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The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature. [ABSTRACT FROM AUTHOR]

Drilling bits are essential downhole hardware that facilitates drilling operations in high-pressure, high-temperature regions and in most carbonate reservoirs in the world. While the drilling process can be optimized, drilling operators and engineers become curious about how drill bits react during rock breaking and penetration. Since it is experimentally expensive to determine, the goal of the study is to maximize the rate of penetration by modeling fluid interactions around the roller cone drilling bit (RCDB), specifying a suitable number of jet nozzles and venturi effects for non-Newtonian fluids (synthetic-based muds), and examining the effects of mud particles and drill cuttings. Ansys Fluent k-epsilon turbulence viscous model, a second order upwind for momentum, turbulent kinetic energy, and dissipation rate, were used to model the specified 1000 kg/m3 non-Newtonian fluid around the roller cone drill bit. The original geometry of the nozzles was adapted from a Chinese manufacturer whose tricone had three jet nozzles. The results of our six redesigned jet nozzles (3 outer, 39.12 mm, and 3 proximal, 20 mm) sought to offer maximum potential for drilling optimization. However, at a pressure of 9.39 × 104 Pa, the wellbore with particle sizes between 0.10 mm and 4.2 mm drill cuttings observed an improved rate of penetration with a rotation speed of 150 r/min. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms. [ABSTRACT FROM AUTHOR]

The increasing penetration of Distributed Generators (D.G.) into the existing power system has brought some real challenges regarding the transient response of electrical systems. The injection of D.G.s and abrupt load changes may cause massive power, current, and voltage overshoots/undershoots, which consequently affects the equilibrium of the existing power system and deteriorate the performance of the connected electrical appliances. A robust and intelligent control strategy is of utmost importance to cope with these issues and enhance the penetration level of D.G.s into the existing power system. This paper presents a Modified Particle Swarm Optimization (MPSO) algorithm-based intelligent controller for attaining a desired power-sharing ratio between the M.G. and the main grid with an optimal transient response in a grid-tied Microgrid (M.G.) system. The proposed MPSO algorithm includes an additional parameter named best neighbor particles (rbest) in the velocity updating equation to convey additional information to every individual particle about all its neighbor particles, consequently leading to the increased exploration capability of the algorithm. The MPSO algorithm optimizes P.I. parameters for transient and steady-state response improvement of the studied M.G. system. The main dynamic response evaluation parameters are the overshoot and settling time for active and reactive power during the D.G. connection and load change. Furthermore, the performance of the proposed controller is compared with the PI-PSO-based MG controller, which validates the effectiveness of the proposed M.G. control scheme in maintaining the required active and reactive power under different operating conditions with minimum possible overshoot and settling time. [ABSTRACT FROM AUTHOR]

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented. [ABSTRACT FROM AUTHOR]

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor's living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper. [ABSTRACT FROM AUTHOR]

The development of single-phase symmetrical bipolar voltage gain matrix converters (MC) is growing rapidly as they find their application in power systems for dynamic restoration of line voltages, high voltage AC–DC converters, and variable frequency controllers for many industrial processes. However, the existing trend in matrix converter technology is a buck–boost operation that has inherently serious issues of high voltage and current surges or stresses. This is a big source of the high voltage and current rating of semiconductor switching devices. There is also a problem of high ripples both for voltage as well for current, requiring large size of filtering capacitors and inductors. The non-symmetrical control of the voltage gain increases the control complication. A large count of operating transistors is critical regarding their cost, size, and power conversion losses, as the space and cost required by their gate control circuits are much larger than the size and cost of the switching transistors. Thus, in this research work, a new single-phase MC is introduced only employing six fully controlled switching devices, ensuring similar operation or outputs as is obtained from the existing topologies that require the use of eight or more fully controlled switching devices, and the reduction by two or more switching transistors helps to compact the overall size and lower the overall cost. The separation in its voltage buck and boost operation enables smooth control of the voltage gain through duty cycle control. The low values of the voltage and current surges reduce the power rating and losses of the switching devices. The flow of the current in the filtering inductor is kept unidirectional to avoid the current interruption and reversal problem once the operation of the converter is abruptly switched from inverting to non-inverting and vice versa. All these factors are comprehensively detailed through the circuit's description and comparative analysis. Simulation and practical results are presented to confirm the effectiveness of the developed circuit topology. [ABSTRACT FROM AUTHOR]

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms' search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem. [ABSTRACT FROM AUTHOR]

In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data. [ABSTRACT FROM AUTHOR]

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics. [ABSTRACT FROM AUTHOR]

