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Changes are emerging that will significantly alter structure and operation of this century's distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. [ABSTRACT FROM AUTHOR]

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

Accurate parameter identification plays an integral role in the modeling of an optimized solar module which in turn helps in the error-free prediction of its output. Five important parameters: the photoelectric current (Iph), ideality factor(α), saturation current (Io), series resistance (Rs), and shunt resistance (Rsh) are required for the accurate modeling of the PV cells/modules and need to be extracted as these parameters are not provided by the manufacturer in the datasheet. This paper proposes a new metaheuristic jellyfish optimization (JFO) algorithm for the parameter extraction of a solar module. The JFO algorithm achieves the optimal solution without being trapped in local solutions in less time. The parameter extraction using the JFO algorithm is done on two different solar modules i.e., Soltech-1STH-215P and PWP-201. The results are compared in terms of extracted parameters (Iph, α, Io, Rs, and Rsh) with the well-known optimization techniques like PSO, GA, and others available in the literature and with the manufacturer I–V and P–V characteristics. The proposed technique I–V and P–V characteristics are validated at different environmental conditions and are found to be similar to that of PSO and GA. It is also observed that the extracted parameters obtained using JFO are comparable with the other twenty-two techniques, and the proposed technique is one of the highly efficient techniques that can be utilized for parameter extraction of PV modules and to predict solar cell characteristics for all commercial modules without setting up any experimental measurements. MATLAB/simulation software is used for implementation and performance validation. [ABSTRACT FROM AUTHOR]

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function's preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system's performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation's results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods. [ABSTRACT FROM AUTHOR]

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double‐diode model, the computation time of the chaotic variants (CTSA1‐1.1885, CTSA2‐1.2502, CTSA3‐1.2962, CTSA4‐1.1204, and CTSA5‐1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]‐17.4532, tunicate swarm algorithm (TSA)‐2.5278, Harris hawks optimization [HHO]‐10.8942, ant lion optimizer [ALO]‐15.0214, and atom search optimization [ASO]‐9.5214). For the triple‐diode model, the computation time of the chaotic variants (CTSA1‐1.2658, CTSA2‐1.3520, CTSA3‐1.3056, CTSA4‐1.2290, and CTSA5‐1.3859) is better than the rest of the compared algorithms (PSO‐18.5421, TSA‐3.2568, HHO‐9.8754, ALO‐13.5841, and ASO‐11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation. [ABSTRACT FROM AUTHOR]

Summary: Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID‐fuzzy‐PID (FO‐hPID‐FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO‐MSCA (hPSO‐MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area‐1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO‐hPID‐FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID‐FPID and FO‐hPID‐FPID controllers with FO‐hPID‐FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one's language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. "Besieged City" is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain's thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel "Besieged City" is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains. [ABSTRACT FROM AUTHOR]

This paper proposes parameter-free two best-worst optimizers (BWOs) that combine the searching capabilities of Jaya and Rao-1 algorithms to intensify their exploration and exploitation capabilities. For the proposed first optimizer, BWO-1, parallel taxonomy has been adopted for the Jaya and Rao-1 combination to obtain dual sets of updated solutions for a given current solution set. For the proposed second optimizer, BWO-2, along with the parallel taxonomy, a subloop of random scaling factors has been introduced in the solution updating mechanism of both standard Jaya and Rao-1 algorithms to generate multiple sets of updated solutions in a single iteration. The best solutions from obtained multiple solution-sets will survive only and lead to the next iteration. Hence, the proposed solution updating mechanisms for BWO-1 and BWO-2 increase the probability of getting a quality solution under set conditions. The ability and scalability of BWO-1 and BWO-2 were assessed, solving the optimization problem of power loss reduction and voltage deviation minimization in the IEEE 33-bus and 69-bus test systems. The results show that the BWO-2 exhibited a lead of 2.97%, 1.83%, and 0.72% over the Rao-1, Jaya, and BWO-1 techniques, respectively. Besides, the BWO-2 achieves up to 38.76% more reduction in power losses against the existing standard, improved, and hybrid optimization techniques. [ABSTRACT FROM AUTHOR]

The traditional fault location methods are not effective for the multisource active distribution network and are difficult to meet both requirements of timeliness and fault tolerance simultaneously. In this paper, a fault location method for active distribution networks which combines both improved matrix algorithm and optimization algorithm is proposed. The improved matrix algorithm is constructed to determine the fault section hypothesis by using the causal relationship between the alarm information and the fault section. Subsequently, network splitting is used for causal verification to check whether the alarm information is distorted. If the alarm information is normal, the fault sections can be quickly located by the above matrix algorithm. If the alarm information is distorted, the optimization model is constructed by using the fault section hypothesis selected by the matrix algorithm. The discrete particle swarm optimization (DPSO) algorithm is used to solve this optimization model, which can accurately locate the real fault sections. Hence, the advantages of both matrix algorithm and optimization algorithm are complementary. Simulation results show that the proposed method has the advantages of both high timeliness and fault tolerance in a complex active distribution network. [ABSTRACT FROM AUTHOR]

Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks' intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs. [ABSTRACT FROM AUTHOR]

This paper considers the problem of tracking the global maximum power point (GMPP) in partially shaded conditions (PSCs) as a multiobjective optimization problem and solves it using a novel multiobjective optimization algorithm on the basis of Bayesian optimization formulation. Bayesian optimization is a metamodel-based global optimization method that is able to balance exploration and exploitation. The Pareto solutions are obtained by using a multiobjective Bayesian optimization algorithm. Also, a new acquisition function is proposed to improve the diversity and convergence of the Pareto solutions. Two objective functions are introduced to remove the large tracking errors and oscillations of the operating point around the GMPP. The suggested method is implemented online for GMPP tracking so that the suggested method monitors any change in environmental conditions and generates the optimal duty cycle for the DC-DC converter for the GMPP tracking (GMPPT) by the PV array. Several multipeak PSC scenarios are implemented and simulated to show efficiency of the suggested approach. The MATLAB/SIMULINK is employed to implement a photovoltaic (PV) system comprising a PV array, a boost converter, and the proposed multiobjective Bayesian optimization algorithm (MOBOA). The simulation results show a very satisfactory performance of the MOBOA in terms of transient state and steady-state oscillations and tracking speed. [ABSTRACT FROM AUTHOR]

Summary: In this paper, model predictive control (MPC) of four leg distribution static compensator (FL‐DSTATCOM) is proposed to compensate the power quality issues and to reduce the higher neutral leg switching frequency of FL‐DSTATCOM. Current control using conventional MPC, selects a switching state which will minimize the single constraint cost function, comprising the difference between reference and actual DSTATCOM currents. However, FL‐DSTATCOM has a limitation of higher neutral leg switching frequency, and it can be reduced by adding an additional constraint to the cost function using a weighting factor. The reduction in neutral leg switching frequency also reduces the switching frequency of phase leg switches. But, the selection of weighting factor during multiple constraint case is a cumbersome task. Therefore, to simplify the weighting factor selection process, an optimization technique, namely, the technique for order of preference by similarity to ideal solution (TOPSIS) is applied, which further selects the optimal switching state. At the same time, compensation using FL‐DSTATCOM mainly depends on reference current extraction and it is a challenging task during unbalance and distortions in supply voltages. To address this problem, an improved conductance factor‐based approach is implemented in this paper, which makes reference current extraction simpler, effective and easy to interpret. The efficacy of the proposed method is evaluated using simulation and experimental studies. [ABSTRACT FROM AUTHOR]

Summary: Energy management of the grid connected microgrid (MG) systems with low cost using a novel hybrid algorithm is implemented in this paper. The novel proposed hybrid technique is the combined performance of both the Random Forest (RF) and Coral Reefs Optimization (CRO) algorithm and in this way it is named as RFCRO. Optimum operation of micro‐sources for decreasing the electricity production cost by hourly day‐ahead and real‐time scheduling is the main objective of the paper. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG‐like wind turbine, photovoltaic array, battery, and fuel cell with corresponding cost functions, the RF is employed to predict the load demand. CRO clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like BFOANN, ALO, GOAPSNN, and RBFNN‐SSA technique. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem. [ABSTRACT FROM AUTHOR]

This paper presents a problem of simultaneous maximization of GenCo's (Generation Company) revenue and minimization of ISO's (Independent System Operator) purchase cost in interconnected power markets having different designs, namely capacity‐plus‐energy market and energy‐only market. Both these problems have solved by multi‐objective optimizers such as multi‐objective particle swarm optimization (MOPSO) and non‐dominated sorting genetic algorithm‐II (NSGA‐II) using market data available in the literature. MOPSO and NSGA‐II are meta‐heuristic optimization algorithms, which are used for several engineering applications to optimize multiple objectives simultaneously. The paper illustrates a comparative analysis of results with optimum allocation and pricing under different market conditions such as cost of recall, probability of recall and load forecasting (L. F.) error etc. Furthermore, main objective to emphasize on GenCo's total revenue maximization as well as ISO's purchase cost minimization instead of capacity cost only. Multi‐objective optimization techniques have used to optimize both objectives simultaneously. Existing literature does not discuss the simultaneous solution of these problems by multi‐objective optimization techniques, which makes this work novel. It has been observed that the capacity price, the maximum value of GenCo's revenue and the minimum value of ISO's purchase increases at a different rate, with increasing the probability of recall, cost of recall and L. F. error. GenCo's revenue and ISO's purchase cost have profoundly affected by the L. F. error, moderately affected by the probability of recall and very lowly affected by the cost of recall. [ABSTRACT FROM AUTHOR]

