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This decentralized method automatically optimizes bandwidth use.
The architecture for the multiagent system employed in this paper is considered as a sequentially decentralized method.
Alternatively we propose a novel decentralized method such that each individual submits a charging profile, with respect to a given fixed price curve, which minimizes its own cost dealing with the tradeoff between the electricity cost and battery degradation cost over the charging interval; the price curve is updated based upon the aggregated PEV charging profiles.
To overcome model complexity and the low computation efficiency of the centralized method, decentralized method treats each unit as an autonomous agent with their own operation goals.
The proposed centralized or decentralized method is based on the Bellman-Lyapunov equation, guaranteed cost, and parameter-dependent quadratic stability.
Numerical simulation are presented and compared with an improved decentralized method based on linear quadratic theory to show the efficacy of the developed approach.
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This result strongly encourages further research in applications of such decentralized methods to spectrum sharing in cognitive radio systems.
Whereas in decentralized methods each node usually processes some local information gathered via a limited number of message exchanges [6, 7].
This represents a contribution that meets our long-term agenda, which consists in proposing a methodology to integrate behavioral models of human travelers reacting to traffic patterns and control measures of these traffic patterns, focusing on distributed and decentralized methods.
Decentralized methods are considered in [107], which proposes a self-organizing approach to provide robust and scalable solutions for service deployment, resource provisioning, and load balancing in a cloud infrastructure.
Examples of leaderless MAS are presented in [13, 14] where voltages are regulated by distributed generators acting as cooperative agents, in [15, 16] where decentralized methods of optimal reactive power control are presented, in [17] where a distributed fair load shedding algorithm is presented, and in [18] where consumer agents cooperate to perform optimal load scheduling.
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