The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively...
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The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.
This work aims at assessing the acoustic efficiency of different thin noise barrier models. These designs frequently feature complex profiles and their implementation in shape optimization processes may not always be ...
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This work aims at assessing the acoustic efficiency of different thin noise barrier models. These designs frequently feature complex profiles and their implementation in shape optimization processes may not always be easy in terms of determining their topological feasibility. A methodology to conduct both overall shape and top edge optimizations of thin cross section acoustic barriers by idealizing them as profiles with null boundary thickness is proposed. This procedure is based on the maximization of the insertion loss of candidate profiles proposed by an evolutionary algorithm. The special nature of these sorts of barriers makes necessary the implementation of a complementary formulation to the classical Boundary Element Method (BEM). Numerical simulations of the barriers' performance are conducted by using a 2D Dual BEM code in eight different barrier configurations (covering overall shaped and top edge configurations;spline curved and polynomial shaped based designs;rigid and noise absorbing boundaries materials). While results are achieved by using a specific receivers' scheme, the influence of the receivers' location on the acoustic performance is previously addressed. With the purpose of testing the methodology here presented, a numerical model validation on the basis of experimental results from a scale model test [34] is conducted. Results obtained show the usefulness of representing complex thin barrier configurations as null boundary thickness-like models. (C) 2014 Elsevier Ltd. All rights reserved.
In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective evolutionary algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Ea...
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In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective evolutionary algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Each MOEA apply different method to handle the difficulty of increasing objectives. Performance metrics ensemble exploits a number of performance metrics using double elimination tournament selection and provides a comprehensive measure revealing insights pertaining to specific problem characteristics that each MOEA could perform the best. Experimental results give detailed information for performance of each MOEA to solve many-objective optimization problems. More importantly, it shows that this performance depends on two distinct aspects: the ability of MOEA to address the specific characteristics of the problem and the ability of MOEA to handle high-dimensional objective space. (C) 2014 Elsevier Ltd. All rights reserved.
In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or remo...
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In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned with the installation or removal of reactive power equipment in power system. This article proposes an effective way to find out the optimal parameter settings related to the system active power loss as well as operational cost. Genetic Algorithm (GA)-based strategy is applied to determine the optimal values of reactive power generation of generators, size of shunt capacitors, transformer tap settings prior to minimizing the system operating cost due to active power loss, installation cost of shunt capacitors at the weak nodes, and the cost of line-charging elements. The proposed approach is applied to IEEE 30 and IEEE 57 bus test system. Finally, a comparative analysis has been done to validate the efficacy of GA-based approach with some well-established meta-heuristic algorithms.
A hybrid algorithm of evolutionary optimization, called hybrid differential evolution (HDE), is developed in this study. The acceleration phase and migration phase are embedded into the original algorithm of different...
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A hybrid algorithm of evolutionary optimization, called hybrid differential evolution (HDE), is developed in this study. The acceleration phase and migration phase are embedded into the original algorithm of differential evolution (DE). These two phases are used to improve the convergence speed without decreasing the diversity among individuals. With some assumptions, this hybrid method is shown as a method using N-p parallel processors of the two member evolution strategy, where N-p is the number of individuals in the solution space. The multiplier updating method is introduced in the proposed method to solve the constrained optimization problems. The topology of the augmented Lagrange function and the necessary conditions for the approach are also inspected. The method is then extended to solve the optimal control and optimal parameter selection problems., A fed-batch fermentation example is used to investigate the effectiveness of the proposed method. For comparison, several alternate methods are also employed to solve this process. (C) 1999 Elsevier Science Ltd. All rights reserved.
This paper presents a comparative study for three evolutionary algorithms (EAs) to the Optimal Reactive Power Planning (ORPP) problem: evolutionary programming, evolutionary strategy, and genetic algorithm. The ORPP p...
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This paper presents a comparative study for three evolutionary algorithms (EAs) to the Optimal Reactive Power Planning (ORPP) problem: evolutionary programming, evolutionary strategy, and genetic algorithm. The ORPP problem is decomposed into P- and Q-optimization modules, and each module is optimized by the EAs in an iterative manner to obtain the global solution. The EA methods for the ORPP problem are evaluated against the IEEE 30-bus system as a common testbed, and the results are compared against each other and with those of linear programming.
This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by plan...
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This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by planners on different networks and operation conditions, the proposed method uses multi-operators and a mechanism for dynamic adaptation of the selection probabilities of these operators. Two sets of search operators are proposed: evolutionary and specialized. The mathematical formulation considers a DC network model including transmission losses and the "N-1" deterministic criterion. The proposed method is applied to a well known academic test system and a configuration of the Brazilian network. (C) 2015 Elsevier B.V. All rights reserved.
Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view o...
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Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour. (C) 2015 Elsevier B.V. All rights reserved.
Today's high performance computer systems must have fast, reliable access to memory and I/O devices. Unfortunately, inter-symbol interference, transmission line effects and other noise sources can distort data tra...
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Today's high performance computer systems must have fast, reliable access to memory and I/O devices. Unfortunately, inter-symbol interference, transmission line effects and other noise sources can distort data transfers. Engineers must therefore determine if bus designs have signal integrity-i.e., can transfer data with minimal amplitude or timing distortion. One method of determining signal integrity on buses is to conduct a set of data transfers and measure various signal parameters at the receiver end. But the tests must be conducted with stressful test patterns that maximize noise to help identify any potential problems. In this paper we describe how an evolutionary algorithm was used to evolve test patterns for use in intrinsic testing.
This paper addresses an application of evolutionary algorithms to optimal siting and sizing of UPFC which are formulated as single and multiobjective optimization problems. The decision variables such as optimal locat...
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This paper addresses an application of evolutionary algorithms to optimal siting and sizing of UPFC which are formulated as single and multiobjective optimization problems. The decision variables such as optimal location, both line and distance of UPFC from the sending end, control parameters of UPFC and system reactive power reserves are considered in the optimization process. Minimization of total costs including installation cost of UPFC and enhancement of the loadability limit are considered as objectives. To reduce the complexity in modeling and the number of variables and constraints, transformer model of UPFC is used for simulation purposes. CMAES and NSGA-II algorithms are used for optimal siting and sizing of UPFC on IEEE 14 and 30 bus test systems. NSGA-II algorithm is tested on IEEE 118 bus system to prove the versatility of the algorithm when applied to large systems. To validate the results of transformer model of UPFC for optimal siting and sizing, results using other models are considered. In single objective optimization problem, CMAES algorithm with transformer model yields better results when compared to other UPFC models. The statistical results conducted on 20 independent trials of CMAES algorithm authenticate the results obtained. For validating the results of NSGA-II with transformer model for optimal siting and sizing of UPFC, the reference Pareto front generated using multiple run CMAES algorithm by minimizing weighted objective is considered. In multiobjective optimization problem, the similarity between the generated Pareto front and the reference Pareto front validates the results obtained. (C) 2015 Elsevier Ltd. All rights reserved.
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