Search

Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and BAT optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented BAT-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique.

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication.

Exhausting reservoirs of oils have led to investing in renewable energies to meet the ever-increasing energy demand in the world and mitigate the environmental impacts of conventional fuels. One of the prevalent renewable energy technologies is the solar photovoltaic (PV) system. These solar units face some challenges, mainly due to the volatile power generation and frequent oscillations, besides low rate when tracking the maximum power. Accordingly, a novel approach is suggested in this research work to address such drawbacks in the conventional control systems. The presented framework is based on a sliding mode controller (SMC) for the sake of maximum power point tracking (MPPT) applied to solar PV units, taking into account diverse meteorological conditions. In this respect, an efficient optimization algorithm, named “θ-modified krill herd (θ-MKH) method” has been proposed to optimally determine the parameters of the SMC. The operating conditions are online and in the grid-connected state. An overarching comparison has been made between the presented approach and other methods like hill climbing (HC), β technique, fractional Voc, incremental conductance (INC), fuzzy logic controller (FLC), and classic fixed-step perturb and observe (P&O) while taking into consideration diverse meteorological conditions. The developed control framework based on the SMC and θ-MKH guarantees the desirable operation of the PV system in terms of limited oscillations and stable operation with the efficiency of 99.5% in tacking the maximum power point in rational time.

This paper presents an integrated method to identify the fault direction in the presence of a series compensator via also identifying the power flow direction. The presented method relies on monitoring both measured angle of the positive-sequence (PS) currents and magnitude of PS voltage. The change of PS voltage magnitude is used to detect the current inversion case. The presented criteria depend on the power flow direction before the fault occurrence. Therefore, a power flow direction identification method is presented using only the change of the phase angle of the PS current. Different system configurations are investigated to test the reliability of the proposed protection scheme (PPS). The power and protection systems’ configurations contain different capacitor ratios leading to current and voltage inversion scenarios, reversing the power flow direction and single-pole tripping. Simulation experiments conducted via the software ATP-EMTP has shown the proper operability of the PPS.

This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.

Identification of the fault section and fault phase selection are considered the main tasks of a multi-terminal transmission line protection scheme. The current study classifies the methods applied to multi-terminal transmission lines. Moreover, investigated solutions for the drawbacks that appeared with the previous methods are suggested. Based on wavelet packet transform, a scheme for a multi-terminal transmission line is presented in this article. The suggested technique uses the wavelet packet transform (WPT) to get the approximate and detail coefficients of the differential current and model voltage for all buses in a system, detects and classifies the faults (FDC) within a half-cycle window, and identifies the faulted section (FSI). A fast mother wavelet is used with a three-coefficient high pass filter to analyze signals of the model voltage and differential. In this regard, two frequency levels were chosen, in which the first level of the differential current signal was chosen to detect and classify a fault, while the first level of the model voltage signal was chosen to identify the faulted section. Several fault cases are simulated and analyzed in the ATP/EMTP environment for an N-terminal system followed by successful testing on three- and five-terminal systems to evaluate the performance of the proposed scheme. The simulation results obtained show that the proposed scheme is efficient and shows high speed and accuracy of fault detection compared to other methods in the literature.

With the growing use of motorized loads, light bulbs, and solar cells, the use of electrical equipment with nonlinear behavior increases the harmonic level of currents and voltages in electrical distribution networks. As a result, analysis of distorted harmonic systems and calculation of overpaying on bills (penalties) due to harmonics consequences play an essential role in controlling harmonics and reactive power compensation in electric distribution networks. Meanwhile, providing a model to calculate the penalty due to harmonics has recently been investigated by many electric companies. The main aim of this work is to provide a decision tool to allow economic assessment of harmonics and its consequences in power systems from a financial point of view supported by a distribution company insight. In this regard, this paper presents a simple model to calculate the rate of the penalty of customers in the presence of harmonic-polluted loads to encourage more efficient use of the power system. One of Sanandaj's commercial projects has been selected as a case study to evaluate the effectiveness of the proposed model. The results obtained show that the presented model calculates and evaluates the problems caused by the existence of harmonics in the studied cases effectively.

Energy generation from renewable energy sources (RESs) is rapidly developing across the world to improve the performance of power networks and increase the share of RESs in world energy production. In this regard, energy storage (ES) technologies are the key enablers for reliable use of renewables because they introduce many benefits for modern power systems. However, the choice of a suitable technology depends on several techno-economic metrics, which require the decision-maker to investigate the applicability of the technology and whether it offers promising benefits or not. Hence, this paper presents an ES cost model that considers long-term, medium-term, and short-term ES applications, technologies and technical characteristics in an integrated framework that consider the ES technical and economic characteristics supported by in-market insight, including capital costs of the technologies; operation and maintenance costs; replacement costs during the lifetime of the system; and disposal and recycling costs, based on the current ES costs. Two key metrics, namely the annualized life cycle cost of storage (LCCOS) and the levelized cost of energy (LCOE), are used to make proper ES operational choices while complying with their technical and operational performance limits. Further, a sensitivity analysis of the governing factors that affect the storage cost is presented to introduce a powerful decision tool to empower techno-economic assessment of ES systems using the proposed cost models.

