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European Conference on the Applications of Evolutionary Computation

EvoApplications 2017: Applications of Evolutionary Computation pp 342–358Cite as

Hybrid Multi-ensemble Scheduling

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

Abstract

A steadily increasing pervasion of the electrical distribution grid with rather small renewable energy resources imposes fluctuating and hardly predictable feed-in, a partly reverse load flow and demands new predictive load planning strategies. For predictive scheduling with high penetration of renewable energy resources, agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good performance. On the other hand, such decoder-based methods are currently designed for single entities and not able to cope with ensembles of energy resources. Combining training sets sampled from individually modeled energy units, results in folded distributions with unfavorable properties for training a decoder. Nevertheless, this happens to be a quite frequent use case, e. g. when a hotel, a small business, a school or similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take part in decentralized predictive scheduling. In this paper, we propose an extension to an established agent approach for scheduling individual single energy units by extending the agents’ decision routine with a covariance matrix adaption evolution strategy that is hybridized with decoders. In this way, locally managed ensembles of energy units can be included. We show the applicability of our approach by conducting several simulation studies.

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Authors and Affiliations

  1. University of Oldenburg, 26129, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

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  1. Jörg Bremer
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  2. Sebastian Lehnhoff
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Correspondence to Jörg Bremer .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Bremer, J., Lehnhoff, S. (2017). Hybrid Multi-ensemble Scheduling. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_23

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