Programming of thermoelectric generation systems based on a heuristic composition of ant colonies

Silva, Ivo Chaves da; Nascimento, Flavia; Oliveira, Edimar J. de; Marcato, André L. M.; Oliveira, Leonardo W. de; Filho, João Alberto Passos

doi:10.1016/j.ijepes.2012.07.036

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…ACOs have been widely used in many fields of application. To name a few, ACOs has been successfully applied to heat transfer problems [39,40], thermoelectricity applications [41] and water distribution systems [6]. ACO has also been combined with other heuristic algorithms such as PSO to solve inverse heat transfer problems [42,43] for higher effectiveness and overall robustness.…”

Section: Ant Colony Optimization For Continuous Variables (Acor)mentioning

confidence: 99%

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

Allyson-Cyr

Gosselin

2019

Numerical Heat Transfer, Part A: Applications

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Section: Ant Colony Optimization For Continuous Variables (Acor)mentioning

confidence: 99%

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

Allyson-Cyr

Gosselin

2019

Numerical Heat Transfer, Part A: Applications

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“…Various algorithms have been applied to solve classic ED problems. They include mixed integer programming [17], Lagrange relaxation [18], neural networks [19], and particle swarm optimization [20]. When stochastic variables involved, in Zhao et al [15], the authors used probability distributions to model wind power and the behavior of EVs, and then theoretically derived the mathematical expectations of the generation costs.…”

Section: Introductionmentioning

confidence: 99%

Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources

Zhu

Jiang

et al. 2014

Energies

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Abstract:The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA)-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.

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A Multi-algorithm Approach to the Optimization of Thermal Power Plants Operation

Justino,

Freitas,

Batista

et al. 2023

Lecture Notes in Computer Science

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Programming of thermoelectric generation systems based on a heuristic composition of ant colonies

Cited by 7 publications

References 34 publications

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources

A Multi-algorithm Approach to the Optimization of Thermal Power Plants Operation

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