This scientific paper aims to increase the voltage source converter (VSC) control efficiency in a multi-terminal high voltage direct current (MTDC) network during dynamic operations. In the proposed study, the Mayfly algorithm (MA) is used to modify the control parameters of VSC stations. Traditional strategies that modify VSC control settings using approximate linear models fail to produce optimal results because VSCs are nonlinear characteristics of the MTDC system. Particle swarm optimization (PSO) may produce optimal outcomes, but it is prone to becoming stuck in a local optimum. To modify the proportional-integral (P.I.) control parameters of the VSC station, the Mayfly algorithm, a modified form of PSO, is used. The suggested algorithm's objective function simultaneously optimizes both the outer and inner control layers. A four-terminal MTDC test system is developed in PSCAD/EMTDC to assess the benefits of the proposed algorithm. For VSCs, a comparison of classical, PSO, and proposed MA-based tuned parameters is carried out. The integral of time multiplied by absolute error (ITAE) criterion is used to compare the performance of classical, PSO, and a proposed algorithm for VSC controller parameters/gains. With an ITAE value of 6.8521 × 10−6, the MA-based proposed algorithm computes the optimal values and outperforms its predecessor to adjust the VSCs controller gains. For (i) wind farm power variation, (ii) AC grid load demand variation, and (iii) ultimate permanent VSC disconnection, steady-state and dynamic performances are evaluated. According to the results, the proposed algorithm based MTDC system performs well during transients. [ABSTRACT FROM AUTHOR]

This paper studies the problem of fault detection for continuous time linear switched systems in the presence of disturbance. For this purpose, a fault detection sliding mode observer approach is designed to generate the residual signal. To minimize the effect of disturbance from the residual, the problem is formulated into H∞ filtering technique to increase more robustness. To deal with the issue of the switched systems stability, the Lyapunov-Krasovskii functional method is utilized along with average dwell time, and linear matrix inequalities are formulated to derive the sufficient conditions. The residual signal is evaluated, and an adaptive threshold is computed for both modes of the switched system. Finally, a simulation example for a case study of boost converter and a numerical example with both abrupt and incipient faults are illustrated to prove the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Variable voltage and frequency are required to govern the torque-speed characteristics of many industrial drive systems. Traditionally, this is achieved with a power converting system implemented with multistage converters. This technology is based on rectifying AC power into DC and then DC into AC with an inverter circuit. The power quality concerns of both conversion stages are tackled by selecting high switching frequency PWM control and harmonics mitigation filters. Also, using a bulky DC-link capacitor is one of the big sources of low system reliability, so this approach increases the conversion losses, circuit, and control complications. The frequency step-down conversion is very attractive with direct AC-AC converters as it has a simple control and circuit structure, but these converters face poor power quality challenges once the output frequency is decreased with respect to an input. In these converters, the total harmonic distortion (THD) of the output voltage becomes very poor once the output frequency is reduced. The problem of high THD of the output is addressed in the power converting circuits implemented with line frequency multi-winding transformers. The required number of output winding and switching devices (diodes and thyristors) increases once the value of the output frequency is decreased. This will increase the overall volume, cost, and losses. The use of a bulky and costly line frequency transformer may be eliminated if AC voltage controllers have non-inverted and inverted voltage buck capabilities, such existing topologies either have complex control schemes or require a large number of operating devices. Therefore, in this research article, a new transformerless frequency step-down converter employing fewer devices is proposed. This approach is realized with a high-frequency controlled rectifier for the required voltage stabilization and a low-frequency inverter bridge for frequency control. Its validation is supported by the results attained from Simulink and practical-based prototypes. [ABSTRACT FROM AUTHOR]

One of the technical challenges that needs to be addressed for the future of the multi-terminal high voltage direct current (M-HVDC) grid is DC fault isolation. In this regard, HVDC circuit breakers (DCCBs), particularly hybrid circuit breakers (H-DCCBs), are paramount. The H-DCCB, proposed by the ABB, has the potential to ensure a reliable and safer grid operation, mainly due to its millisecond-level current interruption capability and lower on-state losses as compared to electromechanical and solid-state based DCCBs. This paper aims to study and evaluate the operational parameters, e.g., electrical, and thermal stresses on the IGBT valves and energy absorbed by the surge arrestors within H-DCCB during different DC fault scenarios. A comprehensive set of modeling requirements matching with operational conditions are developed. A meshed four-terminal HVDC test bench consisting of twelve H-DCCBs is designed in PSCAD/EMTDC to study the impacts of the M-HVDC grid on the operational parameters of H-DCCB. Thus, the system under study is tested for different current interruption scenarios under a (i) low impedance fault current and (ii) high impedance fault current. Both grid-level and self-level protection strategies are implemented for each type of DC fault. [ABSTRACT FROM AUTHOR]

