Publication

Dynamic Demand and Mean-Field Games

Bauso, D., Dec-2017, In : IEEE Transactions on Automatic Control. 62, 12, p. 6310-6323 14 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Bauso, D. (2017). Dynamic Demand and Mean-Field Games. IEEE Transactions on Automatic Control, 62(12), 6310-6323. https://doi.org/10.1109/TAC.2017.2705911

Author

Bauso, Dario. / Dynamic Demand and Mean-Field Games. In: IEEE Transactions on Automatic Control. 2017 ; Vol. 62, No. 12. pp. 6310-6323.

Harvard

Bauso, D 2017, 'Dynamic Demand and Mean-Field Games', IEEE Transactions on Automatic Control, vol. 62, no. 12, pp. 6310-6323. https://doi.org/10.1109/TAC.2017.2705911

Standard

Dynamic Demand and Mean-Field Games. / Bauso, Dario.

In: IEEE Transactions on Automatic Control, Vol. 62, No. 12, 12.2017, p. 6310-6323.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Bauso D. Dynamic Demand and Mean-Field Games. IEEE Transactions on Automatic Control. 2017 Dec;62(12):6310-6323. https://doi.org/10.1109/TAC.2017.2705911


BibTeX

@article{a600b4f43a11436e9d1ebdf950436ebe,
title = "Dynamic Demand and Mean-Field Games",
abstract = "Within the realm of smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies.",
keywords = "Mean-field games, power networks, stochastic stability, POWER GRIDS, SYSTEMS",
author = "Dario Bauso",
year = "2017",
month = "12",
doi = "10.1109/TAC.2017.2705911",
language = "English",
volume = "62",
pages = "6310--6323",
journal = "IEEE-Transactions on Automatic Control",
issn = "0018-9286",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "12",

}

RIS

TY - JOUR

T1 - Dynamic Demand and Mean-Field Games

AU - Bauso, Dario

PY - 2017/12

Y1 - 2017/12

N2 - Within the realm of smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies.

AB - Within the realm of smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies.

KW - Mean-field games

KW - power networks

KW - stochastic stability

KW - POWER GRIDS

KW - SYSTEMS

U2 - 10.1109/TAC.2017.2705911

DO - 10.1109/TAC.2017.2705911

M3 - Article

VL - 62

SP - 6310

EP - 6323

JO - IEEE-Transactions on Automatic Control

JF - IEEE-Transactions on Automatic Control

SN - 0018-9286

IS - 12

ER -

ID: 72166145