29,276 research outputs found
Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
Among the various market structures under peer-to-peer energy sharing, one
model based on cooperative game theory provides clear incentives for prosumers
to collaboratively schedule their energy resources. The computational
complexity of this model, however, increases exponentially with the number of
participants. To address this issue, this paper proposes the application of
K-means clustering to the energy profiles following the grand coalition
optimization. The cooperative model is run with the "clustered players" to
compute their payoff allocations, which are then further distributed among the
prosumers within each cluster. Case studies show that the proposed method can
significantly improve the scalability of the cooperative scheme while
maintaining a high level of financial incentives for the prosumers.Comment: 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech
Conference, 23-27 June 2019, Milano, Ital
Controlled Matching Game for Resource Allocation and User Association in WLANs
In multi-rate IEEE 802.11 WLANs, the traditional user association based on
the strongest received signal and the well known anomaly of the MAC protocol
can lead to overloaded Access Points (APs), and poor or heterogeneous
performance. Our goal is to propose an alternative game-theoretic approach for
association. We model the joint resource allocation and user association as a
matching game with complementarities and peer effects consisting of selfish
players solely interested in their individual throughputs. Using recent
game-theoretic results we first show that various resource sharing protocols
actually fall in the scope of the set of stability-inducing resource allocation
schemes. The game makes an extensive use of the Nash bargaining and some of its
related properties that allow to control the incentives of the players. We show
that the proposed mechanism can greatly improve the efficiency of 802.11 with
heterogeneous nodes and reduce the negative impact of peer effects such as its
MAC anomaly. The mechanism can be implemented as a virtual connectivity
management layer to achieve efficient APs-user associations without
modification of the MAC layer
Peer-to-peer and community-based markets: A comprehensive review
The advent of more proactive consumers, the so-called "prosumers", with
production and storage capabilities, is empowering the consumers and bringing
new opportunities and challenges to the operation of power systems in a market
environment. Recently, a novel proposal for the design and operation of
electricity markets has emerged: these so-called peer-to-peer (P2P) electricity
markets conceptually allow the prosumers to directly share their electrical
energy and investment. Such P2P markets rely on a consumer-centric and
bottom-up perspective by giving the opportunity to consumers to freely choose
the way they are to source their electric energy. A community can also be
formed by prosumers who want to collaborate, or in terms of operational energy
management. This paper contributes with an overview of these new P2P markets
that starts with the motivation, challenges, market designs moving to the
potential future developments in this field, providing recommendations while
considering a test-case
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
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