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Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

With the increasing electricity consumption and difficulty in upgrading existing infrastructures, ill‐conditioned power flow (PF) cases are becoming more frequent nowadays. In this context, classical robust solvers may be unsuitable for realistic networks, which typically encompass thousands of buses, because of their high computational burden or low convergence rate. This article tackles this issue by proposing a novel PF solver, which presents acceptable robustness and efficiency in solving large‐scale ill‐conditioned systems. The proposed algorithm collects the advantage of various numerical solvers, which by separate present different weaknesses, but actuating in coordination their strengths can be jointly exploited. More precisely, the robust Forward‐Euler and Trapezoidal rules are combined with the efficient Darvishi cubic technique. Thereby, an original predictor‐corrector algorithm is developed to effectively coordinate the different numerical algorithms involved, obtaining a robust but efficient yet solution procedure. Various large‐scale ill‐conditioned benchmark systems are studied under different stressing conditions. The results obtained with the developed technique are promising, outperforming other robust and standard PF solvers. [ABSTRACT FROM AUTHOR]

This article describes a technique for improving the performance of an offshore wind farm (OWF), which includes a permanent magnet synchronous generator (PMSG) driven by a variable‐speed wind turbine (VSWT) and connected to the grid through a high‐voltage direct current transmission system (HVDC). Under various network disturbances, the gorilla tropical optimization (GTO) metaheuristic method is used to fine‐tune the proportional‐integral (PI) controller gains for VSC‐based PMSG‐VSWT and VSC‐based HVDC transmission systems. This study compares several strategies for extracting the maximum power from a system while maintaining stability under symmetrical and unsymmetrical fault circumstances. Compared to different algorithms, GTO provides better results and improves system recovery and stability after disturbances. As a consequence, dynamic and transient stability may be enhanced. MATLAB/Simulink is used to implement the control mechanism. [ABSTRACT FROM AUTHOR]

This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation coefficients to improve transient stability. The PI gains are optimized using the Golden Jackal Optimization Algorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm Optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable energy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work. • A hybrid wave and PV system is proposed. • A nonlinear model of the AWS wave energy system is presented. • A novel optimal control based on the GJOA is described. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system's demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study's main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators' operation, due to the penetration of renewable energy sources into the studied networks. [ABSTRACT FROM AUTHOR]

The fuel cell is vital in electrical distribution networks as a distributed generation in today's world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies' transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer's side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model. [ABSTRACT FROM AUTHOR]

In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel enhancement to the Transient Search Optimization (TSO) algorithm to estimate an accurate electrical model of the proton exchange membrane fuel cell (PEMFC). The PEMEFC model is a non-linear model that includes seven unknown variables which cannot be calculated analytically. The TSO is enhanced by inserting two new factors, the Levy function and the Weibull distribution function. The proposed enhanced Transient Search Optimization (ETSO) and TSO algorithms are applied to estimate the seven variables by minimizing the sum of the squared errors (SSEs) between the measured and calculated voltages. The error is defined as the difference between the measured and the calculated output voltage of the PEMFC. Three different commercial types of PEMFCs are modeled: i) Ballard, Mark V 5 kW, ii) Horizon H-12, and iii) 6 kW Nedstack PS6 stacks PEMFC. The estimated seven variables and the minimum SSE of electrical PEMFCs using ETSO and TSO algorithms are compared with the results obtained by using other optimization algorithms like whale optimization algorithm, genetic algorithm, neural network algorithm and others. The results obtained by the ETSO are better than that obtained by TSO by more than 10% and this percentage increases with other algorithms. The accuracy of the proposed PEMFC model is verified by comparing the estimated V – I and P – I characteristics with the measured data. The effectiveness of the proposed ETSO based model is verified by an investigation of sensitivity analysis for design variables and the robustness of the ETSO algorithm via the statistical analysis and the parametric t -test. • This paper presents a novel improved TSO algorithm to extract PEMFC model parameters. • Three commercial PEMFC models are implemented in this paper. • The parameters of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. • The sensitivity and robustness analyses of the proposed method are tested. [ABSTRACT FROM AUTHOR]

This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems' constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function. [ABSTRACT FROM AUTHOR]

In this article, the improved chaotic electromagnetic field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) considers nonlinear and complex optimization problems with different control variables. The sum of squared error between the measured and computed stack voltages is considered as the main objective function. The performance of the ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures are presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures is studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitive compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation. Finally, based on the optimization results a Simulink model for PEMFC is developed to examine the dynamic performance of the PEMFCs. [ABSTRACT FROM AUTHOR]

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses. [ABSTRACT FROM AUTHOR]

Decarbonization of the energy sector calls for new energy sources and power-system operation paradigms. In this context, consumers can partake in system operation as loads and generators by leveraging own resources, such as rooftop photovoltaics (PVs). These prosumers can be gathered into energy communities (ECs) and operated in a centralized manner to exploit eventual synergies optimally. The ECs are usually connected to a distribution network and thus interact with the distribution grid operator (DSO), complicating the scheduling strategies under agent interactions. Moreover, the DSO can thus manage his own resources such as distributed generators or medium-scale storage systems. The operation becomes more complex due to multi-source uncertainties in ECs and DSO. This paper addresses this issue by developing a novel bi-level energy management framework for the robust optimal coordination of ECs and DSO against uncertainties. In this framework, the upper-level agents provide the information by solving the designed optimization problems for the EC operation, sending it to the lower-level agents to facilitate the optimal scheduling of distributed assets. In particular, a novel robust reformulation of the lower level problem is proposed, to handle with uncertainties in renewable generation, demands, and energy prices fixed by the local retailer. The resulting formulation is mixed-integer-linear programming that is efficiently manageable by common solvers. Simulation results on the standard IEEE 33-bus distribution system with six connected ECs and various distributed assets (e.g., renewable generators and storage systems) demonstrate the effectiveness and robustness of the proposed framework. The results also evidence the vital role of storage systems in maximizing the DSO's profits while reducing the ECs' operating costs. [ABSTRACT FROM AUTHOR]

• A novel optimal FLC is presented for performance enhancement of microgrids. • A detailed model of microgrids and its real-time implementation is introduced. • The optimal FLC is based on the AVOA strategy. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. Distributed power grid systems require more intelligent and adaptable control management and optimization to preserve the best performance. These balance the generated power source and load demand even during severe interruptions. This research proposes a comparative analysis of an autonomous microgrid system optimized by fuzzy logic control and proportional-integral controller. The microgrid fuzzy logic control parameters are designed using the African vulture and Particle swarm optimization algorithms. At the same time, the proportional-integral controller is designed by using the Gorilla troops optimization algorithm. The suggested microgrid control techniques are verified using a MATLAB/Simulink environment. The overall system's detailed modeling and control methodologies are also presented. The investigation of the fuzzy logic control approach is also tested in a real-time setting. It is implemented by using OPAL real-time 4510 rapid control prototyping. The attained results prove the superiority of the fuzzy logic controller based on the African vulture optimization technique compared with other optimal control techniques. It is applied to give an ideal design for the fuzzy logic control parameters to preserve adequate power to the different loads. The proposed optimal controller is able to bring a robust and effective control with an accuracy of approximately 4%, 5%, 28%, and 26% than other techniques under different scenarios. Index Terms: African Vultures Optimization algorithm (AVOA), Autonomous Microgrid, Decentralized energy generation, Fuzzy Logic Control, Opal RT-LAB, Proportional plus integral controller. [ABSTRACT FROM AUTHOR]

• A new price setting mechanism for microgrid clusters is developed. • The new approach only requires sharing boundary information. • Uncertainties are incorporated using interval notation and equivalent scenarios. • The novel proposal encourages the participation in energy trading. • The strategy adopted by agents notably impact on the energy transacted. With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids. [ABSTRACT FROM AUTHOR]

