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The deployment of distributed renewable energy resources (DRERs) has accelerated globally due to environmental concerns and an increasing demand for electricity. DRERs are considered to be solutions to some of the current challenges related to power grids, such as reliability, resilience, efficiency, and flexibility. However, there are still several technical and non-technical challenges regarding the deployment of distributed renewable energy resources. Technical concerns associated with the integration and control of DRERs include, but are not limited, to optimal sizing and placement, optimal operation in grid-connected and islanded modes, as well as the impact of these resources on power quality, power system security, stability, and protection systems. On the other hand, non-technical challenges can be classified into three categories—regulatory issues, social issues, and economic issues. This Special Issue will address all aspects related to the integration and control of distributed renewable energy resources. It aims to understand the existing challenges and explore new solutions and practices for use in overcoming technical challenges.

Technology: general issues --- History of engineering & technology --- distribution system --- microgrids --- power quality --- power system management --- power system reliability --- smart grids --- distribution networks --- Monte Carlo simulations --- PV hosting capacity --- photovoltaics --- green communities --- energy independence --- HOMER --- wind turbines --- power losses --- power system optimization --- PV curves --- DG --- TSA/SCA --- solar-powered electric vehicle parking lots --- different PV technologies --- PLO's profit --- uncertainties --- smart grid paradigm --- distributed generation --- model-based predictive control --- robustness --- worst-case scenario --- min-max optimisation --- intraday forecasting --- Gaussian process regression --- machine learning --- off-grid system --- composite control strategy --- solar photovoltaic panel --- wind turbine --- diesel generator --- energy storage system (ESS) --- synchronous machine (SM) --- permanent magnet brushless DC machine (PMBLDCM) --- power quality improvement --- distribution system --- microgrids --- power quality --- power system management --- power system reliability --- smart grids --- distribution networks --- Monte Carlo simulations --- PV hosting capacity --- photovoltaics --- green communities --- energy independence --- HOMER --- wind turbines --- power losses --- power system optimization --- PV curves --- DG --- TSA/SCA --- solar-powered electric vehicle parking lots --- different PV technologies --- PLO's profit --- uncertainties --- smart grid paradigm --- distributed generation --- model-based predictive control --- robustness --- worst-case scenario --- min-max optimisation --- intraday forecasting --- Gaussian process regression --- machine learning --- off-grid system --- composite control strategy --- solar photovoltaic panel --- wind turbine --- diesel generator --- energy storage system (ESS) --- synchronous machine (SM) --- permanent magnet brushless DC machine (PMBLDCM) --- power quality improvement

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Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

Technology: general issues --- History of engineering & technology --- Automatic Voltage Regulation system --- Chaotic optimization --- Fractional Order Proportional-Integral-Derivative controller --- Yellow Saddle Goatfish Algorithm --- two-stage method --- mono and multi-objective optimization --- multi-objective optimization --- optimal design --- Gough–Stewart --- parallel manipulator --- performance metrics --- diversity control --- genetic algorithm --- bankruptcy problem --- classification --- T-junctions --- neural networks --- finite elements analysis --- surrogate --- beam improvements --- beam T-junctions models --- artificial neural networks (ANN) limited training data --- multi-objective decision-making --- Pareto front --- preference in multi-objective optimization --- aeroacoustics --- trailing-edge noise --- global optimization --- evolutionary algorithms --- nearly optimal solutions --- archiving strategy --- evolutionary algorithm --- non-linear parametric identification --- multi-objective evolutionary algorithms --- availability --- design --- preventive maintenance scheduling --- encoding --- accuracy levels --- plastics thermoforming --- sheet thickness distribution --- evolutionary optimization --- genetic programming --- control --- differential evolution --- reusable launch vehicle --- quality control --- roughness measurement --- machine vision --- machine learning --- parameter optimization --- distance-based --- mutation-selection --- real application --- experimental study --- global optimisation --- worst-case scenario --- robust --- min-max optimization --- optimal control --- multi-objective optimisation --- robust design --- trajectory optimisation --- uncertainty quantification --- unscented transformation --- spaceplanes --- space systems --- launchers --- Automatic Voltage Regulation system --- Chaotic optimization --- Fractional Order Proportional-Integral-Derivative controller --- Yellow Saddle Goatfish Algorithm --- two-stage method --- mono and multi-objective optimization --- multi-objective optimization --- optimal design --- Gough–Stewart --- parallel manipulator --- performance metrics --- diversity control --- genetic algorithm --- bankruptcy problem --- classification --- T-junctions --- neural networks --- finite elements analysis --- surrogate --- beam improvements --- beam T-junctions models --- artificial neural networks (ANN) limited training data --- multi-objective decision-making --- Pareto front --- preference in multi-objective optimization --- aeroacoustics --- trailing-edge noise --- global optimization --- evolutionary algorithms --- nearly optimal solutions --- archiving strategy --- evolutionary algorithm --- non-linear parametric identification --- multi-objective evolutionary algorithms --- availability --- design --- preventive maintenance scheduling --- encoding --- accuracy levels --- plastics thermoforming --- sheet thickness distribution --- evolutionary optimization --- genetic programming --- control --- differential evolution --- reusable launch vehicle --- quality control --- roughness measurement --- machine vision --- machine learning --- parameter optimization --- distance-based --- mutation-selection --- real application --- experimental study --- global optimisation --- worst-case scenario --- robust --- min-max optimization --- optimal control --- multi-objective optimisation --- robust design --- trajectory optimisation --- uncertainty quantification --- unscented transformation --- spaceplanes --- space systems --- launchers

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

Automatic Voltage Regulation system --- Chaotic optimization --- Fractional Order Proportional-Integral-Derivative controller --- Yellow Saddle Goatfish Algorithm --- two-stage method --- mono and multi-objective optimization --- multi-objective optimization --- optimal design --- Gough–Stewart --- parallel manipulator --- performance metrics --- diversity control --- genetic algorithm --- bankruptcy problem --- classification --- T-junctions --- neural networks --- finite elements analysis --- surrogate --- beam improvements --- beam T-junctions models --- artificial neural networks (ANN) limited training data --- multi-objective decision-making --- Pareto front --- preference in multi-objective optimization --- aeroacoustics --- trailing-edge noise --- global optimization --- evolutionary algorithms --- nearly optimal solutions --- archiving strategy --- evolutionary algorithm --- non-linear parametric identification --- multi-objective evolutionary algorithms --- availability --- design --- preventive maintenance scheduling --- encoding --- accuracy levels --- plastics thermoforming --- sheet thickness distribution --- evolutionary optimization --- genetic programming --- control --- differential evolution --- reusable launch vehicle --- quality control --- roughness measurement --- machine vision --- machine learning --- parameter optimization --- distance-based --- mutation-selection --- real application --- experimental study --- global optimisation --- worst-case scenario --- robust --- min-max optimization --- optimal control --- multi-objective optimisation --- robust design --- trajectory optimisation --- uncertainty quantification --- unscented transformation --- spaceplanes --- space systems --- launchers