In this paper, it is proposed to apply the mayfly optimization algorithm (MOA) to perform the coordinated and simultaneous tuning of the parameters of supplementary damping controllers, i.e., power system stabilizer (PSS) and power oscillation damping (POD), that actuate together with the automatic voltage regulators of the synchronous generators and the static synchronous series compensator (SSSC), respectively, for damping low-frequency oscillations in power systems. The performance of the MOA is compared with the performances of the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm for solving this problem. The dynamics of the power system is represented using the current sensitivity model, and, because of that, a current injections model is proposed for the SSSC, which uses proportional-integral (PI) controllers and the residues of the current injections at the buses, obtained from the Newton–Raphson method. Tests were carried out using the New England system and the two-area symmetrical system. Both static and dynamic analyses of the operation of the SSSC were performed. To validate the proposed optimization techniques, two sets of tests were conducted: first, with the purpose of verifying the performance of the most effective algorithm for tuning the parameters of PSSs, PI, and POD controllers, and second, with the purpose of performing studies focused on small-signal stability. The results have validated the current injections model for the SSSC, as well as have indicated the superior performance of the MOA for solving the problem, accrediting it as a powerful tool for small-signal stability studies in power systems. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method's performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power. [ABSTRACT FROM AUTHOR]

Accurate analysis of load angle stability of the synchronous generator depends on considering mechanical parameters in addition to electrical parameters in synchronous generator modeling, which leads to increasing state-space variables and model complexity and ultimately reduces the accuracy and performance of conventional controllers. Therefore, an intelligent controller is required for the stable and reliable operation of the synchronous load angle generator. With this approach in this paper, first considering the electrical and mechanical parameters, a complete 14-order model of the synchronous generator is obtained. Then, a PID controller based on the Harris optimization algorithm is proposed to stabilize the load angle of the synchronous generator. The HHO optimization algorithm is a population-based optimization technique that optimizes PID controller coefficients. The effectiveness of the new HHO technique in optimizing the controller PID coefficients over known optimization techniques such as GA-PID and Modified GA-PID has been confirmed using simulation results. The simulation results show that the proposed controller has a higher performance in load angle stability compared to the sliding mode and fuzzy-PID controllers. [ABSTRACT FROM AUTHOR]

The continual increase in the power network topological structure and long distances between load‐generation sources lead to modern powers systems are operated near theirs stability margins. For this issue, reliable and secure operations of electric devices through inter‐area oscillations remain an essential issue in the system dynamic stability. In the case of improper actions of controlling devices, the severe inter‐area interactions may lead to wide‐area instabilities and large blackouts. Using wide‐area measurement system (WAMS), remote signals are transferred through communications links with different inherent time latencies. In the case of large time delays, the main controllers cannot work appropriately which leads to improper performance of damping controllers through severe inter‐area fault event scenarios. For this issue, this paper presents an adaptive wide‐area damping controller (AWADC) for damping inter‐area oscillations in large power systems equipped with WAMS technology consisting of various time latencies. In this case, based on the IEEE C37.118.1 protocol, the generator buses are equipped with phasor measurement unit devices. Also, owing to the different communication links, the measured global signals (GSs) are involved within different time latencies. In this case, at each time window, considering time delay compensator (TDC) technology provided through Simevents discrete event tool, the overall time delay is evaluated and compensated. For identifying the best controlling GSs, considering two observability and controllability concepts, the system dynamic signals are evaluated which the best input decisioning signals and corresponding output controlling signals are provided. The proposed AWADC scheme consists of different compensating blocks which are adjusted through simple and well‐known Jaya optimization algorithm (JOA) through online evaluations. The structure of the proposed AWADC controlling scheme is carried out on a modified 39‐bus New England test system consisting of large synchronous generators whose damping performances are evaluated through severe fault events with the potential of unstable inter‐area oscillations. Simulation results indicate the effectiveness of developed TDC and JOA techniques to provide proper controlling signals through the AWADC controller in the presence of different time latencies. [ABSTRACT FROM AUTHOR]

This paper proposes a maiden application of the fractional‐order proportional‐integral‐derivative plus the second‐order derivative controller called FOPIDD2 (PIλDμD2) to achieve better transient response at a terminal voltage of automatic voltage regulator (AVR). Multi‐verse optimizer (MVO) algorithm is used for tuning six optimization parameters of controller. MVO is a powerful optimization algorithm with great convergence speed and strong search mechanisms for an expanded number of decisions. The proposed FOPIDD2 controller is compared with proportional‐integral‐derivative (PID), PID plus second‐order derivative (PIDD2), fractional order PID (FOPID) and filtered FOPID controllers obtained using different algorithms in both time domain and frequency domain. According to the results, FOPIDD2 controller shows superior performance than compared studies in terms of transient response of terminal voltage of AVR. Additionally, frequency domain analysis shows that FOPIDD2 controller presents satisfactory results. Finally, the system is subjected to a robustness test with changes in time constants and gain constants values. AVR system with FOPIDD2 controller performs better in perturbed system parameters than compared controllers. [ABSTRACT FROM AUTHOR]

