainties parametric in cooperation with the non linearity of power system (PS). Static Synchronous Compensator (STATCOM) plays very important role like the stability support of large and small transient-disturbance in PS. Therefore, the aim of this research is presents fuzzy logic (FL) with the PI controller (a novel controller) its ability to improve the performance of the power system along with the capability of switch irregular and rough actual world data. This new controlling system may be suitable for a wide range of applications especially the models which deal with huge and complicated data analysis. This new controller system carries out the adjustments of the voltage on DC capacitor under transient and steady-state conditions.

Increasing energy demand worldwide has resulted in more penetration of renewable sources for developing non-polluted electric energy despite their prices are not economically competitive to traditional generation systems due to intermittent nature of renewable resources. Energy storage systems are used to counteract the intermittent nature of renewable sources. Therefore, the optimal sizing and design of stand-alone renewable generating systems is a significant concern to get a more cost-effective system. This paper focuses on achieving the optimum design and size of a microgrid to meet the load requirements and reducing the total cost including capital, investment, operational and maintenance costs. For this aim, the sizing problem is formulated to be solved using three well-known metaheuristic algorithms, namely, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Cuckoo Search Optimization (CSO). The employed microgrid comprises hybrid renewable energy sources of PV/Wind systems integrated with a hybrid energy storage system of Battery and FC/Electrolyzer set for supplying AC loads located in Zafarana, Egypt. On the basis of real meteorological data of the studied location, the produced energies from the renewable sources are estimated using MATLAB developed algorithms. The simulation results showed that the optimized design using CSO can robustly and efficiently yield the optimal design of a microgrid.

This paper presents a novel stabilization FACTS-based scheme that acts as a switched compensator for grid-connected wind energy systems. It is a member of a family of devices and switched dynamic voltage stabilization converters that were developed to ensure minimal loss of excitation, voltage stabilization, energy efficient utilization, power quality enhancement and harmonic distortion reduction in AC distribution grid networks. A novel-dual action distributed FACTS based–switched power filter compensator (SPFC) scheme is developed for efficient utilization of wind energy under varying wind conditions and major load excursions. A dynamic multi-level error-driven decoupled time de-scaled multi regulation control strategy is used to guarantee better power quality performance in terms of voltage enhancement and stabilization of the AC buses, improvement of power factor, and harmonic distortion reduction. The proposed SPFC was controlled using an inter-coupled weighted modified proportional-integral-derivative (WM-PID) controller. Cuckoo search (CS) optimization algorithm is employed to get the PID controller gains in terms of variations and excursions in wind speed and dynamic load excursions to reflect the performance of the compensator scheme. The effectiveness of the proposed SPFC with the multi-level control strategy has been assessed by time-domain simulations in Matlab/Simulink environment. The results obtained show the robustness of the proposed topology.

This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR). The scheme proposed utilizes an error-driven proportional–integral–derivative (PID) controller to guarantee better power quality performance in terms of voltage enhancement and stabilization of the buses, energy efficient utilization, and harmonic distortion reduction in a distribution network. This method maintains the load voltage close to or equal to the nominal value in terms of various voltage disturbances such as balanced and unbalanced sag/swell, voltage imbalance, notching, different fault conditions as well as power system harmonic distortion. A grasshopper optimization algorithm (GOA) is used to tune the gain values of the PID controller. In order to validate the effectiveness of the proposed DVR controller, first, a fractional order PID controller was presented and compared with the proposed one. Further, a comparative performance evaluation of four optimization techniques, namely Cuckoo search (CSA), GOA, Flower pollination (FBA), and Grey wolf optimizer (GWO), is presented to compare between the PID and FOPID performance in terms of fault conditions in order to achieve a global minimum error and fast dynamic response of the proposed controller. Second, a comparative analysis of simulation results obtained using the proposed controller and those obtained using an active disturbance rejection controller (ADRC) is presented, and it was found that the performance of the optimal PID is better than the performance of the conventional ADRC. Finally, the effectiveness of the presented DVR with the controller proposed has been assessed by time-domain simulations in the MATLAB/Simulink platform.

A new Power Management Regulation Control (PMRC) integrated with Demand Side Management (DSM) strategies is proposed to enhance the Energy Management System (EMS) of a stand-alone hybrid microgrid. The microgrid combines Wind and PV systems as Renewable Energy Sources (RES) with a hybrid Energy Storage System (ESS) of Battery and Fuel Cell/Electrolyzer set. Towards achieving Net Zero Energy Supply, such microgrid is adequate in remote and isolated new communities with AC controllable critical and noncritical loads. The proposed PMRC implies two-levels of control based on Multi-Agent System (MAS). The first level keeps the output power of each source in its maximum available output power by applying maximum power point tracking (MPPT) techniques. The second level is based on making proper decisions for achieving the power balancing regulation and coordination between the available and the reserve power of the RES and ESS under different operating modes. Valley Filling, Energy Conservation and Load Shifting are applied as DSM strategies to improve loads sustainability during system contingencies. Considering the battery as the most expensive part in the microgrid, the effectiveness of the proposed strategy is further verified by determining the maximum permissible estimated battery lifetime during the operation in all possible scenarios. Extensive simulation studies for various scenarios of microgrid operation in a year were carried out using Matlab/ Simulink with realistic typical wind speed, solar irradiation data and restricted by the status of available ESS.

High penetration of wind energy imposes several operational challenges due to its uncertainty and intermittent nature. Flexible energy resources represent key solutions to compensate for power mismatch associated with wind power (WP) uncertainty and intermittency. This paper proposes a new stochastic unit commitment (SUC) problem formulation including high penetration of wind energy, energy storage system (ESS), and demand side management. Firstly, the Latin hypercube sampling is combined with Cholesky decomposition method to generate different WP scenarios. The simulated scenarios are then reduced using the fast forward selection algorithm. Finally, a novel SUC formulation implements these reduced scenarios to size the ESS optimally, considering its cost and benefit maximization of wind energy. To validate the proposed approach, a nine-unit test system is used to demonstrate the reduction in the operational cost and the increase in the utilized wind energy under different operational conditions.

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