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.

This paper suggests optimal scheduling of Energy Hub (EH) considering water to maximize its profit in a pool-based day-ahead electricity market where the EH including electrical-thermal-water demands. The proposed model is expressed as a bi-level problem and allows the EH acts as an independent price maker considering uncertain parameters. The market settlement mechanism is based on the pay-at-MCP where every market participant is paid at the Market Clearing Price (MCP). Maximizing the profit of the recommended strategic producer and minimizing dispatch cost within the power grid are represented as the upper level and lower level of the bi-level stochastic optimization problem, respectively. The problem is formulated with Mathematical Program with Equilibrium Constraints (MPEC) and is converted into a new Mixed-Integer Linear Program (MILP) based on Karush-Kuhn-Tucker (KKT) conditions. To model the high uncertainties associated with water, heat and electricity demand within the EH as well as the uncertainties in the generators and loads submitted a price to the market, a stochastic framework based on Point Estimate Method (PEM) is developed. Due to the complex and nonlinear nature of the proposed problem, a new optimization algorithm based on a modified Bat Algorithm (BA) is applied to solve the problem optimally. To confirm the strong performance of the proposed strategic EH producer in the electricity market as well as its effect on the buses’ Locational Marginal Prices (LMPs) in a transmission-constrained market, authors applied the presented approach on the 24-bus IEEE system. So overall, the presence of line congestions may increase the opportunity of the proposed producers to make a profit. Depending on the lines’ connection points, the effects of the congestion lines on profit varies in lines. The effective performance of the proposed model is demonstrated in the results.

Power quality determines the fitness of electric power to consumer. Hence, it's considered very important issue in the operation of electrical systems, relates to the resolution of the problems of continuity of service or the malfunction of the end-user equipment. Remote and isolated communities such as villages and islands are often subject to substantial and repeated power outages due to the weakness of the power lines connecting to the main network. This paper investigates the possibility of improving the quality of supply in remote areas using distributed energy resources (DERs) such as; PV, wind, solar or hybrid sources depending on the environmental conditions of these areas. A comprehensive study related to power quality improvement for a realistic model for Aswan Island network that located in Southern Egypt is presented. PV with storage battery and diesel power generation units are connected at the weakest voltage point in the system. DigSelient commercial software is used to simulate the case understudy. Different simulation scenarios are performed to compare and evaluate the overall power quality for the system with DERs.

Minerals fillers such as Magnesium Oxide (MgO), Alumina Trihydrate (ATH), Calcium Carbonate (CaCo3), and Silica (SiO2) were used to produce epoxy composite for high and medium voltage in electrical application. Effects of types and percentage weight of filler on flashover voltage properties of epoxy resin were determined. Cylindrical rod epoxy samples with different lengths (10, 20, and 30mm) and filler types have been prepared, these fillers with 30% percentage loading of epoxy samples by weight. Flashover voltage of composite material was measured under various environmental conditions, to check the accuracy the test was carried out for five times on each sample under the same condition. Results showed that flashover voltage of composite improved with epoxy/ATH filler at all lengths and conditions, also flashover voltage for samples in dry condition is higher than samples with other conditions at all filler and lengths.

Switched Inductor Switched Capacitor Inverter (SISCI) for Photovoltaic (PV) Uses is recommended in this paper. The planned inverter is a two stage-inverter. The first-stage consists off Switched-Inductor-Switched-Capacitor Based Active Network (SISCAN) converter and the second-stage is the five-switch inversion stage. The projected structure owns some intrinsic gains such as high-boosting-ratio, reduced voltage-stresses and moderate-efficiency. Control-algorithm settled for the anticipated-coordination to do the upcoming-tasks: mine Maximum-Power (MP) from PV modules, boost low-dc output-voltage and insert sinusoidal-current synchronized with the grid-voltage. Principal of operation, Analysis and simulation of the proposed system are discussed in details.

This paper proposes the novel concept and model of smart island (SI) in the power system. Over a few decades, the concept of smartness of power system has attracted many attentions due to the capability of interconnection of different systems within a smart platform. The proposed SI model is made up of different sections including the smart transportation systems (STS) consists of electric vehicles (EVs) and the subway, smart energy hub (EH) and a microgrid (MG). The coordinated supply of electricity, thermal and water demand within the SI's sections is also addressed in the proposed model. The islanding potential prepares a proper condition to supply the water demands of the SI's sections through the smart EH which is equipped with a desalination unit. Such a procedure can reduce the water demands' supplement costs at different sections, specifically the subway segments, by using the seawater. Moreover, the electrical demand of the subway is supplied by constructing the subway's charge stations (SCS) at the appropriate locations. In this model, the subway is able to increase its profit by having energy transactions with the EVs and MG through the proposed SCSs. Furthermore, the uncertain parameters associated to the SI are modelled using points estimate method (PEM). Results reveal the effectiveness and applicability of the proposed SI and optimization method.

