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Current industrial practice knows many optimization tasks that can be cast as mixed-integer optimal control problems. Due to the combinatorial character of these problems, the computation of optimal solutions under real-time constraints is still a demanding challenge. Starting with Bock's direct multiple shooting method for optimal control, Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding. In a sequential quadratic programming framework, extensive exploitation of arising structures in an active set method ultimately brings the developed algorithm towards real-time feasibility.

Computer science. --- Nonlinear control theory. --- Non-linear optimization. --- Numerical method. --- Optimal control. --- Engineering & Applied Sciences --- Computer Science --- Computer simulation. --- Mathematics. --- Computer Science. --- Simulation and Modeling. --- Mathematics, general. --- Control theory --- Nonlinear theories --- Math --- Science --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing

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This book includes original research papers related to renewable energy and power systems in which theoretical or practical issues of symmetry are considered. The book includes contributions on voltage stability analysis in DC networks, optimal dispatch of islanded microgrid systems, reactive power compensation, direct power compensation, optimal location and sizing of photovoltaic sources in DC networks, layout of parabolic trough solar collectors, topologic analysis of high-voltage transmission grids, geometric algebra and power systems, filter design for harmonic current compensation. The contributions included in this book describe the state of the art in this field and shed light on the possibilities that the study of symmetry has in power grids and renewable energy systems.

History of engineering & technology --- backstepping method --- hybrid power active filter --- harmonic current compensation --- geometric algebra --- nonsinusoidal power --- passive compensation --- clifford algebra --- circuit systems --- power grids --- supergrids --- high-voltage power transmission --- complex networks --- community detection --- modularity --- evolutionary algorithms --- generational genetic algorithm --- modularity and improved genetic algorithm --- Louvain modularity algorithm --- CSP --- PTC rows --- solar --- shadowing --- energy --- renewable energy --- artificial neural networks --- diesel generation --- direct current networks --- greenhouse emissions --- numerical optimization --- mixed-integer nonlinear programming photovoltaic plants --- distribution networks --- direct power control --- global tracking controller --- passivity-based control --- supercapacitor energy storage system --- reactive power --- thyristor-controlled reactor --- air-gaped reactor --- low-voltage utility grid --- asymmetric compensation of reactive power --- smooth compensation of reactive power --- dynamic optimal dispatch --- wind turbine --- photovoltaic --- Grey Wolf Optimizer (GWO) --- energy management --- convex reformulation --- non-linear optimization --- numerical example --- second-order cone programming --- voltage stability margin

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Information technology industries --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower’s interpolation formula --- Gower’s metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- n/a --- Gower's interpolation formula --- Gower's metric

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower’s interpolation formula --- Gower’s metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- n/a --- Gower's interpolation formula --- Gower's metric

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The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG.

Research & information: general --- Technology: general issues --- solar energy --- wind energy --- energy storage --- renewable energy integration --- Europe --- advanced metering infrastructure --- data acquisition --- IEC standards --- low-cost --- open source --- power measurement --- smart meter --- uncertainty evaluation --- frequency stabilization --- coordinated control --- wind turbine generator --- high-fidelity battery model --- releasable and absorbable energy --- photovoltaic emulator --- photovoltaic panel --- single diode model --- MPPT --- FSWT-SCIG --- battery storage system --- power system stability --- synchronous generator --- hybrid system --- voltage source converter --- passivity-based control --- proportional-integral control --- voltage regulation --- bi-directional converter --- LC impedance source converter --- DC–DC power converter --- bi-directional power flow --- alternating current networks --- direct current networks --- optimal power flow --- non-linear optimization --- control of power electronic converters --- distributed generation --- mixed-integer nonlinear programming --- second-cone programming --- discrete-sine cosine algorithm --- metaheuristic optimization --- DG placement --- evolutionary algorithms --- energy management --- fuzzy controller --- power systems analysis --- interconnected power systems --- latencies --- time-delay effects --- wide area monitoring systems --- renewable energy conversion --- power conditioning devices --- renewable energy policies --- power quality --- computations methods --- control strategies --- electric vehicle charging --- energy management systems --- ancillary services --- monitoring --- prognostic and diagnostic