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This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage. • A comparative analysis of three energy management strategies is presented. • New hybrid optimization technique is developed. • Optimal tuning for the control parameters of the DC-bus voltage controller. • Enhancing the DC-bus voltage profile and some performance criteria are conducted. [ABSTRACT FROM AUTHOR]

Due to the importance of water resources management that requires the optimal model for streamflow prediction, the study of water flow prediction is of great importance. Hence, the present paper aims to introduce an innovative model that can predict streamflow with the highest accuracy. Accordingly, a multilayer perceptron neural network whose optimum neuron number is specified using the evaluation metrics is considered for simulation. The dataset used for this purpose consists of 80% experimental and 20% numerical data. Accordingly, the MLP network used in this research is optimized using various optimizers, namely Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Chaotic Grey Wolf Optimization, Advanced Grey Wolf Optimization, and Adaptive Chaotic Grey Wolf Optimization. Considering the statistical results, Adaptive Chaotic Grey Wolf Optimization is introduced as the leading optimizer. The obtained results highlight that Adaptive Chaotic Grey Wolf Optimization used for optimizing the training process of the MLP network with 15 neurons can predict the daily streamflow with the best standard error, mean square error, mean absolute percent error, mean absolute error, and normalized mean square error of about 5.3126, 2.3049, 0.6684, 1.1038, and 0.4483, respectively. [ABSTRACT FROM AUTHOR]

Electrical energy is one of the key components for the development and sustainability of any nation. India is a developing country and blessed with a huge amount of renewable energy resources still there are various remote areas where the grid supply is rarely available. As electrical energy is the basic requirement, therefore it must be taken up on priority to exploit the available renewable energy resources integrated with storage devices like fuel cells and batteries for power generation and help the planners in providing the energy-efficient and alternative solution. This solution will not only meet electricity demand but also helps reduce greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. In this paper, a modified grey wolf optimizer approach is utilized to develop a hybrid microgrid based on available renewable energy resources considering modern power grid interactions. The proposed approach would be able to provide a robust and efficient microgrid that utilizes solar photovoltaic technology and wind energy conversion system. This approach integrates renewable resources with the meta-heuristic optimization algorithm for optimal dispatch of energy in grid-connected hybrid microgrid system. The proposed approach is mainly aimed to provide the optimal sizing of renewable energy-based microgrids based on the load profile according to time of use. To validate the proposed approach, a comparative study is also conducted through a case study and shows a significant savings of 30.88% and 49.99% of the rolling cost in comparison with fuzzy logic and mixed integer linear programming-based energy management system respectively. • The article proposes a hybrid MG with renewable energy sources and energy storage systems (battery and fuel cell). • Develop a novel energy management system using modified grey wolf optimizer (MGWO) with multi objective functions. • Analyzes the load demand and the cost of electricity from the power grid for effective designing of MG at test locations. • A significant savings of 30.88% and 49.99% of the rolling cost in comparison with FL and MILP based EMS respectively. [ABSTRACT FROM AUTHOR]

The emergence of deregulation delivers a reliable operation of power system due to the fact that deregulated concept encourages competition among different power companies, thus avoiding monopoly. The present paper extends the idea of deregulation for a two-area hybrid power system establishing concurrent frequency-voltage control. The deregulated power system under consideration involves conventional-thermal, solar-thermal, wind-sources with appropriate non-linearities and present day electric-vehicles (EVs). This paper introduces a novel study showing the effects of large penetration of EVs with the utility grid. Furthermore, the optimal number of connected EVs for improved power system performance is examined. For establishing effective control, a 2nd order Active Disturbance Rejection-Controller (ADRC) with extended-state-observer is modelled as secondary controller. Magnetotacticbacteria optimization technique is used to determine the optimal parameter values. Simulation results depict a major reduction in the voltage and frequency deviations for proposed ADRC strategy with respect to industrially applied controllers. Also, a considerable reduction in steady state error values are obtained. Moreover, the ADRC gains prove to be robust enough under varying system conditions. The present study makes a way for further research on ADRC as a market ready controller. [Display omitted] • Develop a two-area deregulated power system integrating EVs for LFC-AVR operation • Design of 2nd order ADRC for maiden attempt in deregulated LFC-AVR power system • Verify the optimality of 2nd order ADRC w.r.t industrially applied controllers • Study the impact of EVs on the performance of the deregulated hybrid power system [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for utilizing bees algorithm in a model predictive control (MPC) in order to control a class of nonlinear systems. The bees algorithm is utilized in order to solve the open loop optimization problem (OOP), and it is based on the foraging behavior of honey bees. The proposed algorithm makes use of the bees algorithm for minimizing a predefined cost function in order to find the best input signals subject to constraints and a model of the system. The class of systems considered in this paper includes autonomous nonlinear systems without delay and with continuous and discrete inputs. The proposed algorithm is validated by simulating a three tank system as a case study. A comparison between the proposed novel MPC with different predictive horizons and a conventional MPC demonstrates the potential advantages of the proposed method such as reduction in computation time, good convergence toward desired values and ability of control management. Simulations also show the simplicity of applying and efficiency of the proposed algorithm for designing an MPC based on the bees algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple genetic algorithm (SGA) as an optimization tool for a wide variety of antenna applications. Over roughly the same period, researchers at the Illinois Genetic Algorithm Laboratory (IlliGAL) at the University of Illinois at Urbana Champaign have developed GA design theory and advanced GA techniques called competent genetic algorithms—GAs that solve hard problems quickly, reliably, and accurately. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems. This paper is composed of two parts. The first part of this paper summarizes previous research conducted by AFRL at Hanscom for which SGAs were implemented to obtain acceptable solutions to several antenna problems. This research covers diverse areas of interest, including array pattern synthesis, antenna test-bed design, gain enhancement, electrically small single bent wire elements, and wideband antenna elements. The second part of this paper starts by briefly reviewing the design theory and design principles necessary for the invention and implementation of fast, scalable genetic algorithms. A particular procedure, the hierarchical Bayesian optimization algorithm (hBOA) is then briefly outlined, and the remainder of the paper describes collaborative efforts of AFRL and IlliGAL to solve more difficult antenna problems. In particular, recent results of using hBOA to optimize a novel, wideband overlapped subarray system to achieve −35 dB sidelobes over a 20% bandwidth. The problem was sufficiently difficult that acceptable solutions were not obtained using SGAs. The case study demonstrates the utility of using more advanced GA techniques to obtain acceptable solution quality as problem difficulty increases. [Copyright &y& Elsevier]

Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances. [ABSTRACT FROM AUTHOR]

[Display omitted] • An intensive light trapping formalism based on interdigitated Graphene electrodes (IGE) engineering and embedded Gold nanoparticles is proposed. • An optoelectronic framework based on the Maxwell–Garnet approach along with the term Percus – Yevick correction has been developed. • The obtained results have proved the role of proposed light trapping formalism in enhancing the MSM photodetector sensing capability. • The built model has been investigated as the MOGA approach's fitness function to identify the optimum design parameters. • The optimization framework has shown that an enhancement of about 47.9 % and 65% in terms of responsivity and time response. In this paper, a novel 4H-SiC metal–semiconductor-metal photodetector design based on Graphene electrode engineering and gold nanoparticles is proposed. The benefits of using an intense light trapping formalism to improve the optical performance of the 4H-SiC PD are investigated by means of an accurate optoelectronic model. The developed model is based on the EMT and avoids the difficulty of considering all nanoparticles. The precision assessment of the built model is carried out by comparison with the experimental data. The findings shed light on the ability of the proposed design to realize the dual task of reducing the unwanted shadowing effect and improving the absorption through Graphene electrode formalism and the Surface Plasmon Resonance effect in the 4H-SiC layer. Furthermore, to optimize the design sensing capability, the proposed model is adopted to formulate fitness functions for the multi-objective genetic algorithm. It is found that for a filling fraction of 0.02 and 7.5 nm radius, the optimized design yields 50 % increase in absorption compared to the standard designs where responsivity of 458 mA/W, PDCR of 3.05 × 106 and response time of 4.7 µs are achieved. These findings confirm the outstanding ability of the proposed design approach to boost up the PD active area for low-cost and high sensing capability, making it valuable for optoelectronic application. [ABSTRACT FROM AUTHOR]

Abstract: The tilt angle of a solar energy system is one of the important parameters for capturing maximum solar radiation falling on the solar panels. This angle is site specific as it depends on the daily, monthly and yearly path of the sun. The accurate determination of the optimum tilt angle for the location of interest is essential for maximum energy production by the system. A number of methods have been used for determining the tilt angle at different locations worldwide. Keeping in view the relevance of the optimum tilt angle in energy production and reducing the cost of solar energy systems, the present study has been undertaken. This paper provides the update status of research and applications of various methods for determining solar panel tilt angle using different optimization techniques. The study shows that for maximum energy gain, the optimum tilt angle for solar systems must be determined accurately for each location. The review will be useful for designers and researchers to select suitable methodology for determining optimal tilt angle for solar systems at any site. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation into the application of an optimized Genetic Algorithm (GA) to solve the Thermal Unit Commitment (UC) problem. A Parallel structure was first developed to handle the infeasibility problem in a structured and improved GA which provides an effective search process and therefore greater economy. The proposed methodology resulted in a better performance with faster operation by using both computational methods and classification of unit characteristics. Typical constraints such as system power balance, minimum up and down times, start-up and shut-down ramps, have also been considered. A number of important parameters (standard and new parameters) of the UC problem have been identified. The proposed method is implemented and tested using a C# program. The tests are carried out using two systems including 10 and 20 units during a scheduling period of 24h. The results are finally compared with those obtained from genetic schemes in other similar investigations through which the effectiveness of the proposed scheme is affirmed. [Copyright &y& Elsevier]