• An optimal bidding strategy for microgrids in clusters is developed. • A multi-stage framework is proposed for considering response fatigue. • Uncertainty risk is managed by enforcing the CVaR. • The total number of response signals can be reduced by 76 %. • The response fatigue minization may reduce the collective welfare by 18 %. The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account. [ABSTRACT FROM AUTHOR]

The integration of Electric Vehicles (EVs) and Photovoltaics (PVs) into the electric power distribution system is rising worldwide due to the environmental concerns and the rapid depletion of fossil fuels. However, the simultaneous integration of EVs and PVs in the power system poses challenges of uncertain power penetration, charging, and dynamic loading conditions, which requires a suitable control design and scheduling optimization to keep the modified electric system stable. This paper proposes a solar-based grid-tied charging station (SGTCS) that optimizes EV charging by enabling the scheduling technique resulting in maximum utilization of PV power. The proposed model also identifies and considers seasonal variation effects of power generation and EV charging connectivity. The SGTCS is implemented using HOMER Grid, and a case study of a practical locality in Islamabad was investigated; the conducted detailed analysis comprises the annual cost estimation, energy costs, EV scheduling, main grid power usage reduction, and the impact of environmental changes. Examining the integration of small and big EVs in the grid-tied solar-integrated charging system, a brief scheduling strategy of 250 sessions per day is analyzed. The resultant techno-economic implications are presented, depicting the proposed study's relevance and applicability. • This paper presents an optimal scheduling and techno-economic analysis of EVs. • This is based on solar-based grid-tied charging station. • A case study of a practical locality in Islamabad is investigated. • The model considers seasonal variation effects of power generation and EV charging. • The results of proposed model are compared with other models. [ABSTRACT FROM AUTHOR]

• A novel hybrid stochastic-IGDT model is developed for HEM problems in nZEBs. • Real energy price and vehicle-to-home capability are properly modelled. • Unpredictable behavior of the electric vehicle is formulated via IGDT. • A bi-objective structure is presented to jointly optimize the energy consumption and cost using lexicographic optimization. • The problem is kept linear being solvable by standard solvers. Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions. [Display omitted] • A detailed model of the WECS is presented comprehensively. • Enhancement of the transient performance of grid-connected WECS is the main goal. • A new optimization algorithm is applied to select the parameters of the PI controllers and the anti-windup coefficients. • Different scenarios have been investigated under normal and abnormal operating conditions. • The results obtained validate the robustness of the proposed controllers. [ABSTRACT FROM AUTHOR]

• A novel uncertainty-aware day-ahead scheduling tool for microgrids considering response fatigue is developed. • The novel methodology presents a modular and linear formulation, being thereby solvable by conventional solvers. • Various types of demand response programs are modelled and analysed. • Uncertainties and response fatigue are jointly treated by an original information gap decision theory-based methodology. • A benchmark case study serves to show the capabilities and effectiveness of the developed method. The implantation of demand response programs may be unsuccessful due to a variety of reasons. One of the most important is the so-called response fatigue, which refers to the discouragement experienced by consumers when they received an excessive number of signals from the operator. This circumstance is, however, typically ignored in energy management tools of electrical energy systems. To solve this issue, this paper proposes an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids based on information gap decision theory, which incorporates additional constraints to bound the duration of demand response signals. Thereby, the harmful effects caused by response fatigue are lessened. The developed optimization problem is formulated as a Mixed-Integer-Linear programming, which is solvable using standard solvers and versatile enough to be adapted to different system layouts. A benchmark case study serves to show the effectiveness of the developed methodology to manage with uncertainties, while the effect of response fatigue in consumers is bounded to acceptable thresholds. As a sake of example, the developed methodology is able to determine a scheduling plan with a total renewable generation 50% lower compared with the deterministic case, while the total demand is overestimated by ∼20%, in which the effect of response fatigue is kept within acceptable bounds yet. Accurateness and efficiency of the new proposal are also checked by making a comparison with other uncertainties modelling. [ABSTRACT FROM AUTHOR]

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters' uncertainty compared with other techniques. [ABSTRACT FROM AUTHOR]