This paper discusses and analyses the work done in developing a Nature‐inspired optimization algorithm in a solar PV system for tracking the global maximum power point (GMPP) in partial shaded condition (PSC). Partial shading is a situation where PV panels connected in series do not receive the same solar radiation, thus exhibiting different I‐V characteristics for the individual panel. Under PSC, solar PV system output exhibits multiple local maximum power point (MPP) and a unique global MPP on the Power‐Voltage curve. Conventional maximum power point tracking (MPPT) techniques such as Perturb and Observe (P&O), incremental conductance (IC), and Hill Climbing (HC) method can only track MPP under uniform insolation conditions. It may fail to track GMPP in case of PSC and get trapped in local MPP, thereby causing loss of power that could have been tapped if the operation would have been at GMPP. To overcome the problem posed by PSC, researchers are applying the nature‐inspired optimization algorithm for tracking GMPP under partial shading conditions with fairly good results. Nature‐inspired optimization algorithm offers the advantage of solving multivariable nonlinear objective functions with constraints by exploiting the search space and can converge quickly to GMPP. Some of the most recent and famous nature‐inspired algorithms have been discussed and compared here. Simulation and hardware results of some of them are also taken in order to compare them effectively. Simulation results show that the Most Valuable Player Algorithm (MVPA) takes the least time to track the MPP in all the cases, and the MPP tracked is also comparable to the maximum while PSO with differentially perturbed velocity (PSO_DV) takes the maximum time to track the MPP in all cases. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control topology for a multilevel inverter with reduced number of power switches for achieving an increased number of levels in output voltage along with improved power quality. The diode clamped multilevel inverter configuration is modified and the gating signals are generated using selective harmonic optimization technique at fundamental frequency using Satin Bower Bird optimization (SBO). It enhances the quality of output voltage waveform with the reduction in total harmonic distortion (THD), less switching power losses, and better efficiency. The inverter is investigated for its competent operation capability on grid‐tied applications. Detailed simulation is conducted and results are presented for various load conditions. To validate the inverter operation, an experimental setup of the modified diode clamped modular inverter (MDCMI) interfacing the grid is presented. The loss analysis is carried out at the inverter and converter segments. Based on the analysis, the proposed control topology demonstrates the reliable and efficient operation of the modular inverter. From the results obtained it is evident that the proposed inverter design suits superlative for the grid applications. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the allocation of a hybrid system that includes PV‐DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV‐DG and DSTATCOM. The considered objective function is a multi‐objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two‐hybrid system on a large 94‐bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single‐hybrid PV‐DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two‐hybrid systems is superior compared with the inclusion of a single‐hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a multi‐objective coyote optimization algorithm (MOCOA) is proposed for techno‐economic benefits of simultaneous placement of fault current limiters (FCLs) and distributed generation (DGs) units in distribution grids. The proposed MOCOA is employed for mitigating the loss of power in distribution lines, minimizing the fault current levels, minimizing the economic costs of FCL size, enhancing the total voltage stability index, and reducing the total voltage deviation. The MOCOA is enhanced by emerging the concept of pareto‐optimal dominance to store the distinguished solutions in each generation. Finally, TOPSIS, a technique is implemented to find the compromise solution to fulfill the operator requirements. Applications are carried out in three distribution systems. The simulation results show the great effectiveness of the proposed MOCOA in acquiring several alternatives with appropriate technical improvements in power losses, voltage stability, fault level reduction, and economical FCL size. [ABSTRACT FROM AUTHOR]

Summary: The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all‐day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor. In this paper, the minimisation of power generation cost of the thermal power units is achieved by incorporating renewable sources, says hydro, winds, and solar plants for 24 hours scheduled, and ATC calculation is the prime objective. In recent literature, the Mayfly algorithm (MA) optimisation approach, which combines the advantages of evolutionary algorithms and swarms intelligence to attend better results, is successfully implemented. In this article, optimum power flow (OPF) based ATC is enforced under various conditions with hydro‐thermal‐solar‐wind scheduling concept on the IEEE 9, IEEE 39, and IEEE 118 test bus systems to check the performance of the proposed chaotic MA. The chaotic MA is a hybridised format of the MA and chaotic map (C‐MAP) method with opposition based learning. It is noted from the simulation study that the suggested hybrid C‐MAP approach has a dominant nature over other well‐established optimisation algorithms. In case of single objective function, the optimum value of the cost function is better than 13% to its nearest competitor approach. For multi‐objective, it is improved by more than 19% and ATC value is enhanced by near about 56% and more. [ABSTRACT FROM AUTHOR]

