A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

Particle Swarm Optimization(PSO) algorithm is population-based and it is effective for multi-objective optimization problems.For the convergence of the swarm makes the classical algorithm easily converge to local pareto front,the convergence and diversity of the solution are not satisfactory.This paper proposed an independent dynamic inertia weights method for multi-objective particle swarm optimization(DWMOPSO).It changed each particle's inertia weight according to the evolution speed which was calculated by the value of each particle's best fitness in the history.It improved the probability to escape from the local optima.In comparison with Coello's MOPSO through five standard test functions,the solution of the new algorithm has great improvement both in the convergence to the true Pareto front and diversity.

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

As for the elastic layout of the direct-driven electric wheel system, the electromechanical coupling between the electromagnetic excitation of in-wheel driving motor and the weak damping system gives rise to negative vibration issues, which further deteriorate the dynamic performance of the electric vehicle. This paper presents a multi-objective optimization method for active suspension system to solve these issues by developing an integrated in-wheel motor electrical vehicles model. The unbalanced electromagnetic excitation from in-wheel driving motor is investigated by means of analytical and finite element methods. The Pareto solution set of optimal parameters are generated by the multi-objective particle swarm optimization method, and a comparison in vehicle dynamic performances is made to verify the targeted optimization method. The simulation results indicate that the optimized active suspension system attenuates the sensitivity of the vehicle system to electromagnetic excitation with a satisfactory balancing between vehicle ride comfort and stability as well as active suspension utilization.

Microgrid systems can experience prolonged outages, load shed, and also can affect the resilience of Microgrid, when there are unexpected contingencies due to human error or natural disasters. Integrating Battery Energy Storage (BES) is necessary to increase the resilience of the Microgrid system by recovery the load because it can act as an additional generator and can return the power supply to the load directly in the event of a contingency. Integration BES needs an optimal location and capacity determination plan. In this paper, by using the Hybrid Multi-Objective Particle Swarm Optimization (H-MOPSO) method which is a combination of Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA II) methods determine optimal BES placement. The objective function used in this research is to maximize annual benefits costs and minimize Load shedding under contingency conditions. This paper also uses Linear Distribution Factor (LODF) to calculate the security constraint of the Microgrid system, which will be used to manage the line limit.

This paper develops a new framework to coordinate the charging process of plug-in hybrid electric vehicles (PHEVs) in the context of energy hubs. Attempts are focused not only on the impact evaluation of PHEVs on the system technical performance, but also customers' preferences on the charging patterns. PHEVs coordinator agent (PCA) is proposed to run a multiobjective optimization (MO) framework for the optimal charging patterns of PHEVs from the vehicle owners' and system operator's viewpoint. Wind energy utilization for PHEVs charging, energy costs at the hub input layer, and customers' convenience are all taken into account as the optimization objectives. Due to its promising performance in dealing with MO problems, the multiobjective particle swarm optimization method is employed to evaluate the optimization problem. The 2-point estimation method is used to model the existence uncertainties. The proposed framework is applied to the modified IEEE 34-node test system, and the obtained results demonstrate the efficiency and applicability of the proposed approach in real-world scenarios.

A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

The resolution of the environment pollution depends on renewable energy sources, such as wind energy systems. These systems face transient and voltage stability issues with wind energy employing fixed-speed induction generators to be augmented with resistive type Superconducting Fault Current Limiter (SFCL) and Superconducting Magnetic Energy Storage (SMES) devices. The use of a combined model based on SFCL and SMES for promoting transient and voltage stability of a multi-machine power system considering the fixed-speed induction generators is the primary focus of this study. Our contribution is the development of a new model that combines the advantages of SFCL and SMES. The proposed model functions assure flexible control of reactive power using SMES controller while reducing fault current using superconducting technology-based SFCL. The effectiveness of the proposed combined model is tested on the IEEE11-bus test system applied to the case of a three-phase short circuit fault in one transmission line.

This paper is devoted to a general methodological study on sensitivity analysis and robust optimization for a planar crank-slider mechanism in presence of joint clearances and random parameters and investigate the effects of parameter uncertainty on optimization results when joint clearance sizes are constantly changing due to wear. The first-order sensitivity analysis based on the response surface proxy model is performed. Then, a multiobjective robust optimization algorithm based on sensitivity analysis is carried out to reduce the undesirable effects of joint clearances and random parameters. In the algorithm, a multiobjective robust optimization model derived from the mean and variance of the objective function is constructed. Here, the objective function is defined based on the consideration of reducing the contact force generated at all clearance joints. Additionally, in order to balance computational accuracy and efficiency in the multiobjective robust optimization process, high-precision Kriging agent models are established. The optimum values of design variables are determined by combining Monte Carlo sampling and multiobjective particle swarm optimization method. By combining the Baumgarte approach with Lankarani–Nikravesh contact force model and Coulomb friction model, the dynamic equations of the planar multibody system with clearance joints are established. The uniform probability distribution is applied for characterizing random parameters. Simulation results show that the influence of design variable variations on the objective function changes in relation to the joint clearance size, but their relative influence degree on the objective function will not vary with the size of joint clearances. Moreover, the optimal solution selected on the Pareto front will affect the average levels and peak fluctuations of the dynamic responses in multibody systems.

