Engineering IT-Enabled Sustainable Electricity Services : The Tale of Two Low-Cost Green Azores Islands
Engineering IT-Enabled Electricity Services: The Tale of Two Low-Cost Green Azores Islands covers sustainable energy services to customers - a balanced choice and coordination of energy generated by traditional and alternative sources. The Green Islands project represents a decade of work by over a...
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| Autor principal: | |
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| Otros Autores: | , |
| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Boston, MA :
Springer US
2013.
Cham : Springer Nature |
| Colección: | Power Electronics and Power Systems
30 |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Engineering IT-enabled sustainable electricity services, the tale of two low-cost green Azores Islands, Marija Ilic, Le Xie, Qixing Liu, editors, New York (N.Y.), Springer, 2013, Power electronics and power systems, 978-0-387-09735-0 |
| Sumario: | Engineering IT-Enabled Electricity Services: The Tale of Two Low-Cost Green Azores Islands covers sustainable energy services to customers - a balanced choice and coordination of energy generated by traditional and alternative sources. 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. This contributed volume presents methods for predicting variable resources, such as wind power generation, and analyzes the achievable accuracy of these predictions. Throughout this book, contributors show that the cost of serving customers in systems with highly uncertain generation will depend to a very large extent on how well the predictions are done. Therefore, the supporting IT technologies based on predictive models become critical to avoid the need for fast-responding storage. 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. Data provided with the book represents a repository of real-world electric energy systems and its IT-enabled smarts |
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| Notas: | L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Formato: | Nécessite un lecteur de fichier PDF |
| ISBN: | 9780387097367 |
| ISSN: | 2196-3193 |
| Acceso: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |