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Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

ABSTRACT In this article, a multi‐objective comprehensive evaluation method is established by comprehensively considering the power and shaft diameter of a multistage hydraulic turbine with an ultrahigh water head and low flow rate to utilize water energy efficiently. Using this method, several schemes for calculating the runner's geometric parameters are attained through the scheme design of different maximum numbers of stages and rotational speeds under different operating conditions of water pressure and flow rate. The reasonable schemes are determined by the maximum value in the intersection of runner diameter value ranges, the blade inlet angle β1 ≥ 12° and the blade inlet flow angle α1 ≥ 6°. Based on the multi‐objective function of water energy utilization considering the comprehensive performance of the runner diameter and power, the design parameters and design stage numbers of the multistage hydraulic turbine with the optimal comprehensive performance of power and shaft diameter are obtained. This method is recommended for the design of ultra‐low specific speed multistage hydraulic turbines with a specific speed of less than 50. Multi‐objective evaluation method for efficient water energy utilization in multistage hydraulic turbines with ultra‐high water head and low flow rate

Summary Increasing interest in smart grids exhibits its potential benefits for providing reliable, secure, efficient, environmental friendly and sustainable electricity from renewable energy resources. Here, reliability models of four types of renewable and hybrid distributed generation were developed. A fuzzy multi‐objective function was suggested for simultaneous optimization of reliability, electricity generation cost, grid loss and voltage profile. This not only considers uncertainty of renewable energy resources but also provides smart generation dispatching. An efficient reliability index consisting of energy and interruption frequency terms was also defined. A novel hybrid heuristic optimization method based on simulated annealing and particle swarm optimization methods was proposed. These approaches were applied to the generation dispatching of a smart grid, and the results were discussed in details. Scenarios including the changes of wind speed, sun light, fuel price and weight coefficients of the objective function were analyzed. This work succeeds to model uncertainty of renewable energy resources and performs technical and economical optimization in the power generation planning. Copyright © 2014 John Wiley & Sons, Ltd.

The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine’s capabilities.

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship’s status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities. This is accomplished by a framework that combines the inverse finite element method (iFEM) with the definition of an optimal Fiber Bragg Gratings-based sensor network for the reconstruction of the full field of displacement; strain; and finally, cross-section internal forces. The optimization of the sensor network was performed by defining a multi-objective function that simultaneously considers the accuracy of the displacement field reconstruction and the associated cost of the sensor network. The framework was successfully applied to a mid-portion of a containership case, demonstrating its effective applicability in real and complex scenarios.

Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper proposes an efficient method for sentiment analysis by effectively combining three procedures: (a) creating the ontologies for extraction of semantic features (b) Word2vec for conversion of processed corpus (c) convolutional neural network (CNN) for opinion mining. For CNN parameter tuning, a multi-objective function is solved for nondominant Pareto front optimal values using particle swarm optimization. Experiments show that the proposed technique outperforms other state-of-the-art techniques while yielding 88.52%, 94.30%, 85.63% and 86.03% in accuracy, precision, recall and F -measure, respectively.

As the world faces increasingly urgent environmental challenges, fuel cell hybrid electric vehicles powered by fuel cells, Maxwell supercapacitors, and Li-ion batteries have been considered a promising solution and likely candidate to replace internal combustion engine vehicles to save fossil fuels and reduce greenhouse gas emissions. Two of the big challenges in fuel cell hybrid electric vehicles are the optimal sizing of components (fuel cell, battery, supercapacitor), and the thermal control of PEMFCs stack temperatures always close to 90 °C. The purpose of this paper is to develop an in-house optimization code for creating a fuel cell hybrid electric vehicle (FCHEV) application for simulating a fuel cell-powered hybrid electric vehicle using MATLAB code based on the multi-objective particle swarm optimization algorithm. In this paper, to answer these two challenges, we demonstrate the FCHEV application in two case studies. In the first case study from the FCHEV application database, the Artemis driving cycle is offered to assess the influence of driving cycle conditions on fuel consumption. It should be noted that fuel consumption is closely related to three main parameters: the maximum speed of the vehicle 111.5 km/h, which directly affects the volume of the fuel cell, the average speed of the vehicle 38.37 km/h during the cycle, which affects the power required from the vehicle, which directly affects the fuel that is consumed, and mainly the speed profile mode (acceleration mode or braking mode). In the second case study, which is considered the most important contribution of this paper, the effects of the temperature of the fuel cell on the fuel consumption in the electric vehicle was shown, we create a thermal controller to maintain a fuel cell temperature at 90 °C and then its note effects on fuel consumption. It should be noted that the addition of the thermal controller reduced fuel consumption by more than 3.47% in the Artemis driving cycle of 2077s. Considering driving cycles greater than the Artemis driving cycle, the impact of these parameters on fuel consumption will be very significant. Graphical Abstract

One of main challenges in the many countries with attention to growth people in the world is sustainable consumption and production of energy to improve environmental and economic issues by smart energy systems. In this paper, a multi-objective function model is developed to supply the demand of a smart micro-grid (SMG) with the aim of minimizing first) the operation cost, second) the emission pollution, and third) the deviation between the original demand curve and its desired level in the day-ahead time period. The third proposed objective function is a new strategy which can be used by the SMG operators to manage the demand consumption through responsible customers (RCs) with shiftable loads. Moreover, a number of consumers can participate in the energy management problem of the system through curtailing the demand as a reserve. The proposed objective functions are optimized to obtain the non-dominated solutions using the epsilon-constraint method. Then, the best solution is selected using combined fuzzy and Weighted sum approaches. To evaluate the effectiveness of the proposed model and its solution approach, it is applied on a test system considering four different case studies. The emission pollution and operation cost in the first case (base case) are 8832.24 kg and $692,930.2. In second case and with the participation of reserve, the reduction of the operation cost and the emission are equal to 6.03% and 7.98% than first case. With the participation of the demand shifting strategy in third case, operation cost and the emission are decreased by 20.2% and 19.89% according to base case. Finally, in fourth case and with participation of reserve and demand shifting strategy, the operation cost and the emission pollution are reduced by 26.5% and 38.1% in comparison with the base case.

This paper models the electrochemical energy storage system and proposes a control method for three aspects, such as battery life, to generate a multiobjective function for optimizing the capacity allocation of electrochemical energy storage under multiple scenarios, with conditional constraints on the system, storage, and progression aspects. The improved whale optimization algorithm is used to solve the multi-objective function to find the most reasonable electrochemical energy storage system capacity optimization allocation scheme. Using the model constructed in this paper under multi-scenario conditions, it is found after solving that the optimal allocation scheme purchases power from the grid at around 25MW during the highest peak hours in summer and 5MW in winter, which ensures the economic benefits. Meanwhile, the maximum power fluctuation of the electrochemical energy storage system at point A of the optimization strategy provided by the model is only 2.16%, which is much lower than the preset 4.32%, so the optimal allocation strategy reaches the optimum. Comparing the performance of configured energy storage in different scenarios, the peak-valley power difference of the model proposed in this paper decreases from 11.6 MW to 8.9 MW, which is a better performance than that of the control group, which is 10.8 MW-9.1 MW, and the effect of peak shaving and valley filling is obvious.