Adaptive Multi-Agent System and Mixed Integer Linear Programming Optimization Comparison for Grid Stability and Commitment Mismatch in Smart Grids

Sharyal Zafar (1, 2) , Vishwajeet Maurya (3) , Anne Blavette (1, 4) , Guy Camilleri (3, 5) , H Ben Ahmed (1, 2) , Marie-Pierre Gleizes (3, 5)

1 SATIE - Systèmes et Applications des Technologies de l'Information et de l'Energie
2 ENS Rennes - École normale supérieure - Rennes
3 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
4 CNRS - Centre National de la Recherche Scientifique
5 UT3 - Université Toulouse III - Paul Sabatier

Existing electrical networks are going through a transition and distributed energy resource, if not managed properly, can hinder this transition. Uncontrolled introduction of photovoltaics and electric vehicles in distribution networks would lead to substantial issues such as commitment mismatches, line congestions, voltage deviations, etc. This paper presents the use of a classical approach, mixed integer linear programming optimization, and a novel approach, adaptive multi-agent system, to solve the highlighted distribution side challenges by utilizing electric vehicles' storage capacity. This comparison serves as a great tool to benchmark the performance of the under-development adaptive multi-agent system methodology.

Mots clés: Multi-Agent Systems; Mixed Integer Linear Programming; Optimality Gap; Electric Vehicles; Balance Responsible Party; Distribution Network Stability

Domaines: Energie électrique

Sharyal Zafar, Vishwajeet Maurya, Anne Blavette, Guy Camilleri, H Ben Ahmed, et al.. Adaptive Multi-Agent System and Mixed Integer Linear Programming Optimization Comparison for Grid Stability and Commitment Mismatch in Smart Grids. 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe 2021), Oct 2021, Espoo (online), Finland. ⟨10.1109/isgteurope52324.2021.9640019⟩. ⟨hal-03398611⟩