Le Xie – författare

Sustainable energy services to customers - a balanced choice and coordination of energy generated by traditional and alternative sources - are the subject of this new innovative book. The myriad factors involved in modeling an effective sustainable power system are overwhelming. The “Green Islands” project represents a decade of work by over a dozen researchers who have developed a model designed to utilize the potential of distributed clean resources. The key is the proper use of Information Technology (IT). Sited on two islands in the Azores, the project developed the model of careful forecasting of demand and supply, down to the minute, coordinating the output of conventional power plants, wind energy, fly wheels, hydroelectricity, demand reduction, and even plug-in electric vehicles to take full advantage of the clean resources available. The energy contingencies of the remote islands are not unique. The issues of integrating promising clean technologies, such as wind, into a complex power grid are challenging in geographically far-flung, island-scale, power systems. Model-based sensing, communications, and decision-making algorithms to coordinate adaptive load management (ALM) could enable customers to utilize just-in-time (JIT), just-in-place (JIP), and just-in-context (JIC) energy resources. The distribution of flexible and efficient energy to customers is the goal. The model the authors have developed could change the way power portfolios are built. A new perspective for optimization of green energy is presented in this book. Additional data provided online via Springer represents a repository of real-world electric energy systems and its IT-enabled smarts.

Sustainable energy services to customers - a balanced choice and coordination of energy generated by traditional and alternative sources - are the subject of this new innovative book. The myriad factors involved in modeling an effective sustainable power system are overwhelming. The “Green Islands” project represents a decade of work by over a dozen researchers who have developed a model designed to utilize the potential of distributed clean resources. The key is the proper use of Information Technology (IT). Sited on two islands in the Azores, the project developed the model of careful forecasting of demand and supply, down to the minute, coordinating the output of conventional power plants, wind energy, fly wheels, hydroelectricity, demand reduction, and even plug-in electric vehicles to take full advantage of the clean resources available. The energy contingencies of the remote islands are not unique. The issues of integrating promising clean technologies, such as wind, into a complex power grid are challenging in geographically far-flung, island-scale, power systems. Model-based sensing, communications, and decision-making algorithms to coordinate adaptive load management (ALM) could enable customers to utilize just-in-time (JIT), just-in-place (JIP), and just-in-context (JIC) energy resources. The distribution of flexible and efficient energy to customers is the goal. The model the authors have developed could change the way power portfolios are built. A new perspective for optimization of green energy is presented in this book. Additional data provided online via Springer represents a repository of real-world electric energy systems and its IT-enabled smarts.

This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid.