Sharif Digital Repository

Owing to wind power inherent characteristics and technical constraints of power systems operation, a considerable amount of wind energy cannot be delivered to load centers and gets curtailed. Transmission congestion together with temporal mismatch between load and available wind power can be accounted as the main reasons for this unpleasant event. This paper aims to concentrate on the wind energy curtailment for which it provides a combinatorial planning model to maximize wind power utilization. Jointly operating the wind power generation system with pumped hydro energy storage (PHES), the planning procedure tries to reach schemes with the minimum level of wind energy curtailment as well as... 

With increment in the penetration of wind energy in power systems, the necessity of considering its impacts on transmission expansion planning (TEP) studies, especially for large scale wind farms, is inevitable. A new multi-objective (MO) optimization transmission expansion planning algorithm considering wind farm generation is presented in this two-paper set. Part I is mainly devoted to derivation of the theory and algorithm. The objective functions used in the TEP studies take into account investment cost, risk cost and congestion cost. The combination of Monte Carlo simulation (MCS) and Point Estimation Method (PEM) is implemented to investigate the effects of network uncertainties. Due... 

We consider a multi-objective linear programming model with type-2 fuzzy objectives. The considered model has the flexibility for the user to specify the more general membership functions for objectives to reflect the inherent fuzziness, while being simple and practical. We develop two solution strategies with reasonable computing costs. The additional cost, as compared to the type-1 fuzzy model, is indeed insignificant. These two algorithms compute Pareto optimal solutions of the type-2 problems, one being based on a maxmin approach and the other on aggregating the objectives. Finally, applying the proposed algorithms, we work out two illustrative examples  

This research studies multi-objective master production schedule (MPS) and advanced order commitment (AOC) in two-stage supply chains. Simulation-based experimental analysis evaluates the impact of environmental and MPS design factors on schedule cost and instability. The results provide insight into multi-objective MPS design considerations through rolling schedule policies. The study reveals that the manufacturers production smoothness utility coefficient and its interaction with other experimental factors considerably impact on the systems performance. In addition, it introduces a simulation framework with embedded mixed integer programming models that could be used as a framework for... 

In view of its special features, the brushless doubly fed induction generator (BDFIG) shows high potentials to be employed as a variable-speed drive or wind generator. However, the machine suffers from low efficiency and power factor and also high level of noise and vibration due to spatial harmonics. These harmonics arise mainly from rotor winding configuration, slotting effects, and saturation. In this paper, analytical equations are derived for spatial harmonics and their effects on leakage flux, additional loss, noise, and vibration. Using the derived equations and an electromagnetic-thermal model, a simple design procedure is presented, while the design variables are selected based on... 

Photovoltaic-electrolyzer systems are one of the most promising alternatives for obtaining hydrogen from a renewable energy source. Determining size and the operational conditions are always a key issue while coupling directly renewable electricity sources to PEM electrolyzer. In this research, the multi objective optimization approach based on an imperialist competitive algorithm (ICA), which is employed to optimize the size and the operating conditions of a directly coupled photovoltaic (PV)-PEM electrolyzer. This allows the optimization of the system by considering two different objectives, including, minimization of energy transfer loss and maximization of hydrogen generation. Multi... 

The integration of materials selection and design are essential to the success of new product development, especially when applied to biomedical devices. The knee prosthesis, like any other implant, is a product that still lacks satisfactory design solutions for solving the problem of aseptic loosening. Stress shielding is one of the main causes of aseptic loosening that is intimately related to the overall design of the knee prosthesis. The design of the location pegs in the femoral component of the knee prosthesis is seen to have a critical effect on the stress shielding. In this study, therefore, different combinations of location peg geometries and material designs were assessed using... 

This paper focuses on multi-objective optimisation (MOO) to optimise the planar solid oxide fuel cell (SOFC) stacks performance using a genetic algorithm. MOO problem does not have a single solution, but a complete Pareto curve, which involves the optional representation of possible compromise solutions. Here, two pairs of different objectives are considered as distinguished strategies. Optimisation of the first strategy predicts a maximum power output of 108.33 kW at a breakeven per-unit energy cost of 0.51 $/kWh and minimum breakeven per-unit energy cost of 0.30 $/kWh at a power of 42.18 kW. In the second strategy, maximum efficiency of 63.93%at a breakeven per-unit energy cost of 0.42... 

The design process always has some known or unknown uncertainties in the design variables and parameters. The aim of robust design is minimization of performance sensitivity to uncertainties. Tolerance allocation process can significantly affect quality and robustness of the product. In this paper, a methodology to minimize a product's sensitivity to uncertainties by allocating manufacturing tolerances is presented. The robust tolerance design problem is formulated as a multi-objective optimization based on the combined function-uncertainty-cost model. Genetic algorithm is utilized to solve the multi-objective optimization and a case study is presented to illustrate the methodology  

An important manufacturing technology spectrum is to consider rapid and sensitive assemblies through convergent product (CP) in digital environment. We integrate functions and sub-functions using a comprehensive mathematical optimization process. To form the CP, a web-based fuzzy network is considered in which a collection of base functions and sub-functions configure the nodes and each arc in the network is to be a link between two nodes. The aim is to find an optimal tree of functionalities in the network adding value to the product in the web environment. First, a purification process is performed in the product network to assign the links among bases and sub-functions. Then, fuzzy... 

