loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jörg Bremer 1 and Sebastian Lehnhoff 2

Affiliations: 1 University of Oldenburg, Germany ; 2 OFFIS – Institute for Information Technology, Germany

ISBN: 978-989-758-172-4

Keyword(s): Uncertainty, SVDD, Smart Grid, Distributed Generation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Enterprise Information Systems ; Evolutionary Computing ; Formal Methods ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Imponderabilities like weather, user interaction, projected heat demand, and many more have a major impact on feasibility – in the sense of being technically operable by a specific energy unit. Deviations from the predicted initial operational state of an energy unit may easily foil a planned schedule commitment and provoke the need for ancillary services. In order to minimize control power and cost arising from deviations from agreed energy product delivery, it is advantageous to a priori know about individual uncertainty. We extend an existing surrogate model that has been successfully used in energy management for checking feasibility during constraint-based optimization. The surrogate is extended to incorporate confidence scores based on expected feasibility under changed operational co nditions. We demonstrate the superiority of the new surrogate model by results from several simulation studies. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.227.2.109

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bremer, J. and Lehnhoff, S. (2016). Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling.In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 42-50. DOI: 10.5220/0005691600420050

@conference{icaart16,
author={Jörg Bremer. and Sebastian Lehnhoff.},
title={Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={42-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005691600420050},
isbn={978-989-758-172-4},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling
SN - 978-989-758-172-4
AU - Bremer, J.
AU - Lehnhoff, S.
PY - 2016
SP - 42
EP - 50
DO - 10.5220/0005691600420050

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.