Summary: With the advent of rapid advancements in the energy storage technologies and in harnessing the renewable resources, a variety of mix of "Problem formulation techniques" and "Solution methodologies" have emerged for scheduling the power generation in Grid integrated environment. Multiple constituent attributes and their combinations thereof define the nature of Unit Commitment problem formulation in a complex way. An attempt has been made in this paper to consolidate the available research efforts, spanning over about past two decades, so as to unravel the rising complexities in developing the unit commitment formulation in this rapidly evolving green energy era. The prevailing unit commitment models have been thoroughly reviewed on the bases of deployment of different combinations of features like objective functions, constraints, optimization techniques, types of renewable generation, and types of storage. This review work presents, in a categorized manner, the viewpoints on problem formulation as well as on the solution methodologies. Further, a novel Unit Commitment model has been proposed by the authors wherein modeling details of each of them have been presented in a conscientious manner. The proposed model is unique as the battery charge/ discharge decision making has been included as a co‐optimization problem with the existing Unit Commitment problem. The superiority of the proposed model has also been reported. [ABSTRACT FROM AUTHOR]

Summary: Hydro power plants are in general more stable to the load demands and thus the change in frequency can be easily controlled. Whereas in multi‐source interconnected power system the stability of the system is marginal. In certain conditions the system is also unmeasurable to implement the load frequency control. Linear Quadratic Regulator with output feedback design forms a familiar optimal choice to tackle this problem, but the technique is dependent on the choice of the weighting matrices Q and R for the performance index. Particle swarm optimization technique is commonly used in power system applications for obtaining the weighting matrices. In this paper, an output feedback‐based LQR‐PID technique is used to obtain an improved response for the multisource systems. Due to integral action, the steady‐state value achieved for the change in frequency is minimized. In addition, if the system is unmeasurable LQR technique cannot be applied, in the literature, Linear Quadratic Gaussian is used for the same. Here a balanced truncation model order reduction is used on the original system to convert it to a measurable system. In addition, a comparative analysis is included to highlight the efficacy of the proposed control technique. [ABSTRACT FROM AUTHOR]

Summary: Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi‐objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33‐bus and 69‐bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205‐bus. Results obtained by the hybrid FA‐SCAC‐PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy‐efficient analysis to proceed for different cases studied based on the best hybrid FA‐SCAC‐PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel fractional‐order PID plus derivative with filter coefficient (FOPID‐DN) controller for automatic voltage regulator (AVR) system using the MATLAB/Simulink environment. An AVR system is employed in the power system to maintain the terminal voltage at the desired level. Parameters of the proposed FOPID‐DN controller are optimally tuned by the recently developed Equilibrium Optimizer (EO) algorithm. Performance of the designed FOPID‐DN controller for an AVR system is compared with various controllers which are optimally tuned by different optimization algorithms. Comparative transient response analysis of the proposed technique has been carried out by considering vital transient response indicators like maximum overshoot, rise time, and settling time. Performance analysis of the proposed technique for an AVR system has been done by considering several input conditions. Robustness analysis of the EO algorithm tuned FOPID‐DN controller has been performed by varying the time constants of different components in an AVR system. The capability of the FOPID‐DN controller to handle nonlinearities of an AVR system has been investigated. The stability of the FOPID‐DN controlled AVR system has been analyzed. In addition, the disturbance rejection capability of the FOPID‐DN controlled AVR system has also been investigated. From the various simulation results obtained using the MATLAB/Simulink environment, it has been observed that the proposed FOPID‐DN‐based AVR system provided better performance as compared to different existing controllers. [ABSTRACT FROM AUTHOR]

Summary: We study the optimal operation of air‐conditioning loads for commercial consumers based on the time‐of‐use (TOU) ($/kWh) and demand ($/kW) pricing plan. The problem is formulated as a dynamic program that aims to adjust consumer needs for electricity charges and comfort flexibly. Moreover, the Monte Carlo method is used to imitate the environmental uncertainties due to predicted outdoor temperatures. To determine the day‐ahead temperature schedules for air‐conditioning loads, a quantum‐behaved particle swarm optimization based on space compression strategy (QPSO‐SC) is presented in this paper. Space compression and particle re‐initialization by chaotic initialization enhance the performance of the QPSO‐SC. Finally, we test the proposed method for the TOU and demand pricing plan from Duke Energy. Thermostatical, daily energy charge saving, and monthly charge saving strategies are first compared, and the QPSO‐SC is further compared with the particle swarm optimization, genetic algorithm, differential evolution, and other optimization algorithms. From the extensive simulations, the QPSO‐SC is observed to be capable of yielding higher‐quality solutions stably and efficiently than the other optimization algorithms; the proposed optimal strategy can also achieve a better balance between electricity charge and customer comfort, considering environmental uncertainties. [ABSTRACT FROM AUTHOR]

