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A Decentralized PSO with Decoder for Scheduling Distributed Electricity Generation

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

A steadily increasing pervasion of the distribution grid with rather small renewable energy resources imposes fluctuating and hardly predictable feed-in and thus calls for new predictive load planning strategies. On the other hand, combined with controllable, shiftable loads and electrical storages, these energy units set up a flexibility potential for fine-grained control. To tap the full potential, distributed control strategies are discussed for scheduling due to the expected large number of controlled entities. Decoder strategies for unit independent algorithm implementation and feasibility assurance had recently been applied to some first optimization approaches for scheduling in smart grid. We extended a distributed particle swarm to harnesses such decoder approach for model independent constraint-handling and achieved a higher accuracy compared with other approaches. A multi swarm is integrated after the island model into a decentralized agent-based solution and compared with an established decentralized approach for predictive scheduling within virtual power plants. We demonstrate the superiority of the particle swarm in terms of achieved solution accuracy and the competitiveness in terms of sent messages.

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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. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Bremer, J., Lehnhoff, S. (2016). A Decentralized PSO with Decoder for Scheduling Distributed Electricity Generation. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_28

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