Distributed Neuro-Dynamic Algorithm for Price-Based Game in Energy Consumption System Academic Article

abstract

• 2019, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, a plug-in hybrid electric vehicles energy consumption system is studied. In order to protect each players privacy, the information exchange is going on the neighboring players, and a connected undirected graph is used to pattern the information flow between the players. Hence, it is impossible for each player to access the aggregate electricity consumption directly, which determines the electricity price. Under the noncooperative game frame, a distributed neuro-dynamic algorithm is proposed to optimize the benefit of each individual player base on the pricing strategies. A dynamic average consensus is applied to estimate the aggregate consumption and a projection neural network is employed to seek the Nash equilibrium point. The convergence of the proposed distributed algorithm is analyzed through the Lyapunov stability analysis. Finally, the effectiveness of the distributed neuro-dynamic algorithm is manifested in the simulation.

authors

• Huang, Tingwen

published proceedings

• NEURAL PROCESSING LETTERS

author list (cited authors)

• Wen, S., He, X., & Huang, T.

citation count

• 4

complete list of authors

• Wen, Shifan||He, Xing||Huang, Tingwen

publication date

• February 2020

publisher

• Springer Nature  Publisher

published in

• Neural Processing Letters  Journal

keywords

• Distributed Neuro-dynamic Algorithm
• Dynamic Average Consensus
• Nash Equilibrium
• Noncooperative Game
• Projection Neural Network

Digital Object Identifier (DOI)

• 10.1007/s11063-019-10102-z

start page

• 559

end page

• 575

volume

• 51

issue

• 1

URL

• http://dx.doi.org/10.1007/s11063-019-10102-z

user-defined tag

• 7 Affordable and Clean Energy