Summary: A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non‐parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO‐tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware‐in‐loop validation of the proposed load frequency control scheme is also carried out using OPAL‐RT to measure its fidelity with the numerical simulation results. [ABSTRACT FROM AUTHOR]

Summary Partial shading conditions generate multiple peaks in the P‐V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. Bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA‐CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA‐BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA‐CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA‐BA strategy. The results of the BA‐BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA‐BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications. [ABSTRACT FROM AUTHOR]

Summary: The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh. [ABSTRACT FROM AUTHOR]

Summary: In battery swapping station (BSS), the battery swapping of electric vehicle (EV) is not synchronous with the centralized charging of BSS. Based on new energy electricity generation and battery swapping demand information of EVs, how to formulate charging–discharging strategies of centralized charging stations is essential in BSS energy management system. Different from traditional BSS scheduling issues, considering that the supply–demand balance of power in isolated microgrid (IMG) could be regulated by the discharging of backup batteries, a service model of EV BSS is proposed with strong constraints, coupling relationships and randomness. Because the microgrid‐BSS capacity configuration optimization issue is nonlinear and multivariable, if traditional optimization algorithms are utilized, it would take a long time and the optimal solution may not be obtained. Moreover, there is no guarantee of complete convergence even with the standard particle swarm optimization (PSO) algorithm. In this paper, the quantum behavior particle swarm optimization (QPSO) algorithm is used to handle operation control issues of BSS. As shown in case results, with the QPSO algorithm proposed, both the reliability of battery swapping service and the maximum profit of BSSs could be implemented. [ABSTRACT FROM AUTHOR]

Summary: Buildings around the world account for about one‐third of the energy consumption. Enough energy is required to maintain the comfort level for the occupants. Recently, the rise in global temperature resulting in climate change is associated with the comfort level for both outdoor and indoor of the buildings. Thus, providing acceptable comfort levels within buildings has become significant. The comfortable indoor environment of building requires energy for the operation of various appliances. A smart and energy efficient approach is the need of an hour to reduce energy consumption and attain comfortable indoor environment. The building energy and comfort management system (BECMS) model incorporating trade‐off between energy consumption and comfort has already been focused in previous studies. However, limited analyses have been observed in comparing the most efficient population‐based algorithm for BECMS. In this paper, a comparative study has been carried out using three different optimization techniques including multi‐objective genetic algorithm (MOGA), hybrid MOGA (HMOGA), and multi‐objective particle swarm optimization method (MOPSO) for optimal energy and comfort management in buildings. These optimization techniques have been widely employed to solve various optimization problems. The significant contribution of this article identifies the best suited algorithm in attaining best optimized solution of energy and comfort in a building. The comparative analysis of the three optimization techniques shows that MOPSO outperforms and attains maximum comfort level and higher energy savings. [ABSTRACT FROM AUTHOR]

Summary: Distributed energy resources (DERs) are a better option for fulfillment of load demand near the load centers. Optimal location and rating of DERs plays a vital role in the enhancement of distribution system performance in terms of power loss reduction, voltage profile enhancement, environmental friendliness, increasing reliability, and postponement of system upgrading. This paper addresses the Gbest‐guided artificial bee colony (GABC) optimization technique for the optimal integration of renewable energy sources (RESs) and electric vehicles (EVs) in distribution feeder to improve the power loss reduction, voltage level, cost reduction, and emissions reduction under load demand uncertainty. In this work, mathematical models of RESs, that is, wind turbine generation system (WTGS) and photovoltaic generation system (PVGS), are employed to determine the generated output power. The foremost aim of this research work is simultaneous abatement of total active power loss, entire cost, and emissions generation; therefore a multiobjective‐based fitness function is formed. Moreover, the impact of charging/discharging behavior of EVs on the distribution grid has also been considered in this study. The proposed methodology is applied on two test systems: (a) 28‐bus practical rural distribution network located at Kakdwip zone in the province of West Bengal, India, and (b) standard 69‐bus distribution feeders. Finally, from the achieved numerical outcomes, it is analyzed that, GABC algorithm generates better outcomes as compared to other optimization techniques. This indicates the viability and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new population‐based meta‐heuristic algorithm called Social Optimization Algorithm (SOA) is proposed. This algorithm has been inspired by the social behavior of human‐beings, and mimics an ideal justly‐established society where the two principles of justice, that is, equality of opportunity and community principles are meticulously followed. Reaching goals in such a society is optimally achieved. Although a real fully‐just society does not exist in the real world, we implement the principles in such ideal societies through simulations to introduce our new optimization algorithm. The advantage of the proposed method is that it does not have any algorithm‐specified parameters to be set. This is a great merit compared to similar meta‐heuristics. The algorithm has been simulated on 23 well‐known test functions, and the results have been compared with those produced by Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Differential Evolution (DE). In 57% of the 23 cases SOA yields superior results. The algorithm has also been tested on five other benchmark functions to be compared with Teaching‐Learning‐Based Optimization (TLBO) and Artificial Bee Colony (ABC).The results provide evidence for efficient performance with superior solution quality of the proposed algorithm in solving benchmark problems. To further illustrate the effectiveness of the proposed algorithm, it has been tested on the Economic Dispatch problem as a real‐world constrained optimization problem. The results have been compared with Biogeography‐Based Optimizer (BBO), Harmony Search (HS) and PSO. The results show that the SOA is able to produce very competitive results compared to other well‐known meta‐heuristic algorithms. Meta‐heuristic, SOA, Economic Dispatch. [ABSTRACT FROM AUTHOR]

