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Self-organizing Coalitions for Managing Complexity pp 207–263Cite as

Coalitions of Electric Vehicles in Smart Grids

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 29))

Abstract

In this chapter, we introduce the use of self-organised coalitions in smart grid scenarios for finding a coalition structure that maximises the systems’ utility. The complexity of such a task is exponential with the number of agents, and optimal coalition formation has been considered impractical. Several heuristic alternatives have been proposed in the research literature to handle such a problem. However, most existing methods approach coalition formation neglecting important aspects like maximising the total revenue or ensuring stability. Nonetheless, these points are fundamental in the context of smart grids, especially when we refer to virtual power plants (VPPs) of plug-in electric vehicles (PEVs), which have very limited energy capacity and small profits. In this chapter, we present two classes of constraints: (i) geographic-based, where the geographic position of PEVs is considered to avoid overloading the energy distribution network; and (ii) user-based, where the preferences of the PEV-users (owners) are taken into account to promote lasting coalitions. We also propose three methods for addressing coalition formation within such constrained scenarios: (i) DCCF, where agents invite neighbours to join their coalitions; (ii) SACF, where agents ask to join their neighbours’ coalitions; and (iii) SACF\(^+\), which is a natural evolution of SACF, where agents can change their coalitions, thus making the process much more dynamic. In all cases, agents negotiate the formation of coalitions among themselves, each on behalf a single PEV. The presented approaches were evaluated in closed and open world scenarios. Regarding the results, all three methods run in a few milliseconds regardless of the number of agents, achieving near-optimal solutions. In all tested cases, results were above 90% of optimum, on average. In comparison, despite delivering optimal solutions, traditional approaches took several hours and run for up to 20 agents, which represents a small and unrealistic scenario for smart grids. Thus, the proposed approaches show that providing approximate solutions for the coalition formation problem is attainable in smart grids scenarios.

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Notes

  1. 1.

    We use the terms PEV and agent interchangeably.

  2. 2.

    We assume a power rating \(w_i\) of 3.3 kW for every agent \(i \in \mathscr {A}\). This value is similar to that of some commercial PEVs. However, any other such value could be used here.

  3. 3.

    We consider that each time step corresponds to 1 min.

  4. 4.

    The distance metric does not play an important role in this work. Anyway, geographical distance is a reasonable approximation (in the absence of a better one) for this problem. In real situations, it could be trivially replaced by another one.

  5. 5.

    In real scenarios, the ID could be easily replaced by any other comparable code, such as the vehicle’s license plate.

  6. 6.

    It is important to note that, in the beginning, all agents act as requesters.

  7. 7.

    The leader of a coalition does not have any specific characteristics. Instead, it is just a role assumed by an agent to represent the coalition in negotiation processes, thus avoiding redundant negotiations. When the coalition is broken (i.e., one of its members has left), the leader is also responsible for notifying its members about the event.

  8. 8.

    The rationale behind the second case (request received by a singleton agent) is that the requester can also have received requests after having made his request. In this sense, the confirmation is needed to ensure that the requester is still interested in joining the coalition.

  9. 9.

    If there are no coalitions, then singleton neighbours are considered in this process.

  10. 10.

    A request is mutual when two agents propose the same coalition to each other. In this case, only the agent with the lowest ID will accept the request of the other, thus becoming the leader of that coalition.

  11. 11.

    It is noteworthy that only the agent with the greatest ID operates this phase.

  12. 12.

    Again, we consider each time step corresponds to 1 min.

  13. 13.

    As for SACF, in the beginning, all agents act as requesters.

  14. 14.

    Following the same idea of SACF, the leadership of a coalition is just a role assumed by an agent to represent the coalition in negotiation processes, thus avoiding redundant negotiations. The leader is also responsible for notifying the coalition’s members when it is broken (i.e., one of its members has left). Here, the leader of a coalition is the agent with lowest ID (which, in real situations, could be replaced, e.g., by the vehicle’s licence plate).

  15. 15.

    The second phase is important because the requester may also receive requests. Thus, a confirmation is needed for ensuring the requester is still interested in joining the coalition.

  16. 16.

    It is noteworthy that only the agent with the greatest ID operates this phase.

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Acknowledgements

Ramos and Bazzan are partially supported by CNPq and CAPES grants. This work was also partially supported by the European Regional Development Fund (ERDF) together with the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC). Icons from Openclipart (http://openclipart.org/) were used in Figs. 10.1, 10.3 and 10.15.

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Authors and Affiliations

  1. Instituto de Informática, Universidade Federal do Rio Grande do Sul, Porto Alegre, 15064, Brazil

    Gabriel de O Ramos & Ana L. C. Bazzan

  2. Department of Telematics Engineering, School of Telecommunications Engineering, University of Vigo, 36310, Vigo, Spain

    Juan C. Burguillo

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  1. Gabriel de O Ramos
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  3. Ana L. C. Bazzan
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Correspondence to Juan C. Burguillo .

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Ramos, G.d.O., Burguillo, J.C., Bazzan, A.L.C. (2018). Coalitions of Electric Vehicles in Smart Grids. In: Self-organizing Coalitions for Managing Complexity. Emergence, Complexity and Computation, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-69898-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-69898-4_10

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