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The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method. [ABSTRACT FROM AUTHOR]

This article develops two metaheuristics optimization techniques, Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO), to handle multi-objective optimal power flow (MOOPF) issues. Multi Objective GWO (MOGWO) and Multi Objective HHO (MOHHO) are the names of the developed techniques. By combining these optimization techniques with Pareto techniques, the non-dominated solution set can be obtained. These developed approaches are characterized by simplicity and have few control parameters. Fuel cost, emissions, real power losses, and voltage deviation were the four objective functions considered. The theories used to determine the best compromise solution and organize the Pareto front options are the fuzzy membership equation and the crowding distance approach, respectively. To validate and evaluate the performance of the presented techniques, two standard IEEE bus systems—30-bus and 57-bus power systems—were proposed. Bi, Tri, and Quad objective functions with 21 case studies are the types of objective functions and the scenarios that were applied in this paper. As compared to the results of the most recent optimization techniques documented in the literature, the comparative analysis results for the proposed methodologies demonstrated the superiority and robustness of MOGWO and MOHHO. [ABSTRACT FROM AUTHOR]

In the present work, India's primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836. [ABSTRACT FROM AUTHOR]

This paper presents the results of a comprehensive investigation into the comparative analysis and design optimization of ferrite-based surface permanent magnet vernier machines (SPMVMs). While SPMVMs boast a simple mechanical structure and enhanced torque density attributed to the flux modulation effect, they suffer from a persistent challenge of low power factor. Several factors hinder the adoption of low-cost ferrite magnets in SPMVMs. First, ferrite magnets are prone to irreversible demagnetization, constraining the allowable range of magnet thickness. Second, the reduced residual magnetic flux density of ferrite magnets exacerbates the decrease in power factor and machine efficiency. Thus, achieving optimal performance in ferrite-based SPMVMs necessitates the careful selection of various design parameters. To address these issues, this study employs a surrogate-based metaheuristic optimization algorithm with adaptive sampling to identify the optimal solution. Additionally, the integration of a Halbach array is explored to further enhance the performance of the three-slot/two-pole SPMVM topology. Subsequently, two ferrite-based SPMVM baseline models—one with a conventional SPM structure and another with a Halbach magnet array—are thoroughly designed, optimized, and subjected to detailed performance analysis using the 2D finite element method. [ABSTRACT FROM AUTHOR]

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms' performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings. [ABSTRACT FROM AUTHOR]

This paper addresses the critical role of supercapacitors as energy storage systems with a specific focus on their modeling and identification. The lack of a standardized and efficient method for identifying supercapacitor parameters has a definite effect on widespread adoption of supercapacitors, especially in high-power density applications like electric vehicle regenerative braking. The study focuses on parameterizing the Zubieta model for supercapacitors, which involves identifying seven parameters using a hybrid metaheuristic gradient-based optimization (MGBO) approach. The effectiveness of the MGBO method is compared to the existing particle swarm optimization (PSO) and to the following algorithms proposed and developed in this work: 'modified MGBO' (M-MGBO) and two PSO variations—one combining PSO and M-MGBO and the other incorporating a local escaping operator (LCEO) with PSO. Metaheuristic- and gradient-based algorithms are both affected by problems associated with locally optimal results and with issues related to enforcing constraints/boundaries on solution values. This work develops the above-mentioned innovations to the MGBO and PSO algorithms for addressing such issues. Rigorous experimentation considering various types of input excitation provides results indicating that hybrid PSO-MGBO and PSO-LCEO outperform traditional PSO, showing improvements of 51% and 94%, respectively, while remaining comparable to M-MGBO. These hybrid approaches effectively estimate Zubieta model parameters. The findings highlight the potential of hybrid optimization strategies in enhancing precision and effectiveness in supercapacitor model parameterization. [ABSTRACT FROM AUTHOR]

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes. [ABSTRACT FROM AUTHOR]

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value's fitted root mean square error and horizontal movement value's fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications. [ABSTRACT FROM AUTHOR]

