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Adaptive multi-tiered resource allocation policy for microgrids

  • Received: 31 December 2015 Accepted: 25 February 2016 Published: 04 March 2016
  • We consider a cluster of buildings within proximity that share a large-capacity battery for peak-shaving purposes, and draw power from the grid at a premium once they reach a certain threshold. Our goal is to identify a resource allocation policy that minimizes the amount of energy the cluster draws at a premium, while also ensuring fair access to all of its members. We introduce an adaptive policy that allows for maximum energy savings when the network load is low, and ensures fairness when the aggregate power level is high. We compare this adaptive policy with two standard resource allocation strategies with complementary advantages, and demonstrate through an extensive performance evaluation, that it combines the benefits of both. It is therefore suitable for a microgrid operator where equal weight is given to both cluster-wide cost minimization and fairness among all customers.

    Citation: Konstantinos Christidis, Michael Devetsikiotis. Adaptive multi-tiered resource allocation policy for microgrids[J]. AIMS Energy, 2016, 4(2): 300-312. doi: 10.3934/energy.2016.2.300

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  • We consider a cluster of buildings within proximity that share a large-capacity battery for peak-shaving purposes, and draw power from the grid at a premium once they reach a certain threshold. Our goal is to identify a resource allocation policy that minimizes the amount of energy the cluster draws at a premium, while also ensuring fair access to all of its members. We introduce an adaptive policy that allows for maximum energy savings when the network load is low, and ensures fairness when the aggregate power level is high. We compare this adaptive policy with two standard resource allocation strategies with complementary advantages, and demonstrate through an extensive performance evaluation, that it combines the benefits of both. It is therefore suitable for a microgrid operator where equal weight is given to both cluster-wide cost minimization and fairness among all customers.


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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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