This paper proposes an optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO) for addressing the operational optimization challenges of Combined Cooling, Heating, and Power (CCHP) integrated energy systems. CCHP systems enhance energy efficiency and reduce environmental pollution, thus possessing significant economic and environmental benefits. Despite existing research progress, current methodologies still inadequately address the simultaneous optimization of economic and environmental aspects. The key contributions of this research include constructing an operational optimization model for the CCHP system, improving the MOPSO algorithm, specifically in terms of inertia weight, learning factors, and individual optimal values, and applying these improvements to solve the model. The theoretical foundations of the CCHP system, multi-objective optimization problems, principles of Particle Swarm Optimization (PSO), and the characteristics and advantages of MOPSO are discussed comprehensively. The optimization model targets minimizing economic costs and optimizing environmental performance, clearly defining decision variables and constraints, and rigorously evaluating MOPSO algorithm applicability. A detailed procedure for constructing and solving the optimization model is provided. A case study is conducted by establishing background information, setting system parameters, configuring MOPSO algorithm parameters, and performing the optimization. Results are thoroughly analyzed, comparing the method's effectiveness against other optimization methods to validate its superiority. The study concludes that this approach effectively optimizes CCHP operations, providing a reference for coordinated planning in integrated energy systems, and discusses future research directions.

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

Nowadays, the use of superconductive magnetic energy storage (SMES) devices in grid integration are highly escalated with the technological amelioration of magnetic components. The power quality and energy conversion efficiency of the power conditioning system of the SMES and renewable energy systems depend on the control algorithms of the voltage source inverter (VSI). However, the traditional control schemes cause inferior response and conversion efficiency. In this paper, an advanced control technique named PI+LLC controller is proposed, which is based on the proportional integral (PI) controller and lead-lag compensator (LLC). The proposed control technique offers significant reduction in the total harmonic distortion (THD), superior dynamic response, smooth response against fault, excellent reference tracking capability of grid current and improved power quality performance at both inverter side and grid side for a 5-level neutral point clamped inverter based grid-tied photovoltaic system. It is expected that, the proposed control scheme can also be used to mitigate the excessive heat of the VSI based SMES system by improving the performance of the power conditioning system. The performance of the proposed control technique is evaluated in MATLAB/Simulink environment to validate the excellent features of the proposed control scheme.

With the rapid development of clean and renewable energy technology, energy storage devices are more eagerly required. The applicable high temperature superconducting (HTS) materials achieved arouse the superconducting magnetic energy storage (SMES) devices having unique properties to play a substantial role. Superior characteristics have made the SMES technology attractive and a perspective option to practical applications broadly, especially for smart grids (SGs). SMES technology is described and verified including principle, circuit topology, control strategy, and device performance to form a comprehensive understanding of the emerging energy storage technology using the advanced HTS material and associated technology. SMES application is then introduced with the emphasis to develop relevant concepts to suit smart grids (SGs).

The increase in aircraft passengers and airfreight traffic has given rise to concerns about greenhouse gas emissions for traditional aircraft and the resulting damage to the environment. This has led several companies and organizations, including NASA, to set goals to enhance aircraft efficiency as well as reduce fuel burn, pollution, and noise for commercial aircraft. The most notable electric aircraft (EA) concept is the N3-X, which was developed by NASA to achieve environmental goals while maintaining the annual growth of the aviation industry. However, one of the main challenges that EA is facing is their overall weight. This paper proposes and explores an improved power system architecture for use in EA, based on the N3-X concept. The number of superconducting magnetic energy storage (SMES) and fault current limiter (FCL) devices required can be reduced by utilizing multifunctional superconducting devices that combine the functionalities of both a SMES and a FCL, thus reducing the weight and cost of the EA by eliminating a complete device. The proposed control technique offers greater flexibility in determining the appropriate size of coils to function as a FCL, based on the fault type. The proposed EA power system architecture including the SMES-FCL devices is modelled in Simulink/MATLAB to test the system performance under different failure scenarios.

The Superconducting Magnetic Energy Storage (SMES) device is gaining significance in utility applications, as it can handle high power values with a fast rate of exchanging energy at high efficiency. The large-scale invasion of renewable energy systems into grid give rise to many integration issues. This SMES can handle these issues due to its various features. In this study the role of SMES in tackling stability issues due to presence of wind generation farms is investigated. The Doubly Fed Induction Generators (DFIG) based wind farms are most popular due to their distinctive advantages. Their intermittent output power and sensitivity to the grid disturbances gives rise to stability issues. These problems require innovative and advanced control strategies. Here coupling of SMES with Static Compensator (STATCOM) (STAT-SMES) has been suggested for boosting the transient stability of the hybrid large-scale DFIG and Synchronous Generator (SG) system. For investigation different faults and fast speed wind variation are considered. This coupled controller can compensate the real and reactive powers along with fast damping of oscillations under test conditions of entire system. The chopper link of SMES is controlled using Fuzzy based intelligent system and Proportional Integral (PI) controller. The connection of SMES to STATCOM shows better damping and improvement in all variables. The comparative study among Fuzzy based STAT-SMES, PI based STAT-SMES, only STATCOM, and without any controller is performed using MATLAB/Simulink. The simulation outcomes indicate that proposed Fuzzy based controller is better than other control systems in different test conditions.