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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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    [1] Time-of-Use. Pacific Gas and Electric Company, 2015. Available from: http://www.pge.com/touintro/.
    [2] Eyer J, Corey G (2010) Energy storage for the electricity grid: Benefits and market potential assessment guide. Sandia National Laboratories: 69-73.
    [3] Demand Charges 101. Stem, 2015. Available from: http://www.stem.com/resources/learning.
    [4] Public Utilities Code Section 748 Report to the Governon and Legislature on Actions to Limit Utility Cost and Rate Increases. California Public Utilities Commission, 2012. Available from: http://www.cpuc.ca.gov/NR/rdonlyres/339C0DD6-0298-4BC7-AAD9-A27779AA43D4/0/2012SB695ReporttoGovernorandLegislatureFinalv2.pdf.
    [5] Caron S, Kesidis G (2010) Incentive-based energy consumption scheduling algorithms for the smart grid. First IEEE International Conference on Smart Grid Communications (SmartGridComm) IEEE, 391-396.
    [6] Koutsopoulos I, Tassiulas L (2012) Optimal control policies for power demand scheduling in the smart grid. IEEE Journal on Selected Areas in Communications 30: 1049-1060. doi: 10.1109/JSAC.2012.120704
    [7] Samadi P, Mohsenian-Rad AH, Schober R, et al. (2010) Optimal real-time pricing algorithm based on utility maximization for smart grid. First IEEE International Conference on Smart Grid Communications (SmartGridComm)IEEE: 415-420.
    [8] Kim T, Poor H (2011) Scheduling power consumption with price uncertainty. IEEE Transactions on Smart Grid 2: 529-527.
    [9] Alizadeh M, Scaglione A, Thomas R (2012) From packet to power switching: Digital direct load scheduling. IEEE Journal on Selected Areas in Communications 30: 1027-1036. doi: 10.1109/JSAC.2012.120702
    [10] Fahrioglu M, Alvarado F (2000) Designing incentive compatible contracts for effective demand management. IEEE Transactions on Power Systems 15: 1255-1260. doi: 10.1109/59.898098
    [11] Goudarzi H, Pedram M (2011) Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems. IEEE International Conference on Cloud Computing (CLOUD) IEEE: 324-331.
    [12] Ardakanian O, Keshan S, Rosenberg C (2012) On the use of teletraffic theory in power distribution systems. Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet ACM, 21.
    [13] Richardson I, Thomson M, Infield D, et al. (2010) Domestic electricity use: A high-resolution energy demand model. Energy and Buildings 42: 1878-1887. doi: 10.1016/j.enbuild.2010.05.023
    [14] Ghiassi-Farrokhfal Y, Keshav S, Rosenberg C (2015) Toward a realistic performance analysis of storage systems in smart grids. IEEE Transactions on Smart Grid 6: 402-410. doi: 10.1109/TSG.2014.2330832
    [15] Bertsekas D, Gallager R, Humblet P (1992) Data networks, Volume 2, New Jersey: Prentice-Hall International.
    [16] Ferreira H, Garde R, Fulli G, et al. (2013) Characterisation of electrical energy storage technologies. Energy 53: 288-298. doi: 10.1016/j.energy.2013.02.037
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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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