Distributed Economic Dispatch of Embedded ...
Document type :
Article dans une revue scientifique: Article original
Permalink :
Title :
Distributed Economic Dispatch of Embedded Generation in Smart Grids
Author(s) :
DIBANGOYE, jille [Auteur]
Institut National des Sciences Appliquées de Lyon [INSA Lyon]
Doniec, Arnaud [Auteur]
Fakham, Hicham [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Colas, Frederic [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Guillaud, Xavier [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Institut National des Sciences Appliquées de Lyon [INSA Lyon]
Doniec, Arnaud [Auteur]
Fakham, Hicham [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Colas, Frederic [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Guillaud, Xavier [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Journal title :
Engineering Applications of Artificial Intelligence
Volume number :
44
Pages :
64-78
Publisher :
Elsevier
Publication date :
2015
ISSN :
0952-1976
Keyword(s) :
Smart grid
Multi Agent Planning
Distributed Stochastic Unit Commitment Problem
Multi Agent Planning
Distributed Stochastic Unit Commitment Problem
English keyword(s) :
Smart grid
Distributed stochastic unit commitment problem
Multi-agent planning
Information preserving
Distributed stochastic unit commitment problem
Multi-agent planning
Information preserving
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
Sciences de l'ingénieur [physics]/Energie électrique
Sciences de l'ingénieur [physics]/Energie électrique
English abstract : [en]
In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized ...
Show more >In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized generation for the unit commitment (UC) problem. Unit commitment problems consist in finding the optimal schedules and amounts of power to be generated by a set of generating units in response to an electricity demand forecast. While this problem has received a significant amount of attention, classical approaches assume that these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, etc. In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment problems. We introduce a novel distributed gradient descent algorithm which allows us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.Show less >
Show more >In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized generation for the unit commitment (UC) problem. Unit commitment problems consist in finding the optimal schedules and amounts of power to be generated by a set of generating units in response to an electricity demand forecast. While this problem has received a significant amount of attention, classical approaches assume that these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, etc. In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment problems. We introduce a novel distributed gradient descent algorithm which allows us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Comment :
https://hal.archives-ouvertes.fr/hal-01890396v1
Research team(s) :
Équipe Réseaux
Submission date :
2020-05-15T13:26:50Z
2022-02-11T08:04:50Z
2022-02-11T08:21:02Z
2022-02-11T08:04:50Z
2022-02-11T08:21:02Z
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