Adaptive nesting of evolutionary algorithms for the optimization of Microgrid’s sizing and operation scheduling
- 28 September 2016
- journal article
- focus
- Published by Springer Nature in Soft Computing
- Vol. 21 (17) , 4845-4857
Abstract
No abstract availableKeywords
Funding Information
- Spanish Ministerial Commission of Science and Technology, MICYT (TIN2014-54583-C2-2-R)
- Comunidad Autonoma de Madrid (S2013ICE-2933 02)
This publication has 28 references indexed in Scilit:
- Computational intelligence in wave energy: Comprehensive review and case studyRenewable and Sustainable Energy Reviews, 2016
- From evolutionary computation to the evolution of thingsNature, 2015
- Using multi-objective metaheuristics for the optimal selection of positioning systemsSoft Computing, 2015
- Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealingInternational Journal of Electrical Power & Energy Systems, 2014
- Application of Modified Bacterial Foraging Optimization algorithm for optimal placement and sizing of Distributed GenerationExpert Systems with Applications, 2014
- Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variable load conditionsInternational Journal of Electrical Power & Energy Systems, 2013
- Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithmEnergy, 2013
- Introduction to Evolutionary Computing2003
- A Comparison of Selection Schemes Used in Evolutionary AlgorithmsEvolutionary Computation, 1996
- An Overview of Evolutionary Algorithms for Parameter OptimizationEvolutionary Computation, 1993