B Rajanarayan Prusty – författare

This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration. It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling.Key Features: Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers. Discusses dependence modeling of multi-site renewable generations in detail. Covers probabilistic analysis, useful for data analysts. Discusses various aspects of renewable energy integration i.e. technical, economic, etc. Covers correlation factors, and methodologies are validated with case studies with various standard test systems. The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration.

It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling.

Key Features:

Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers.

Discusses dependence modeling of multi-site renewable generations in detail.

Covers probabilistic analysis, useful for data analysts.

Discusses various aspects of renewable energy integration i.e. technical, economic, etc.

Covers correlation factors, and methodologies are validated with case studies with various standard test systems.

The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration.

It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling.

Key Features:

Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers.

Discusses dependence modeling of multi-site renewable generations in detail.

Covers probabilistic analysis, useful for data analysts.

Discusses various aspects of renewable energy integration i.e. technical, economic, etc.

Covers correlation factors, and methodologies are validated with case studies with various standard test systems.

The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

This comprehensive reference text discusses uncertainty modeling of renewable energy resources and its steady state analysis. The text discusses challenges related to renewable energy integration to the grid, techniques to mitigate these challenges, problems associated with integration at transmission and distribution voltage level, and protection of power system with large renewable power integration. It covers important concepts including voltage issues in power networks, use of FACTS devices for reactive power management, stochastic optimization, robust optimization, and spatiotemporal dependence modeling.Key Features: Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers. Discusses dependence modeling of multi-site renewable generations in detail. Covers probabilistic analysis, useful for data analysts. Discusses various aspects of renewable energy integration i.e. technical, economic, etc. Covers correlation factors, and methodologies are validated with case studies with various standard test systems. The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:an exclusive section on essential preprocessing approaches for the data-driven modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:an exclusive section on essential preprocessing approaches for the data-driven modela detailed overview of data-driven model applications to power system planning and operational activitiesspecific focus on developing forecasting models for renewable generations such as solar PV and wind power, andshowcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworksThis book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include:

an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks

This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.