| Title: |
Moment Constrained Optimal Transport for Energy Demand Management of Heterogeneous Loads |
| Authors: |
Cardinal, Julien; Le Corre, Thomas; Bušić, Ana |
| Contributors: |
Apprentissage, graphes et optimisation distribuée (ARGO); Département d'informatique - ENS-PSL (DI-ENS); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS); Laboratory of Information, Network and Communication Sciences (LINCS); Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Mines-Télécom Paris (IMT)-Sorbonne Université (SU); ANR-22-PETA-0004,AI-NRGY,Distributed AI-based architecture of future energy systems integrating very large amounts of distributed sources(2022) |
| Source: |
NETGCOOP 2025 - 12th International Conference of Networks, Games, Control and Optimization ; https://hal.science/hal-05375478 ; NETGCOOP 2025 - 12th International Conference of Networks, Games, Control and Optimization, Oct 2025, Bilbao, Spain |
| Publisher Information: |
CCSD |
| Publication Year: |
2025 |
| Subject Terms: |
Smart Grids; Optimal Transport; Mean Field Control; [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] |
| Subject Geographic: |
Bilbao; Spain |
| Description: |
International audience ; This paper addresses the problem of coordinating a large population of heterogeneous electrical loads, such as electric vehicles (EVs) and water heaters (WHs), under global operational constraints. We extend the Moment Constrained Optimal Transport for Control (MCOT-C) framework to accommodate multiple classes of agents with distinct dynamics and cost structures. Our formulation relies on a meanfield limit that captures agent heterogeneity through class-specific distributions. We propose a scalable gradient descent algorithm and a Model Predictive Control (MPC) scheme that enables online adaptation of this algorithm to uncertain or progressively revealed agent information. The proposed approach is validated through numerical experiments on real datasets for EVs and WHs, demonstrating the effectiveness of this method in enforcing global constraints while preserving agent-level dynamics. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-05375478; https://hal.science/hal-05375478v1/document; https://hal.science/hal-05375478v1/file/NETGCOOP_2025%20%281%29.pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.84A574FF |
| Database: |
BASE |