The DC circuit breaker is a crucial equipment for eliminating faults in DC transmission lines. The electromagnetic release functions as a critical component that restricts the circuit breaker's lifespan. It is essential to prioritize its safety and reliability during usage and predicting its remaining useful life (RUL) is paramount. This paper proposes a new prediction technique based on particle filtering (PF) and the whale optimization algorithm (WOA) for the remaining useful life of electromagnetic release. The particle filtering algorithm is a commonly used technique in practical engineering fields such as target tracking and RUL prediction. It is a mainstream method for solving the parameter estimation of non-linear non-Gaussian systems. The WOA is introduced to improve the PF algorithm in order to ensure the diversity of particles and lessen the effect of particle degradation. The WOA replaces the traditional resampling process, and after each computation is finished, the weights of the particles in the particle set are reassigned in order to improve the particle distribution and increase algorithm accuracy. The rate of loss of the spring reaction force and the striker counterforce are chosen as the degradation characteristics after the degradation factors of electromagnetic release are analyzed. The degradation curves at different temperatures of electromagnetic release are obtained using the accelerated life test, and the full-life data during normal operation are derived using the Arrhenius equation. Finally, the RUL is predicted by comparing this paper's method with the conventional PF method. The experimental results demonstrate that the method presented in this paper can more accurately predict the RUL of the electromagnetic release and has a higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management. [ABSTRACT FROM AUTHOR]

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case. [ABSTRACT FROM AUTHOR]

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval's theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination ( R 2 ) higher than the other models, and it has the smallest mean absolute percentage error ( M A P E ) and root mean square error ( R M S E ). [ABSTRACT FROM AUTHOR]

This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of generation in operation and the topology of the network. Then, four metaheuristic techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Artificial Bee Colony (ABC) were used to solve the coordination problem of every cluster of operative scenarios. Furthermore, non-standard characteristics of DOCRs were also used. The number of clusters was limited to the maximum number of setting setting groups within commercial DOCRs. In the optimization model, each relay is evaluated based on three optimization variables, namely: time multiplier setting (TMS), the upper limit of the plug setting multiplier (PSM), and the standard characteristic curve (SCC). The effectiveness of the proposed approach is demonstrated through various tests conducted on a benchmark test microgrid. [ABSTRACT FROM AUTHOR]

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research. [ABSTRACT FROM AUTHOR]

This document discusses the challenges of achieving high-power quality in modern power grids with a large share of distributed power production, particularly due to the integration of renewable energy sources (RES). The variable nature of RES can lead to disturbances and power quality degradation. The document highlights the importance of voltage source converters and control systems in enabling the flexible operation of RES. It also mentions the concept of microgrids, which are closely connected to power quality and other factors such as the Internet of Things and cyber security. The document includes eight research papers covering various topics related to power quality analysis and control of renewable energy sources in power grids. These topics include power factor correction, supercapacitor parameters identification, pulse width modulation, disturbance analysis, vibration analysis of motors, control of converters, phasor measurement units, and optimization of controller parameters. The document concludes by expressing gratitude to the authors, staff, and reviewers involved in the research papers. [Extracted from the article]

Continual learning has recently become a primary paradigm for deep neural network models in modern artificial intelligence services, where streaming data patterns frequently and irregularly change over time in dynamic environments. Unfortunately, there is still a lack of studies on computing cluster management for the processing of continual learning tasks, particularly in terms of the timeliness of model updates and associated energy consumption. In this paper, we propose a novel timeliness-aware continual learning management (TA-CLM) system aimed at ensuring timely deep neural network model updates for continual learning tasks while minimizing the energy consumption of computing worker nodes in clusters. We introduce novel penalty cost functions to penalize quantitatively deep neural network model update latency and present the associated optimization formulation to ensure the best task allocation. Additionally, we design a simulated annealing-based optimizer, which is a meta-heuristic technique and easy to implement, to solve the non-convex and non-linear optimization problem. We demonstrate that the proposed TA-CLM system improves both latency and energy performance over its competitors by an average of 51.3% and 51.6%, respectively, based on experimental results using raw data from well-known deep neural network models on an NVIDIA GPU-based testbed and a large-scale simulation environment. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO's effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4–7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization. [ABSTRACT FROM AUTHOR]

In recent years, with the growing popularity of smart microgrids in distribution networks, the effective coordination of directional overcurrent relays (DOCRs) has presented a significant challenge for power system operators due to the intricate and nonlinear nature of their optimization model. Hence, this study proposes a hybrid GA-SQP algorithm to enhance the coordination of directional overcurrent relays (DOCRs) in radial and non-radial interconnected distributed power networks. The proposed approach combines the advantages of both the genetic algorithm (GA) and sequential quadratic programming (SQP) methods to optimize the objective function of relay coordination in the best manner. Thus, the proposed hybrid techniques improved the convergence of the problem and increased the likelihood of obtaining a globally optimal solution. Finally, to validate the effectiveness of the proposed algorithm, it was tested through three case studies involving the IEEE 3-Bus, 8-Bus, and modified 30-Bus distribution networks. In addition, the results were compared to those obtained using previous methods. The results obtained from the comparison of the proposed method and recent advanced research indicate that the proposed optimization approach is preeminent in terms of accuracy and total operating time as well as the continuity of the minimum margin time requirements between the primary/backup relay pairs. [ABSTRACT FROM AUTHOR]