Distribution companies try to meet different goals such as increasing profitability, reducing investment and improving reliability indices. Therefore, distribution network planning has converted into complex multi-objective optimization problems. Mathematicians and operation research experts have developed many methods which can be used for solving these problems. Despite all efforts to develop applicable approaches for these problems, there is still no general consensus on project selection policy. Evaluating the strengths and weaknesses of each network optimization strategy selection in pragmatic manner is the objective of current research. In this paper, many methods used in network... 

In this work a microgrid is established within the geographical perspective of interest. The characteristics of the grid and its urban are employed to determine the candidate points for establishment of EV charging stations. The investment costs and operation costs are calculated for each station realizing the required constraints. This process resulted in optimum selection of EV stations. The optimization is achieved based on two different types of preliminary assumptions. In the first one, it is assumed to have only one station, while in the second one possibility of having multi-stations is considered. Using the total investment and operation costs as the objective function of... 

In this study, a bi-objective multi-state redundancy allocation problem of series-parallel systems consisting of some serial subsystems, each with non-repairable components in parallel, is investigated. Furthermore, due to uncertainty involved, both the performance rates and the availabilities of components are considered fuzzy. In addition, two strategies of all-unit and incremental-quantity discounts are used to purchase the components and that the fuzzy universal generating function (FUGF) is employed to evaluate the system availability. The aim is to find the optimal redundancy so as within limited budget and system weight the maximum system availability is obtained while the total cost... 

The main purpose of the present work is multi-objective shape optimization of a projectile tip for impacting and normal penetrating. Velocity drop, weight and inner volume of projectile have been considered as three conflicting objective functions. For this purpose, at the first step, finite element modeling was done using ABAQUS/Explicit and projectile penetration was examined in different geometric dimensions. Hammersley sequence sampling was employed for designing computer experiments. In the next step, results of the FEM were employed as raw data for MLF-type neural network training to achieve a mathematical model which is able to describe velocity drop behavior. Projectile weight and... 

In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests... 

The objective of this research is geometrical and kinematical optimization of full-toroidal continuously variable transmission (CVT) in order to achieve high power transmission efficiency and low mass. At first, a dynamic analysis is performed for the system. A computer model is developed to simulate elastohydrodynamic (EHL) contact between disks and roller and consequently, calculate CVT efficiency. The validity of EHL model is investigated by comparing output of this model and experimental data. Geometrical parameters are obtained by means of Particle Swarm Optimization algorithm, while the optimization objective is to maximize CVT efficiency and minimize its mass. The algorithm is run for... 

In this paper sustainability analysis (exergy, environmental and economic) and multi-objective optimization for an aromatic plant are provided and interactions between decision variables are discussed. Environmental evaluation shows that the cancer human toxicity and global warming are the most important environmental concerns and the weight of EIs (environmental impacts) are mainly due to process wastes. The optimizations results demonstrate parameters like reactor temperature have a wide range in the optimizations while some variables, such as extraction unit variables have the same value. Utility EI reduction occurred in economic and exergy optimizations rather than environmental... 

Most of the reported robust and non-robust optimization works are formulated based on a single-objective optimization, commonly in terms of net present value. However, variation of economical parameters such as oil price and costs forces such high computational optimization works to regenerate their optimum water injection policies. Furthermore, dynamic optimization strategies of water-flooding often lack robustness to geological uncertainties. This paper presents a multi-objective while robust optimization methodology by incorporating three dedicated objective functions. The goal is to determine optimized and robust water injection policies for all injection wells. It focuses on reducing... 

A redundancy allocation problem consists of the selection of a number of redundancies to be allocated in each subsystem of a series-parallel configuration to maximize system reliability, while minimizing overall system cost. Most existing studies either ignore system cost or assume deterministic component reliabilities. However, the selection of a network system design requires the estimation of its reliability and cost. Therefore, the uncertainty associated with such estimates must be considered in the decision process. In this study, an efficient method based on the loss function approach is proposed to solve the redundancy allocation problem. This approach not only optimizes the overall... 

This paper presents a biomechanical model with seven degrees of freedom as a seated human exposed to vertical vibration. Experimental data is used to find the stiffness and damping parameters of the model. The data includes values of seat to head transmissibility, driving point mechanical impedance and apparent mass. The multi-objective function is used to obtain theoretical results similar to three different experimental cases. The unknown coefficients are calculated by a genetic algorithm. Improved results, in comparison with previous models, are achieved from the presented model. Next, this modified model is assembled on a quarter car and parameters of the cushion and the suspension...