Nanofluids as an EOR technique are reported to enhance oil recoveries. Among all the nanomaterial silica with promising lab results, economic and environmental acceptability are an ideal material for future applications. Despite the potential to enhance recoveries, understanding the two-fold impact of parameters such as concentration, salinity, stability, injection rate, and irreproducibility of results has arisen ambiguities that have delayed field applications. This integrated study is conducted to ascertain two-fold impacts of concentration and salinity on recovery and stability and evaluates corresponding changes in the recovery mechanism with variance in the parameters. Initially, silica nanofluids' recovery potential was evaluated by tertiary flooding at different concentrations (0.02, 0.05, 0.07, 0.1) wt. % at 20,000 ppm salinity. The optimum concentration of 0.05 wt. % with the highest potential in terms of recovery, wettability change, and IFT reduction was selected. Then nano-flooding was carried out at higher salinities at a nanomaterial concentration of 0.05 wt. %. For the mechanism's evaluation, the contact angle, IFT and porosity reduction, along with differential profile changes were analyzed. The recovery potential was found at its highest for 0.05 wt. %, which reduced when concentrations were further increased as the recovery mechanisms changed and compromised stability. Whereas salinity also had a two-fold impact with salinity at 30,000 ppm resulting in lower recovery, higher salinity destabilized the solution but enhanced recoveries by enhancing macroscopic mechanisms of pore throat plugging. [ABSTRACT FROM AUTHOR]

The concept of hybrid high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) grid systems brings a massive advantage to reduce AC line loading, increased utilization of network infrastructure, and lower operational costs. However, it comes with issues, such as integration challenges, control strategies, optimization control, and security. The combined objectives in hybrid HVAC–HVDC grids are to achieve the fast regulation of DC voltage and frequency, optimal power flow, and stable operation during normal and abnormal conditions. The rise in hybrid HVAC–HVDC grids and associated issues are reviewed in this study along with state-of-the-art literature and developments that focus on modeling robust droop control, load frequency control, and DC voltage regulation techniques. The definitions, characteristics, and classifications of key issues are introduced. The paper summaries the key insights of hybrid HVAC–HVDC grids, current developments, and future research directions and prospects, which have led to the evolution of this field. Therefore, the motivation, novelty, and the main contribution of the survey is to comprehensively analyze the integration challenges, implemented control algorithms, employed optimization algorithms, and major security challenges of hybrid HVAC–HVDC systems. Moreover, future research prospects are identified, such as security algorithms' constraints, dynamic contingency modeling, and cost-effective and reliable operation. [ABSTRACT FROM AUTHOR]

Most of the traditional PID tuning methods are heuristic in nature. The heuristic approach-based tuned PID controllers show only nominal performance. In addition, in the case of a digital redesign approach, mapping of the heuristically-designed continuous-time PID controllers into discrete-time PID controllers and in case of the direct digital design approach, mapping of the continuous-time plant (forward converter) into the discrete-time plant, results in frequency distortion (or warping). Besides this, nonlinear elements such as ADC and DAC, and delay in the digital control loop deteriorate the control performance. There is a need to tune conventionally-designed digital controllers to enhance performance. This paper proposes optimized discrete-time PID controllers for a forward DC–DC converter operating in continuous conduction mode (CCM). The considered conventional digital PID controllers designed on the basis of the digital redesign and direct digital approaches are tuned by one of the multivariable unconstrained pattern search methods named Hooke–Jeeves (H–J) search method to ensure excellent output voltage regulation performance against the changes in input voltage and load current. Numerical results show that the H–J-based optimized PID compensated forward converter system shows tremendous improvement in performance compared to its unoptimized counterpart and simulated annealing (SA)-based compensated system, thus justifying the applicability of the H–J method for enhancing the performance. [ABSTRACT FROM AUTHOR]

One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA). [ABSTRACT FROM AUTHOR]

The aim of this research is to perform an in-depth performance comparison of ground-mounted and rooftop photovoltaic (PV) systems. The PV modules are tilted to receive maximum solar irradiance. The efficiency of the PV system decreases due to the mutual shading impact of parallel tilted PV modules. The mutual shading decreases with the increasing interrow distance of parallel PV modules, but a distance that is too large causes an increase in land cost in the case of ground-mounted configuration and a decrease in roof surface shading in the case of rooftop configuration, because larger sections of roof are exposed to sun radiation. Therefore, an optimized interrow distance for the two PV configurations is determined with the aim being to minimize the levelized cost of energy (LCoE) and maximize the energy yield. The model of the building is simulated in EnergyPlus software to determine the cooling load requirement and roof surface temperatures under different shading scenarios. The layout of the rooftop PV system is designed in Helioscope software. A detailed comparison of the two systems is carried out based on energy output, performance ratio, capacity utilization factor (CUF), energy yield, and LCoE. Compared to ground-mounted configuration, the rooftop PV configuration results in a 2.9% increase in CUF, and up to a 23.7% decrease in LCoE. The results of this research show that installing a PV system on a roof has many distinct advantages over ground-mounted PV systems such as the shading of the roof, which leads to the curtailment of the cooling energy requirements of the buildings in hot regions and land cost savings, especially for urban environments. [ABSTRACT FROM AUTHOR]