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability. [ABSTRACT FROM AUTHOR]

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field. [ABSTRACT FROM AUTHOR]

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability. [ABSTRACT FROM AUTHOR]

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six. [ABSTRACT FROM AUTHOR]

Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter's effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique. [ABSTRACT FROM AUTHOR]

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms. [ABSTRACT FROM AUTHOR]

The existing distribution networks were designed at a time when there was virtually no embedded generation. The design methods ensured the voltage at various parts of the network remained within the limits required by standards, and for the most part, this was very successfully achieved. As Distributed Energy Resources (DERs) started to grow, the rise in voltage due to injected currents and the local impedances started to push network voltages toward, and even above, the desired upper limits. Voltage limits are based on typical appliance requirements, and long-term over-voltages will ultimately result in unacceptably short appliance life spans. Distribution Static Compensators (dSTATCOMs) are shunt-connected devices that can improve low-voltage networks' performance by injecting currents that do not transfer real power. The currents can be reactive, negative or zero sequence, or harmonic. System performance can be improved by reducing conduction loss, improving voltage profile and voltage balance, or reducing Total Harmonic Distortion (THD). To obtain these benefits, optimal sizes of dSTATCOMs need to be placed at optimal locations within the distribution network. This paper has considered seventy research articles published over the past years related to the optimal placement and sizing of dSTATCOMs. In this study, minimization of power losses, voltage profile improvement, loadablity factor, voltage sag mitigation, and reduction in annual operating costs are considered fitness functions that are subjected to multiple constraint sets. The optimization algorithms found in the literature are categorized into six methods: analytical methods, artificial neural network-based methods, sensitivity approaches, metaheuristic methods, a combination of metaheuristic and sensitivity analysis, and miscellaneous. This study also presents a comparison among distribution network types, load flow methods optimization tools, etc. Therefore, a comprehensive review of optimal allocation and sizing of dSTATCOMs in distribution networks is presented in this paper, and guidance for future research is also provided. [ABSTRACT FROM AUTHOR]

Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞISE) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system's output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system's multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles' hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by 42.41 % and improve dynamic tracking accuracy by 1.95 % , compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of 2.66 % , 3.56 % , and 4.24 % in dynamic tracking accuracy, respectively. [ABSTRACT FROM AUTHOR]

An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%. [ABSTRACT FROM AUTHOR]

The parameter identification of a PEMFC is the process of using optimization algorithms to determine the ideal unknown variables suitable for the development of an accurate fuel-cell-performance prediction model. These parameters are not always available from the manufacturer's datasheet, so they need to be determined to accurately model and predict the fuel cell's performance. Five optimization methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) algorithm, antlion optimizer (ALO), and heap-based optimizer (HBO)—are used to compute seven unknown parameters of a PEMFC. During optimization, these seven parameters are used as decision variables, and the fitness function to be minimized is the sum square error (SSE) between the estimated cell voltage and the actual measured cell voltage. The SSE obtained for the BES algorithm was noted to be 0.035102. The COOT algorithm recorded an SSE of 0.04155, followed by ALO with an SSE of 0.04022 and HBO with an SSE of 0.056021. BES predicted the performance of the fuel cell accurately; hence, it is suitable for the development of a digital twin for fuel-cell applications and control systems for the automotive industry. Furthermore, it was deduced that the convergence speed for BES was faster compared to the other algorithms investigated. This study aims to use metaheuristic algorithms to predict fuel-cell performance for the development and commercialization of digital twins in the automotive industry. [ABSTRACT FROM AUTHOR]

Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade conditions (PSCs). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPTs) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time was reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, the best initialization of search agents, and the optimal design of the dc–dc converter are introduced for optimal MPPT performance. [ABSTRACT FROM AUTHOR]

