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Short term scheduling of multiple grid-parallel PEM fuel cells for microgrid applications

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2010年第20期35卷 11099-11106页

作者： El-Sharkh, M. Y. Rahman, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

This paper presents a short term scheduling scheme for multiple grid-parallel PEM fuel cell power plants (FCPPs) connected to supply electrical and thermal energy to a microgrid community. As in the case of regular power plants, short term scheduling of FCPP is also a cost-based optimization problem that includes the cost of operation, thermal power recovery, and the power trade with the local utility grid. Due to the ability of the microgrid community to trade power with the local grid, the power balance constraint is not applicable, other constraints like the real power operating limits of the FCPP, and minimum up and down time are therefore used. To solve the short term scheduling problem of the FCPPs, a hybrid technique based on evolutionary programming (EP) and hill climbing technique (HC) is used. The EP is used to estimate the optimal schedule and the output power from each FCPP. The HC technique is used to monitor the feasibility of the solution during the search process. The short term scheduling problem is used to estimate the schedule and the electrical and thermal power output of five FCPPs supplying a maximum power of 300 kW. (C) 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

关键词： PEM fuel cell Short term scheduling evolutionary programming Hill climbing Distributed generation Unit commitment

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Influence of the number and location of design parameters in the aerodynamic shape optimization of a transonic aerofoil and a wing through evolutionary algorithms and support vector machines

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ENGINEERING OPTIMIZATION 2017年第2期49卷 181-198页

作者： Andres-Perez, Esther Gonzalez-Juarez, Daniel Martin-Burgos, Mario J. Carro-Calvo, Leopoldo Salcedo-Sanz, Sancho Ingn Sistemas Def Espana SA ISDEFE INTA Dept Engn Torrejon De Ardoz Madrid Spain Tech Univ Madrid UPM Torrejon De Ardoz Madrid Spain Spanish Natl Inst Aerosp Technol INTA Fluid Dynam Branch Torrejon De Ardoz Madrid Spain Univ Alcala UAH Dept Signal Theory & Commun Alcala De Henares Spain

Surrogate-based optimization (SBO) has recently found widespread use in aerodynamic shape design owing to its promising potential to speed up the whole process by the use of a low-cost objective function evaluation, to reduce the required number of expensive computational fluid dynamics simulations. However, the application of these SBO methods for industrial configurations still faces several challenges. The most crucial challenge nowadays is the 'curse of dimensionality', the ability of surrogates to handle a high number of design parameters. This article presents an application study on how the number and location of design variables may affect the surrogate-based design process and aims to draw conclusions on their ability to provide optimal shapes in an efficient manner. To do so, an optimization framework based on the combined use of a surrogate modelling technique (support vector machines for regression), an evolutionary algorithm and a volumetric non-uniform rational B-splines parameterization are applied to the shape optimization of a two-dimensional aerofoil (RAE 2822) and a three-dimensional wing (DPW) in transonic flow conditions.

关键词： Surrogate-based optimization aerodynamic shape design evolutionary programming support vector machines

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Clan-based evolutionary approach for solving control problems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 1996年第6期31卷 41-60页

作者： Babu, GP Murty, MN Ram, BR INDIAN INST SCI DEPT COMP SCI & AUTOMAT BANGALORE 560012 KARNATAKA INDIA HUMAYUN NAGAR HYDERABAD ANDHRA PRADESH INDIA INDIAN INST SCI DEPT COMP SCI BANGALORE KARNATAKA INDIA

This paper presents a dan-based evolutionary approach for solving control problems. Three selected control problems, viz. linear-quadratic, harvest, and push-cart problems, are solved using the proposed approach. Results are compared with those of the evolutionary programming (EP) approach. In most of the cases, the proposed approach is successful in obtaining (near) optimal solutions for these selected problems.

关键词： control problems optimization evolutionary programming genetic algorithms clan

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Contribution to design of complex mechatronic systems. An approach through evolutionary optimization

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2005年第1期42卷 1-25页

作者： Sakka, S Coiffet, P Natl Inst Adv Ind Sci & Technol AIST Tsukuba ISI JRL Tsukuba Ibaraki Japan Univ Versailles LRV Velizy Villacoublay France

This paper describes a new evolutionary methodology aimed at optimizing various and heterogeneous data in common evolution. The representation of solutions uses mixed-integer genotypes and variable-length chromosomes to face a complex problem of task decomposition and high-level control generation. A memory operator is introduced to face convergency uncertainties issued from the irregularities of both discontinuous evaluation function and heterogeneous solution representation. The stability of the evolutionary algorithm is analyzed with dimension changes in the optimization problem.

关键词： evolutionary programming multi-criteria optimization mixed-integer genotypes variable-length chromosomes preliminary design control trajectory planning generator

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Application and comparison of metaheuristic techniques to generation expansion planning problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2005年第1期20卷 466-475页

作者： Kannan, S Slochanal, SMR Padhy, NP Arulmigu Kalasalingam Coll Engn Dept Elect Engn Krishnankoil Tamilnadu India Thiagarajar Coll Engn Dept Elect Engn Madurai Tamil Nadu India Indian Inst Technol Dept Elect Engn Roorkee Uttar Pradesh India

This paper presents both application and comparison of the metaheuristic techniques to Generation Expansion Planning (GEP) problem. The Metaheuristic techniques such as the Genetic Algorithm, Differential Evolution, evolutionary programming, evolutionary Strategy, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Simulated Annealing, and Hybrid Approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods Virtual Mapping Procedure (VMP) and Penalty Factor Approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, Intelligent Initial Population Generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional Dynamic programming and the effectiveness of each proposed methods has also been illustrated in detail.