Due to the impediments of voltage source inverter and current source inverter, Z-Source Inverter (ZSI) has become notorious for better power quality in low and medium power applications. Several modifications are proposed for impedance source in the form of Quasi Z-Source Inverter (QZSI) and Neutral Point Clamped Z-Source Inverter (NPCZSI). However, due to the discontinuity of the source current, NPCZSI is not suitable for some applications, i.e., fuel cell, UPS, and hybrid electric vehicles. Although in later advancements, source current becomes continuous in multilevel QZSI, low voltage gain, higher shoot-through duty ratio, lesser availability of modulation index, and higher voltage stress across switches are still an obstacle in NPCZSI. In this research work, a three-level high voltage gain Neutral Point Clamped Inverter (NPCI) that gives three-level AC output in a single stage, is proposed to boost up the DC voltage at the desired level. At the same time, it detains all the merits of previous topologies of three-level NPCZSI/QZSI. Simulations have been done in the MATLAB/Simulink environment to show the effectiveness of the proposed inverter topology. [ABSTRACT FROM AUTHOR]

In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company. [ABSTRACT FROM AUTHOR]

Locating and planning charging stations for Low-Emission Vehicles (LEVs) such as Battery Electric Vehicle (BEV), Hydrogen Fuel-Cell Vehicle (HFCV), and Natural Gas Vehicle (NGV) are becoming increasingly important for LEV users, government, and the automobile industry. Conventional planning approach of charging station usually plans single functional charging station that can only serve one kind of LEVs, and other factors such as fuel type, driving range, initial fuel tank level, and refueling time of the LEV are less considered in the planning stage. In this article, we propose a bi-level planning model to locate and size Multi-Functional Charging Station (MFCS) which can recharge BEV, HFCV, and NGV at the same time in a medium-sized city with different functional areas (e.g., residential area, industrial area, CBD area). We also established a method for generating a daily route considering vehicle attributes and user habits, and we loaded these traveling data into the upper model to select a set of optimal combinations of refueling station locations with a relatively high success ratio. In the lower model, we introduced the mathematical relationship between number of chargers and average user waiting time, and set the total social cost factor, including investment cost and waiting time cost, to evaluate each optimal combination, and then identified the optimum locational result and defined the size of each station. In the case study, we verify the proposed model in several scenarios and conclude that multifunctional refueling station performs better in terms of investment cost and users' satisfaction level. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs. [ABSTRACT FROM AUTHOR]

Although an analytical design approach-based digital controller—which is essentially a deadbeat controller—shows zero steady-state error and no intersampling oscillations, it takes a finite number of sampling periods to settle down to a steady-state value. This paper describes the application of a derivative-free Nelder–Mead (N–M) simplex method to the digital controller for retuning of its coefficients intelligently to ensure improved settling and rise times without disturbing the deadbeat controller characteristics (i.e., no ripples between the sampling periods and no steady-state error). A switching-mode buck regulator working at 1 MHz in continuous conduction mode (CCM) is considered as a plant. Numerical simulation results depict that the N–M algorithm-based optimized digital controller not only shows improved steady-state and transient performance but also guarantees rigorous robustness against model uncertainty and disturbance as compared to its traditional counterpart, as well as the other optimized digital controller fine-tuned through other derivative-free metaheuristic optimization techniques, such as the genetic algorithm (GA). A system generator-based hardware software co-simulation is also performed to validate the simulation results. [ABSTRACT FROM AUTHOR]

In this research, a new single-phase direct AC-to-AC converter, operating in buck and boost mode, with a bipolar voltage gain, is proposed. The operation is accomplished through high frequency direct and indirect PWM control of a single switch with low voltage stresses. This reduces, not only the control effort, but also the switching losses. The low voltage stresses across the high frequency switches, reduce the dv/dt problem significantly without any loss and bulky voltage snubber arrangement. The operation, in its all-operating modes, has a low inductor ripple current and switching current. The proposed converter may be employed as an AC voltage restorer in a power distribution system to cope with the voltage sag and swell issues. The detailed analysis of the proposed converter is carried out in order to compare its performance with the existing converters. The simulation results obtained using the MATLAB/Simulink environment are verified through experimental results. [ABSTRACT FROM AUTHOR]