Elektromobile Flotten im lokalen Energiesystem mit Photovoltaikeinspeisung unter Berücksichtigung von Unsicherheiten
| dc.contributor.author | Seddig, Katrin | |
| dc.date.accessioned | 2021-02-17T08:47:51Z | |
| dc.date.available | 2021-02-17T08:47:51Z | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-02-11T17:58:46Z | |
| dc.identifier | ONIX_20210211_9783731510314_77 | |
| dc.identifier | https://library.oapen.org/handle/20.500.12657/46708 | |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/63676 | |
| dc.description.abstract | In this book, a model is developed which can be used to identify the load shifting potential of electric vehicle fleets considering the integration of photovoltaic generation and uncertainty. Different approaches using simulation, deterministic and stochastic optimization are developed to schedule the charging of three different electric vehicle fleets at a common charging infrastructure under uncertainty. | |
| dc.language | German | |
| dc.relation.ispartofseries | Produktion und Energie | |
| dc.rights | open access | |
| dc.subject.other | Elektromobilität | |
| dc.subject.other | E-Flotten | |
| dc.subject.other | Photovoltaik | |
| dc.subject.other | Unsicherheit | |
| dc.subject.other | Optimierungsmodell | |
| dc.subject.other | electromobility | |
| dc.subject.other | e-fleets | |
| dc.subject.other | photovoltaics | |
| dc.subject.other | uncertainty | |
| dc.subject.other | optimization model | |
| dc.subject.other | thema EDItEUR::K Economics, Finance, Business and Management::KC Economics | |
| dc.title | Elektromobile Flotten im lokalen Energiesystem mit Photovoltaikeinspeisung unter Berücksichtigung von Unsicherheiten | |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000118692 | |
| oapen.relation.isPublishedBy | 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 | |
| oapen.pages | 218 | |
| oapen.place.publication | Karlsruhe | |
| dc.seriesnumber | 34 | |
| dc.abstractotherlanguage | In this book, a model is developed which can be used to identify the load shifting potential of electric vehicle fleets considering the integration of photovoltaic generation and uncertainty. Different approaches using simulation, deterministic and stochastic optimization are developed to schedule the charging of three different electric vehicle fleets at a common charging infrastructure under uncertainty. |
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