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Assisted energy management in smart microgrids

  • Andrea Monacchi
  • Wilfried Elmenreich
Original Research

Abstract

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service-level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service availability is an opposite objective to the one of system reactivity. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.

Keywords

Energy management Energy trading Artificial neural network Smart metering Behavior modeling 

Notes

Acknowledgments

The work of A. Monacchi is supported with a research scholarship by the Alpen-Adria-Universität Klagenfurt. The work of W. Elmenreich is supported by Lakeside Labs, Klagenfurt, Austria and funded by the European Regional Development Fund (ERDF) and the Carinthian Economic Promotion Fund (KWF).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Networked and Embedded SystemsAlpen-Adria-Universität KlagenfurtKlagenfurtAustria

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