Integrating renewable resources into the electrical systems of marine vessels achieves the dual goal of diversifying energy resources and reducing greenhouse gas emissions. The presence of intermittent renewable sources and sudden nonlinear load changes can cause frequency deviations in isolated hybrid marine microgrids. To address this issue, the paper proposes a conventional PID (proportional–integral–derivative)-controller-based LFC (load frequency controller) which is optimized by meta-heuristic optimization algorithms, namely, PSO (particle swarm optimization), GWO (grey wolf optimization) and hybrid PSO-GWO. The proposed LFC was designed using transfer functions of various microgrid components, with ITAE (integral time absolute error) and ITSE (integral time square error) serving as performance indices. The proposed LFC's validation was performed through HIL (hardware-in-loop) real-time simulation using a DS 1104 R&D controller board, with simulation results showing the better performance of the optimized frequency response compared to the nonoptimized LFC controller in terms of rise time, fall time, slew rate and overshoot. The hybrid PSO-GWO algorithm performs better than the other optimization algorithms. The simulation results demonstrate the stability and robustness of the proposed controller. In summary, the proposed PID-controller-based LFC can regulate frequency deviation in standalone hybrid marine microgrids effectively. [ABSTRACT FROM AUTHOR]

The main target is the maximization of the output power of PEM "proton exchange membrane" fuel cell via fuzzy modelling and optimization. In the beginning, using the experimental data, a robust fuzzy model is designed for simulating the PEM fuel cell using the relative humidity (%) and stoichiometric ratio at the anode and cathode. Then, the artificial ecosystem optimiser (AEO) is applied to determine the best values of the controlling input parameters. During the optimization process, the four controlling input parameters of the PEMFC are used as the decision variables, whereas as the cost function is required to be at the maximum of the output power density of the PEMFC. For the fuzzy model of the power, the RMSE values are 1.5588 and 3.1906, respectively, for training and testing data. The coefficient of determination values are 0.9826 and 0.8743 for training and testing, respectively. This confirms a successful modelling phase. Finally, the integration between fuzzy and AEO boosted the power of the PEMFC from 57.95 W to 78.44 W (by around 35%). Under this optimal condition, the controlling input parameters values are 26.65%, 56.77%, 1.14, and 1.68, respectively, for anode relative humidity, cathode relative humidity, anode stoichiometric ratio and cathode stoichiometric ratio. The present study, however, intends to highlight the importance of fuzzy modelling and metaheuristic algorithms in the development of digital twins to accelerate the commercialization of fuel cells as well as its applicability in diverse global economic sectors where a higher power requirement is needed. It is also aimed at informing the fuel cell research community and policy makers on strategies that could be adopted in boosting fuel cell performance and therefore could be a good reference source in decision-making for fuel cell commercialization and its practical implementation. [ABSTRACT FROM AUTHOR]

The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and numerical modelling of complex physical phenomena, which require extensive resources and time to obtain reliable, yet limited results. To complement these approaches, artificial-intelligence-based techniques (AI) are gaining increasing interest, given their computational speed and capability of searching large solution spaces and/or identifying key study patterns. Under this scope, this paper presents a comprehensive review on the use of computational systems and AI-based techniques to wave climate and energy resource studies. The paper reviews different optimization methods, analyses their application to extreme events and examines their use in wave propagation and forecasting, which are pivotal towards ensuring survivability and assessing the local wave operational conditions, respectively. The use of AI has shown promising results in improving the efficiency, accuracy and reliability of wave predictions and can enable a more thorough and automated sweep of alternative design solutions, within a more reasonable timeframe and at a lower computational cost. However, the particularities of each case study still limit generalizations, although some application patterns have been identified—such as the frequent use of neural networks. [ABSTRACT FROM AUTHOR]

The economic interest in power loss minimization and regulatory requirements regarding voltage levels in distribution systems are considered. In this paper, a computational technique to assist in the optimization of the power losses and voltage characteristic in the steady state through distribution network reconfiguration and the location and size of the distributed generators is addressed. The whale optimization algorithm (WOA) is chosen to perform this task since it can explore the sizeable combinatorial search space of the problem, which is also nonlinear and nonconvex. The purpose of this study is to mitigate power losses; voltage ranges are borne in mind as the problem restrictions. The proposals for solving the issue are evaluated using a specialized power flow algorithm. The algorithm is implemented in MATLAB and the 33-bus and 69-bus grids are employed to assess the performance of the approach. The results indicate that the WOA method outperforms regarding power loss reduction and voltage characteristic improvement in the concurrent integration of distribution network reconfiguration and distributed generators compared with the four metaheuristics shown in the results section. [ABSTRACT FROM AUTHOR]

