Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling

Jörg Bremer, Sebastian Lehnhoff

2016

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 conditions. We demonstrate the superiority of the new surrogate model by results from several simulation studies.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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