Multi-Objective Cooperative Scheduling for Smart Grids: (Short Paper)
CAMBLONG, Haritza
Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] [UPV / EHU]
ESTIA INSTITUTE OF TECHNOLOGY
Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] [UPV / EHU]
ESTIA INSTITUTE OF TECHNOLOGY
CAMBLONG, Haritza
Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] [UPV / EHU]
ESTIA INSTITUTE OF TECHNOLOGY
< Réduire
Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] [UPV / EHU]
ESTIA INSTITUTE OF TECHNOLOGY
Langue
EN
Communication dans un congrès
Ce document a été publié dans
On the Move to Meaningful Internet Systems. OTM 2017 Conferences - Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part I, 2017-10-23, Rhodes. 2017, vol. 10573 LNCS, p. 543-551
Springer Verlag
Résumé en anglais
In this work, we propose a multi-objective cooperative scheduling for Smart Grids (SG) consisting of two main modules: (1) the Preference-based Compromise Builder and (2) the Multi-objective Scheduler. The Preference-based ...Lire la suite >
In this work, we propose a multi-objective cooperative scheduling for Smart Grids (SG) consisting of two main modules: (1) the Preference-based Compromise Builder and (2) the Multi-objective Scheduler. The Preference-based Compromise Builder generates the best balance or what we call ‘the compromise' between the preferences or associations of sellers and buyers that must exchange power simultaneously. Once done, the Multi-objective Scheduler proposes a power schedule for the associations, in order to achieve optimal benefits from different perspectives (e.g., economical by reducing the electricity costs, ecological by minimizing the toxic gas emissions, and operational by reducing the peak load of the SG and its components, and by increasing their comfort). Conducted experiments showed that the proposed algorithms provide convincing results. \textcopyright 2017, Springer International Publishing AG.< Réduire
Unités de recherche