Adaptive multi-tiered resource allocation policy for microgrids

Konstantinos Christidis; Michael Devetsikiotis; Konstantinos Christidis; Michael Devetsikiotis

doi:10.3934/energy.2016.2.300

AIMS Energy

2016, Volume 4, Issue 2: 300-312. doi: 10.3934/energy.2016.2.300

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Adaptive multi-tiered resource allocation policy for microgrids

Department of ECE, North Carolina State University, 890 Oval Dr, Raleigh, NC 27606, USA

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.
- microgrids,
- battery storage,
- peak shaving,
- resource pooling,
- resource allocation
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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Abstract

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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