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey's mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10−14, 28.35 × 10−28, and 2.5 × 10−14, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested. [ABSTRACT FROM AUTHOR]

The approach proposed for the development of this research work was based on the integration of Distributed Energy Generation (DG) into an energy distribution network, improving the voltage profile by establishing an optimal location and sizing of DG power plants, for which the use of a heuristic optimization method known as Genetic Algorithm (GA), which has several restrictions to limit its operation and thus achieve an optimal solution to the planned optimization problem, was resorted to. The development of a power flow allowed having the voltage measurement in the bars of the network before incorporating DG, allowing to verify that the voltage fluctuations present an improvement after the incorporation one by one of each DG central defined by the GA. The systems in which the voltage measurement was performed, as well as the verification of its fluctuations with and without DG once the GA was used, were the IEEE systems of 9 and 14 busbars, the latter the one used to demonstrate the scalability of the GA to locate and dimension DG without considering the number of busbars in a system. [ABSTRACT FROM AUTHOR]

The amount of energy that a thermoelectric generator (TEG) is capable of harvesting mainly depends on the temperature difference between the hot and cold sides of the TEG. To ensure that the TEG operates efficiently under any condition or temperature variation, it is crucial to have a reliable MPPT that keeps the TEG as close as possible to its MPP. Fractional control is usually preferred over integer control because it allows for more precise, flexible, and robust control over a system. The controller parameters in fractional control are not limited to integer values, but rather can have fractional values, which enables more precise control of the system's dynamics. In this paper, an optimized fractional PID-based MPPT that effectively addresses two primary issues, dynamic response and oscillation around MPP, is proposed. Firstly, the five unknown parameters of the optimized fractional PID-based MPPT were estimated by the BES "bald eagle search" algorithm. To validate the superiority of the BES, the results were compared with those obtained using other optimization algorithms, such as ant lion optimizer (ALO), equilibrium optimizer (EO), cuckoo search (CS), and WOA "whale optimization algorithm". The results demonstrate that BES outperforms ALO, EO, CS, and WOA. Additionally, the tracking performance of proposed MPPT was evaluated using two scenarios that involved variations in temperature differences and sudden changes in the load demanded. Overall, the proposed optimized fractional PID-based MPPT effectively improves dynamic performance and eliminates oscillation around MPP under steady state compared to other tracking methods, such as P&O "perturb and observe" and incremental conductance (INR). [ABSTRACT FROM AUTHOR]

Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster. [ABSTRACT FROM AUTHOR]

In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use six inputs: power rating, primary voltage, secondary voltage, primary and secondary winding connections, and frequency. The TOC of the transformer, which includes the cost of the basic materials of the transformer plus the cost of losses, is minimized under the imposed constraints (excitation current, impedance, no-load losses, load losses, and efficiency) usually specified in the standards. As a case study, the three algorithms are applied to optimize the design of a three-phase shell-type distribution transformer of 750 kVA. All applied metaheuristic algorithms provide good results, while DE avoids local optima leading to better TOC reduction. The results of the optimization algorithms used are superior to those of the manufacturer, showing a 6% TOC reduction. Optimization of the design of a power transformer may have important implications for reducing greenhouse gas emissions and extending the lifetime of the equipment. [ABSTRACT FROM AUTHOR]

Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the K p ,   K i parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the K p ,   K i parameter to reach the optimal value by different methods is a promising and popular research direction at present. The K p ,   K i parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used K p ,   K i parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting the K p ,   K i parameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the K p ,   K i parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the   K p ,   K i parameter tuning of PI controllers. [ABSTRACT FROM AUTHOR]

The increasing number of renewable energy sources (RESs) connected to power grids contributes to the emergence of not only balancing problems but also technical ones, such as the overloading of power lines. If renewable sources with a high generation level are planned to be connected in the area under consideration, then a large number of significant overloads should be expected, especially during contingency analysis. As a rule, high-voltage networks have a mesh topology, which is why the concept of using advanced mathematical algorithms was developed, with the help of which the resulting threats can be eliminated. This article presents a proposal for a new method of eliminating line overloads and determining the currently available nodal generation levels. Its innovation is a new method of eliminating problems related to the capacity of power grids. The high efficiency of the method results from the appropriately defined response of properly selected RES sources to the state of network congestion. The problem under consideration is illustrated with the example of a modified IEEE 118-bus test network. In order to eliminate line overloads, the article proposes a two-stage approach. In the first step, the sources that are most responsible for the occurring overloads are determined. In the second step, a metaheuristic algorithm is used to solve a nonlinear optimisation problem with constraints. This task involves reducing the power generated in the sources selected in the previous step in such a way that the resulting line overloads are eliminated, and, at the same time, the total value of the curtailed generation is minimal. [ABSTRACT FROM AUTHOR]