关键词： ant colony optimization combinatorial optimization differential evolution dynamic programming evolutionary programming evolutionary strategy generation expansion planning genetic algorithm hybrid approach metaheuristics particle swarm optimization simulated annealing Tabu search

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evolutionary algorithms and their applications to engineering problems

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12363-12379页

作者： Slowik, Adam Kwasnicka, Halina Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 St PL-75453 Koszalin Poland Wroclaw Univ Sci & Technol Dept Computat Intelligence Wybrzeze Wyspianskiego 27 St PL-51370 Wroclaw Poland

The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

关键词： Nature-inspired methods Genetic algorithm Genetic programming Differential evolution Evolution strategy evolutionary programming Real-life applications

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Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-before-detect systems

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SOFT COMPUTING 2013年第6期17卷 1095-1106页

作者： Mazurek, Przemyslaw West Pomeranian Univ Technol Dept Signal Proc & Multimedia Engn PL-71126 Szczecin Poland

Track-before-detect (TBD) algorithms are used for tracking systems, where the object's signal is below the noise floor (low-SNR objects). A lot of computations and memory transfers for real-time signal processing are necessary. GPGPU in parallel processing devices for TBD algorithms is well suited. Finding optimal or suboptimal code, due to lack of documentation for low-level programming of GPGPUs is not possible. High-level code optimization is necessary and the evolutionary approach, based on the single parent and single child is considered, that is local search approach. Brute force search technique is not feasible, because there are N! code variants, where N is the number of motion vectors components. The proposed evolutionary operator-LREI (local random extraction and insertion) allows source code reordering for the reduction of computation time due to better organization of memory transfer and the texture cache content. The starting point, based on the sorting and the minimal execution time metric is proposed. The unbiased random and biased sorting techniques are compared using experimental approach. Tests shows significant improvements of the computation speed, about 8 % over the conventional code for CUDA code. The time period of optimization for the sample code is about 1 h (1,000 iterations) for the considered recursive spatio-temporal TBD algorithm.

关键词： evolutionary programming Code optimization GPGPU Track-before-detect algorithm

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Cooperative coevolution of generalized multi-layer perceptrons

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NEUROCOMPUTING 2004年第1期56卷 257-283页

作者： García-Pedrajas, N Ortiz-Boyer, D Hervás-Martínez, C Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

In this work we present the cooperative coevolution of multi-layer generalized perceptrons. This model is based on the cooperation of different subpopulations of modules, each one being a generalized multi-layered perceptron. In some previous works we have developed a modular cooperative coevolutive model for evolving multi-layer perceptrons with two hidden layers. This model performs very well but tends to generate big networks. In the present paper we show the results of substituting these multi-layer perceptions by generalized multi-layer perceptrons, which allow a more compact representation of networks. The use of generalized multi-layer perceptrons improved the performance of the evolutionary model with regard to the evolution of other kinds of networks. Another improvement was that the networks obtained were much smaller. The comparison proved statistically significant by means of a Student's t-test. (C) 2003 Elsevier B.V. All rights reserved.

关键词： cooperative coevolution generalized multi-layer perceptron topology automatic design evolutionary programming

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A study of cost-optimized operation of a grid-parallel PEM fuel cell power plant

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IEEE TRANSACTIONS ON POWER SYSTEMS 2006年第3期21卷 1104-1114页

作者： El-Sharkh, M. Y. Tanrioven, M. Rahman, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

A cost-based approach is developed to study the economics of operation of the proton exchange membrane (PEM) fuel cell power plants (FCPP). This paper includes the operational cost, thermal recovery, power trade with,the local grid, and hydrogen production. The cost-based approach is used to determine the optimal operational strategy that yields a minimum operating cost. The optimal operational strategy is achieved through the estimation of the hourly generated power, the amount of thermal power recovered from the FCPP to satisfy the thermal load, the amount of power trade with the local grid, and the amount of hydrogen that can be generated from the FCPP. An evolutionary programming-based technique is used to determine the optimal operational strategy. The strategy is tested using electrical and thermal load profiles. Results are encouraging and indicate the effectiveness of the proposed approach.

关键词： evolutionary programming fuel cell fuel cell cost-based approach proton exchange membrane (PEM) fuel cell

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A solution to unit commitment problem using fire works algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 77卷 221-227页

作者： Saravanan, B. Kumar, C. Kothari, D. P. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India Bannari Amman Inst Technol Dept EEE Sathyamangalam Tamil Nadu India Gaikwad Patil Grp Res Nagpur Maharashtra India

This paper presents a new approach using swarm intelligence algorithm called Fireworks Algorithm applied to determine Unit Commitment and generation cost (UC) by considering prohibited operating zones. Inspired by the swarm behaviour of fireworks, an algorithm based on the explosion (search) process and the mechanisms of keeping the diversity of sparks has been developed to minimize the total generation cost over a given scheduled time period and to give the most cost-effective combination of generating units to meet forecasted load and reserve requirements, while adhering to generator and transmission constraints. The primary focus is to achieve better optimization while incorporating a large and often complicated set of constraints like generation limits, meeting the load demand, spinning reserves, minimum up/down time and including more realistic constraints, such as considering the restricted/prohibited operating zones of a generator. The generating units have certain ranges where operation is restricted based upon physical limitations of machine components or instability, e.g., due to steam valve or vibration in shaft bearings. Therefore, prohibited operating zones as a prominent constraint must be considered. In this paper the incorporating of complicated constraints of an optimization problem into the objective function is not considered by neglecting the penalty term. Numerical simulations have been carried out on 10 - unit 24 - hour system. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Swarm intelligence Prohibited operating zone Unit commitment evolutionary programming Spinning reserve Fireworks algorithm

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