This paper basically investigates the enhancement of a modified non-square direct matrix converters (MC) for renewable microgrid applications using generative adversarial networks (GAN). Such a deep learning technique which benefits from two multi-layer adversary perceptions is used to enhance the security of the data in the microgrid. This paper also provides a new application of the non-square direct MC in the microgrid system which is able to provide balanced output with any desired amplitude and frequency under unbalanced condition specifically in the case of using renewable energy sources such as photovoltaics (PVs) and wind turbines. In addition, a new modified social spider optimization (MSSO) algorithm is introduced to help improving the training process of GAN. Simulation results show that matrix converter based on GAN makes it possible to convert any input voltage to the desired output voltage which leads to the elimination of the back to back converter of wind turbine.

Due to the direction of the world to diminish pollution production and to enhance the environmental conditions, the penetration of wind energy in the electric grid is appreciably increased nowadays. The variable-speed wind turbine direct-drive permanent-magnet synchronous generator is connected to the electrical network through a frequency converter-based an optimum controller, such as a conventional proportional-integral controller (PI) or the fuzzy logic controller (FLC). The PI has a constant gain but has some drawbacks, such as affected by variable variation. This paper exhibits an implementation of a grasshopper optimization algorithm (GOA) to design FLC. GOA-based FLC is utilized to control the grid- and generator-side converters to enhance the transient stability of the grid tied-wind energy. To ensure the efficacy of the proposed control scheme, a comparison among the results achieved by the GOA-based FLC and Newton Raphson based-PI has occurred. The simulation results describe the capability of the GOA-based FLC to enhance the response of the grid-tied wind energy under various operation cases. Simulation is carried out by MATLAB/Simulink program.

Transformers are equipment designed to operate perfectly at linear loads. Nowadays with wide use of modern technology requirement and industry needs which utilize nonlinear loads, the transformers are subjected to more nonlinear loads. Many research studied the influence of harmonics upon transformers. This paper focuses on transformer losses calculations for the case study of Mubarak Pumping Station (MPS) with and without passive harmonic filters. Also harmonic indices including K-Factor transformer, Total Harmonic Distortion (THD), Harmonic Loss Factor (FHL) and de-rating of transformer are calculated. When distribution transformer is operating under harmonic load, the value of load losses is increased, while the no load losses increased with a little amount. No load losses of a distribution transformer under harmonic loads can increase its operating cost. K-Factor calculation depends upon the fundamental current and the harmonic current components, and it is an indicative value of the harmonic contents in the power system.

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.

In this paper, demand response concept has been exploited to control the real time charging of plug-in hybrid electric vehicles. A developed voltage stability index is embedded in the continuous power-flow solution process adopted in distribution management systems (DMS). If the distribution system is unbalanced, positive sequence data is used for evaluating the voltage stability index (SI). The developed control strategy controls the charging level such that guarantees secure operation of the distribution system regardless the number of plug-in hybrid electric vehicles (PHEVs) connected to the distribution system. The developed control method uses fuzzy logic controller for evaluating the battery of each PHEV in the system level of charging and discharging. The controller will be a part of the smart charger and uses both of the battery state of charge (SOC) and (SI) as input variables. Based on the controller output, the interface converter of each PHEV decides the desired level of charging.

The power conversion from variable AC voltage into a desired AC voltage with fixed magnitude and fixed frequency is attracting more attention especially in case of grid-connected wind generation system. Indeed, the Direct Matrix Converter (DMC) can be used as a suitable solution for such AC/AC conversion to fulfill the requirement of the output voltage with desired magnitude and frequency. This paper introduce Model Predictive control for multiphase, more than three phases, seven-to-three Direct Matrix Converter (DMC). This system is a step towards using multiphase generation, especially in wind generation. Seven-to-three direct matrix converter is used to convert seven-phase input to three phase output. Model predictive control is used in this paper control the three phase output current injected into the grid to be sinusoidal and in phase with the grid voltage. Current injected into the grid has a very low Total Harmonic Distortion (THD). The proposed system is simulated using MatLab/Simulink Simulation to verify its operation.

Power system stabilizers (PSSs) are added to excitation system to enhance the damping during low frequency oscillations. This paper presents a study of fuzzy logic power system stabilizer (PSS) for stability enhancement of a single machine power system. In order to accomplish the stability enhancement, two approaches to design Fuzzy Logic Power System Stabilizers (FLPSS) as follows: 1-FLC 1 has Speed deviation and power angle as its input Δω and ΔPa. 2-FLC 2 has Speed deviation and Acceleration as its input Δω and Δδ. The performance of these two approaches is compared and is compared with the system without PSS. The stabilizing signals were computed using the fuzzy membership functions depending on these variables. The simulations were tested under different operating conditions, also tested with different membership functions. The simulation results are quite encouraging and satisfactory.