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison. [ABSTRACT FROM AUTHOR]

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module's performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods. [ABSTRACT FROM AUTHOR]

In this study, the load frequency control of a two-area thermal generation system based on renewable energy sources is considered. When solar generation is used in one of the control areas, the system becomes nonlinear and complicated. Zero deviations in the frequencies and the flow of power through the tie lines are achieved by considering load disturbances. A novel grey wolf optimizer, which is a metaheuristic algorithm motivated by grey wolves is utilized for tuning the controller gains. The proportional, integral, and derivative gains values are optimized for the two-area Solar integrated Thermal Plant (STP). As the load connected to the system varies continuously with time, random load variation is also applied to observe the effectiveness of the proposed optimization method. Sensitivity analyses have also been adopted with the deviation in the time constants of different systems. Inertia constant variations of both areas are considered from −25% to +25%, with or without STP. The proposed algorithm shows good dynamic performance as shown from the simulation results in terms of settling time, overshoot values, and undershoot values. The power in the tie line achieves zero deviation quite rapidly in solar-based cases compared to those without STP. [ABSTRACT FROM AUTHOR]

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

Main Forecasting Problems There are different types of forecasting problems that can arise in electricity demand forecasting, including: Very short-term forecasting, which refers to the prediction of electricity demand or production for a time horizon from seconds to a few hours ahead. The applications of long-term forecasts of wind farm power generation are, e.g., maintenance scheduling, wind farm design, electricity market restructuring, and optimization of operating costs. A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner. A power system requires forecasts that predict the future electricity demand, the power generation from RESs, and meteorological data that are important regarding consumer demand and the level of generation from RESs. [Extracted from the article]

In classical power systems, frequency measurements are transferred via a specialised communication channel, resulting in time delay. The time delay plays a major role in a power system, which can reduce the dynamic performance of the load–frequency control (LFC) system and can destabilise the system. The research to date has tended to focus on developing a new algorithm to determine the delay margin (DM) rather than looking into a hybrid algorithm which includes a nature-inspired metaheuristic optimisation technique. This paper introduces a novel method for computing the DM based on grey wolf optimisation (GWO), specifically for the constant time delay. In the proposed method, GWO is employed to optimise the minimum error of the spectral radius and to determine the best design variable of the crossing frequency. With the help of the proposed method, the sweeping range is no longer required, which improves the accuracy of the result. To evaluate the proposed method, a two-area network power system is considered as a case study. Furthermore, the effect of the PI controller gains on the DM is taken into account. The proposed method efficacy is demonstrated by comparing it with the most recently published methods. The results demonstrate that the proposed method is remarkably better than the existing methods found in the literature, where the smallest percentage inaccuracy using the simulation-based DM based on GWO is found to be 0.000%. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal ensemble method for one-day-ahead hourly wind power forecasting. The ensemble forecasting method is the most common method of meteorological forecasting. Several different forecasting models are combined to increase forecasting accuracy. The proposed optimal ensemble method has three stages. The first stage uses the k-means method to classify wind power generation data into five distinct categories. In the second stage, five single prediction models, including a K-nearest neighbors (KNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) model, a support vector regression (SVR) model, and a random forest regression (RFR) model, are used to determine five categories of wind power data to generate a preliminary forecast. The final stage uses an optimal ensemble forecasting method for one-day-ahead hourly forecasting. This stage uses swarm-based intelligence (SBI) algorithms, including the particle swarm optimization (PSO), the salp swarm algorithm (SSA) and the whale optimization algorithm (WOA) to optimize the weight distribution for each single model. The final predicted value is the weighted sum of the integral for each individual model. The proposed method is applied to a 3.6 MW wind power generation system that is located in Changhua, Taiwan. The results show that the proposed optimal ensemble model gives more accurate forecasts than the single prediction models. When comparing to the other ensemble methods such as the least absolute shrinkage and selection operator (LASSO) and ridge regression methods, the proposed SBI algorithm also allows more accurate prediction. [ABSTRACT FROM AUTHOR]