Abstract: The paper proposes the variables sampling interval (VSI) scheme to monitor the means and the variances in two dependent process steps. The performance of the considered VSI control charts is measured by the adjusted average time to signal derived by a Markov chain approach. An example of the process control for the metallic film thickness of the computer connectors system shows the application and performance of the proposed VSI control charts in detecting shifts. Furthermore, the performance of the VSI control charts and the fixed sampling interval control charts are compared via the numerical analysis results. These demonstrate that the former is much faster in detecting shifts. Whenever quality engineers cannot specify the values of variable sampling intervals, the optimal VSI control charts are recommended. Furthermore, the impacts of misusing Shewhart charts to monitoring the process mean and variance in the second process step are also investigated. [ABSTRACT FROM AUTHOR]

Abstract: The optimum operational conditions of an air-to-air rotary regenerator (also called air preheater or heat wheel) for air conditioning applications which was designed and manufactured in Energy Systems Improvement Laboratory (ESIL) has been investigated in this paper. The performance of such a rotary regenerator was modeled and the numerical values of modeling output were verified with the experimental data obtained from the equipment testing. In the next step, the optimum operational conditions of the rotary regenerator were obtained using genetic algorithm optimization technique subject to a list of constraints. The objective function in the optimization technique was the thermal effectiveness, while the design parameters (decision variables) were volumetric flow rates of cold and hot air streams, matrix rotational speed, and the exchanger frontal area (heat transfer surface area). The apparatus was tested under the optimized operating conditions and the results were compared with the results obtained numerically applying genetic algorithm optimization. The experimental value for the effectiveness showed an acceptable closeness (2.07%) with the corresponding value obtained from the system modeling and optimization. The economic analysis of energy savings by the designed and manufactured regenerator showed a pay back period about 3 years. [Copyright &y& Elsevier]

In this paper, an inverse problem of heat transfer in the glass forming process is studied to determine a number of unknown heat transfer coefficients. An analysis program for transient heat conduction is developed to simulate the forming and cooling processes. The analysis involves the contact and separation of glass and molds and it is repeated until the process reaches a periodic state. Temperature measurements are taken at several available times and spatial positions of glass and molds during the forming operation. Heat removal by the cooling water from the plunger is also recorded. An optimization problem is formulated to determine the heat transfer coefficients that minimize the difference between the measured data and the analysis results. Significant time savings are achieved in computing finite difference-based sensitivity during the optimization by employing a distributed computing techniques over a cluster of computers. The analysis results of the optimum heat transfer coefficients are found to agree well with the measured data. [Copyright &y& Elsevier]

A district-cooling system (DCS) has been applied in a number of countries where chilled water from a central plant is delivered through a distribution network to groups of buildings in an urban district. Because of the expected considerable investment and lengthy payback period, well-planned and optimized system design and operation are crucial areas leading to the success of the implementation. Much saving can be achieved when the plant serves a group of buildings with diversifying daily cooling-load patterns. Among various design factors and solution schemes, one important planning decision is therefore to determine the desirable mix of building types, within the district of interest, to be served by the DCS. An approach to determine this optimal mix through the use of genetic algorithm (GA) was described in this paper. The thermal-load modeling technique and the objective function for optimization were derived. The case studies showed that the method was effective to give optimal or near-optimal solutions. [Copyright &y& Elsevier]

In this paper, four procedures for optimizing inductive coil design are presented to reduce computational time and lead time. First, a theoretical coil design is proposed by defining the quantitative relationship between billet length and coil length. Second, the numerical simulation of the induction heating process using a general purpose finite element analysis code, ansys, is carried out to predict the temperature distribution. Third, an objective function on the basis of the optimization process for the coil design is proposed by introducing an optimization technique. Finally, the results of the optimal coil design are also applied to the induction heating process to obtain a fine globular microstructure. The proposed objective function based on the computational techniques and numerical simulation model would contribute to producing thixoformed parts with good mechanical properties and reducing lead time. [Copyright &y& Elsevier]

This paper presents the direct kinematics solutions for a novel 3-PRS parallel mechanism. The geometric method is applied to formulate three coupled trigonometric equations. One algorithm for solving the three coupled equations is to use the Bezout’s elimination method which leads to a total of 64 solutions. By categorizing the 64 solutions into six groups and further examining the corresponding configuration for each group, the preferred solution to the direct kinematics problem can be obtained. The other approach for solving the coupled equations is to apply the optimization techniques with side and behavior constraints set by analyzing undesired configurations. This approach can obtain the preferred solution without checking all the possible solutions. Moreover, it can perform more efficiently than the elimination method. [Copyright &y& Elsevier]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]