A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets

Antonio De Paola, Vincenzo Trovato, David Angeli, Goran Strbac

Research output: Contribution to journal › Article

Abstract

This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modelling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.
LanguageEnglish
Pages1-1
Number of pages1
JournalIEEE Transactions on Smart Grid
Early online date24 Jan 2019
DOIs
StatusE-pub ahead of print - 24 Jan 2019

Cite this

A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets. / De Paola, Antonio; Trovato, Vincenzo; Angeli, David; Strbac, Goran.

In: IEEE Transactions on Smart Grid, 24.01.2019, p. 1-1.

Research output: Contribution to journal › Article

De Paola, A, Trovato, V, Angeli, D & Strbac, G 2019, 'A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets', IEEE Transactions on Smart Grid, pp. 1-1. https://doi.org/10.1109/TSG.2019.2895247
De Paola A, Trovato V, Angeli D, Strbac G. A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets. IEEE Transactions on Smart Grid. 2019 Jan 24;1-1. https://doi.org/10.1109/TSG.2019.2895247
De Paola, Antonio ; Trovato, Vincenzo ; Angeli, David ; Strbac, Goran. / A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets. In: IEEE Transactions on Smart Grid. 2019 ; pp. 1-1.
@article{bcfc55cbcf42495ea8b5a6c5f7066389,
title = "A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets",
abstract = "This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modelling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.",
author = "{De Paola}, Antonio and Vincenzo Trovato and David Angeli and Goran Strbac",
year = "2019",
month = "1",
day = "24",
doi = "10.1109/TSG.2019.2895247",
language = "English",
pages = "1--1",
journal = "IEEE Transactions on Smart Grids",
issn = "1949-3053",
publisher = "IEEE",

}

TY - JOUR

T1 - A Mean Field Game Approach for Distributed Control of Thermostatic Loads Acting in Simultaneous Energy-Frequency Response Markets

AU - De Paola, Antonio

AU - Trovato, Vincenzo

AU - Angeli, David

AU - Strbac, Goran

PY - 2019/1/24

Y1 - 2019/1/24

N2 - This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modelling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.

AB - This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modelling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.

U2 - 10.1109/TSG.2019.2895247

DO - 10.1109/TSG.2019.2895247

M3 - Article

SP - 1

EP - 1

JO - IEEE Transactions on Smart Grids

T2 - IEEE Transactions on Smart Grids

JF - IEEE Transactions on Smart Grids

SN - 1949-3053

ER -

Access to Document

  • DePaolaTrovatoAngeliStrbac_TCLCoordinationMFG_FinalManuscript_OnlineVersion

    (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

    Accepted author manuscript, 1 MB