Summary: The distributed generations (DGs) of an islanded microgrids (MGs) comprises of interconnecting primary and secondary control layers. The interactions among these controllers may yield new oscillatory modes with low damping, which decrease the stability margins. As the distribution network is dynamic in nature, the maintenance of system stability is of great concern, mainly while operating in autonomous mode. This paper proposes the optimal internal model control (IMC)‐based droop control scheme to improve the small‐signal stability and transient‐response of an inverter‐based MGs (IMGs) under different operating conditions. The proposed optimal scheme is achieved with the following approach: (a) first, comprehensive small signal model is prepared for the study system, and the interaction of the control parameters which influence the system stability are investigated through eigenvalue analysis, (b) the critical values of the significant parameters of the IMC and droop controllers are sorted out, and their corresponding stability domain is formulated, (c) based on the stability domain, PSO optimization technique is employed to generate optimal values, which delivers effective coordination among the crucial control parameters to improve the stability, (d) the developed optimal control scheme is cross‐validated by comparing with the existing methods from the literature. The effectiveness of the proposed optimal scheme is subsequently assessed quantitatively with the help of time‐domain specifications. [ABSTRACT FROM AUTHOR]

Summary: Power flow control in the power system aims to supply electricity to customers efficiently and low operating costs. Based on the conventional approach, the electric power operators only emphasize the function of optimizing generating costs according to load demand and active power limits of generators. On the other hand, the adjustment of the optimization results requires manual adjustments in its implementation. The optimization of the fuel costs minimization, which involves all constraints, is very expected by the operators so that the operator can implement the results directly without making any adjustments. Furthermore, the constraints in power flow optimization that consist of generator capability curve limits, voltage limits, transmission line limits, and active power limits on generators will lead to more complicated optimization, and the application program will run slowly. This paper proposes a method that can reduce optimization constraints with less computation time. The method is to reduce the limits of the generator capability curve through a mathematical model. While the optimization method used is the predictor‐correctors techniques and reduction techniques in the interior point algorithm. The method uses a simple power system using a four‐bus power system that provides accurate and satisfying results. Besides, this method has been successfully implemented in a large‐scale system, namely, the 500 kV Java—Bali Power System with 65 buses and 40 generating units. [ABSTRACT FROM AUTHOR]

Summary: To alleviate the overloads in the power system and to reduce the exponential growth in carbon dioxide (CO2) emissions, deployment of the renewable energy sources (RES) into the power system is the need of the hour. However, injecting these RES into the current power system network causes large voltage and power overshoots hence deteriorate the transient response and power quality of the overall power system. In this paper, an efficient solution of the above‐mentioned issues is explored by developing an optimal microgrid (MG) controller using one of the most modern and intelligent artificial intelligence (AI) techniques named the salp swarm optimization algorithm (SSA). The intelligence of the SSA is exploited to select the optimal controller gains and dc‐link capacitance value by minimizing a time integrating error fitness function (FF) which in‐turn enhances the dynamic response and power quality of the studied MG system. The proposed grid‐tied MG controller is designed to achieve the preset active and reactive power sharing ratio between distributed generator (DG) and utility grid during DG and load switching conditions. To validate the superiority of the proposed controller, its performance is compared with that of its precedent grasshopper optimization algorithm (GOA)‐based controller for the identical operating conditions and system configuration. The outcomes of the study show that the proposed MG controller outperforms its competitor in terms of transient response and quality of power. [ABSTRACT FROM AUTHOR]

Summary: An improved whale optimization algorithm (IWOA) for the design of a robust power system stabilizer (PSS) for the multi‐machine power system is developed in this paper. Tuning of PSS parameters using the proposed IWOA is carried out by minimizing a multi‐objective function comprising the damping ratio and damping factor of lightly damped oscillating modes of all the generators. The advantage of considering the objective function is that the lightly damped and undamped oscillating modes of all the generators can shift to a prespecified zone of the s‐plane. The performance of the proposed IWOA is tested on standard benchmark test functions and compared with familiar optimization algorithms. The efficacy of the proposed design technique is tested on three benchmark test systems: three‐generator nine‐bus system, two‐area four‐machine interconnected system, and the New England 10‐generator 39‐bus system working on various operating conditions under several typical disturbances. The potential of the proposed method is demonstrated through eigenvalue analysis. The proposed IWOA‐based PSS is compared with the other stabilizers to show its efficacy. [ABSTRACT FROM AUTHOR]

