TY - GEN

T1 - Smart Greedy Distributed Allocation in Microgrids

AU - Bistritz, Ilai

AU - Ward, Andrew

AU - Zhou, Zhengyuan

AU - Bambos, Nicholas

N1 - Publisher Copyright:
© 2019 IEEE.

PY - 2019/5

Y1 - 2019/5

N2 - We consider a microgrid that consists of N providers and B consumers. Each provider has a certain supply and each consumer has a certain demand. The efficiency of transmitting energy between providers and consumers is modeled using a bipartite graph G. Our goal is to maximize the amount of utilized energy using a distributed algorithm that each provider runs locally. We propose a non-cooperative energy allocation game, and adopt the best-response dynamics for this game as our distributed algorithm. We prove that the best-response dynamics converge in no more than N steps to one of at most N! pure Nash equilibria of our game. Despite the fact that some of these Nash equilibria are suboptimal, we are able to prove that our algorithm achieves near-optimal performance in "almost all" games. We do so by analyzing the best-response dynamics in a random game, where the network is generated using a random model for the graph G. We prove that the ratio between the utilized energy of our algorithm and that of the optimal solution converges to one in probability as B increases (and N is any function of B). Using numerical simulations, we demonstrate that our asymptotic analysis is valid even for B - 10 consumers.

AB - We consider a microgrid that consists of N providers and B consumers. Each provider has a certain supply and each consumer has a certain demand. The efficiency of transmitting energy between providers and consumers is modeled using a bipartite graph G. Our goal is to maximize the amount of utilized energy using a distributed algorithm that each provider runs locally. We propose a non-cooperative energy allocation game, and adopt the best-response dynamics for this game as our distributed algorithm. We prove that the best-response dynamics converge in no more than N steps to one of at most N! pure Nash equilibria of our game. Despite the fact that some of these Nash equilibria are suboptimal, we are able to prove that our algorithm achieves near-optimal performance in "almost all" games. We do so by analyzing the best-response dynamics in a random game, where the network is generated using a random model for the graph G. We prove that the ratio between the utilized energy of our algorithm and that of the optimal solution converges to one in probability as B increases (and N is any function of B). Using numerical simulations, we demonstrate that our asymptotic analysis is valid even for B - 10 consumers.

UR - http://www.scopus.com/inward/record.url?scp=85070218950&partnerID=8YFLogxK

U2 - 10.1109/ICC.2019.8761111

DO - 10.1109/ICC.2019.8761111

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AN - SCOPUS:85070218950

T3 - IEEE International Conference on Communications

BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Communications, ICC 2019

Y2 - 20 May 2019 through 24 May 2019

ER -