Summary: Today's vehicle will consume high amounts of gas and produce exhaust, which is harmful to the environment. An increasing ecological awareness and the shortage of fossil fuel resources are strong motivation to develop more efficient vehicles, with no fuel consumption but without reducing driving comfort. The research work reported in the paper shows the profoundness of design and assessment of solar powered e‐rickshaw at the location with latitude of 22.75° at Indore, India. The works at macro level include pre‐feasibility assessment through analysis of 8760‐hour solar radiation and wind velocity. Duration of project is analysed by the critical path method, and vehicle cost is optimized by three different optimization techniques such as grasshopper, krill herd, and cuckoo optimization techniques. At the end, regression analysis shows the validation of cost‐optimized results. Result of profitability analysis of location of Indore shown by 5100 observation and frequency mode of solar radiation, clearness index, and temperature by 2 827 662, 1 341 397, and 13 413 557. In the modelling of solar‐powered electric vehicle system, 0.75‐KW motor is required to run a vehicle, which is powered by 500‐W solar panel, and energy is stored in 23‐Ah battery. [ABSTRACT FROM AUTHOR]

Summary: Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its nonpolluted and nondepleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing maximum power point tracking (MPPT) techniques' performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, this paper proposes a fuzzy logic controll er (FLC)–based elephant herding optimization (EHO) technique for MPPT in the hybrid wind‐solar system. The proposed fuzzy‐EHO (FEHO) method has the tendency to track the maximum power under the partial shading condition of the photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind‐solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with an experimental setup and provides better efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for determining optimal arrangements of overhead double-circuit power line conductors aimed to reduce electric and magnetic field emissions. Extremely low frequency electric and magnetic field optimization problems have multiple objectives. Multiple-objective optimization allows the simultaneous optimization of two or more conflicting objectives and determination of mutually non-dominated solutions. The multi-objective optimization technique used in this paper is based on Genetic Algorithm to obtain a set of mutually non-dominated solutions (Pareto optimal set) for the two objectives: the electric and magnetic field strengths near overhead double-circuit power line. Also, additional assessment criteria were defined for the selection of several decision scenarios from the Pareto optimal set. The results and practical aspects of the proposed methodology are demonstrated on two typical 400-kV double-circuit overhead power lines. The efficiency of the proposed multiple-objective optimization method is compared with the existing one based on single-objective optimization function. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a novel multi-objective energy management method for a microgrid that includes renewable energy, diesel generators, battery storage, and various loads. The fluctuation of the grid tie-line power flow is regarded as one of the optimal objectives in order to decrease the power exchange between the microgrid and the main grid caused by intermittent and variable renewable resources. The uncertainty of renewable energy and loads is also considered in this paper. Stochastic optimization method is implemented for the output of renewable resources and controllable loads. Using the ε-constrained and weighted sum method, the multi-objective operation optimization problem, which includes the minimum microgrid operation costs, fluctuations of the grid tie-line power, and the risk management, is converted to a single-objective mixed-integer linear optimization problem. The scheduling is achieved for controllable units such as diesel generators, batteries, controllable loads, and grid tie-line power under various weights. The main contribution of the study is to achieve the energy management for a microgrid with regulation of the grid tie-line power fluctuations and peak shaving. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective mathematical programming (MMP) model to day-ahead market clearing of joint energy and reserves auctions by integrating a thyristor-controlled series compensator (TCSC) device with the optimization problem developed under normal and contingency cases. The proposed market clearing framework includes minimization of energy and reserves offer cost, congestion rent, TCSC cost, expected interruption cost, line overload, voltage deviation, and loadability limit. A new index called congestion efficiency index is proposed for the best placement of TCSC under network contingency conditions. Traditional MMP methods such as direction scalarization and ε-constraint methods scalarize the objective vector into a single objective. Those cases are time-consuming and require a number of runs equal to the number of desired efficient solutions. In this paper, the non-dominated sorting genetic algorithm II (NSGA-II), which is integrated with an adaptive neuro-fuzzy inference system (ANFIS), is proposed to find the solution of the optimal schedule of the units energy and reserves by which the parameters of NSGA-II (probabilities of crossover and mutation) are dynamically set, according to a training process. The proposed methodology is developed on the IEEE 30-bus test system, and the results are compared with fuzzy-based and ordinary NSGA-II methods. These comparisons confirm the efficiency of the developed method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]