Coordination of Electric Vehicle Charging Through Multiagent Reinforcement Learning

Autor(es)

Da Silva, Felipe Leno ^[1] ; Nishida, Cyntia E. H. ^[1] ; Roijers, Diederik M. ^{[2, 3]} ; Costa, Anna H. Reali ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Intelligent Tech Lab, Sao Paulo 05508010 - Brazil

^[2] HU Univ Appl Sci Utrecht, Inst ICT, Utrecht 3584 - Netherlands

^[3] Vrije Univ Brussel, AI Lab, Brussels - Belgium

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON SMART GRID; v. 11, n. 3, p. 2347-2356, MAY 2020.
• Citações Web of Science: 0

Resumo

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near future, since EVs are regarded as valuable assets both for transportation and energy storage purposes. However, recharging a large fleet of EVs during peak hours may overload transformers in the distribution grid. Although several methods have been proposed to flatten peak-hour loads and recharge EVs as fairly as possible in the available time, these typically focus either on a single type of tariff or on making strong assumptions regarding the distribution grid. In this article, we propose the MultiAgent Selfish-COllaborative architecture (MASCO), a Multiagent Multiobjective Reinforcement Learning architecture that aims at simultaneously minimizing energy costs and avoiding transformer overloads, while allowing EV recharging. MASCO makes minimal assumptions regarding the distribution grid, works under any type of tariff, and can be configured to follow consumer preferences. We perform experiments with real energy prices, and empirically show that MASCO succeeds in balancing energy costs and transformer load. (AU)

• Processo FAPESP: 15/16310-4 - Transferência de Conhecimento no Aprendizado por Reforço em Sistemas Multiagentes
• Beneficiário: Felipe Leno da Silva
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/00344-5 - Reusando soluções de tarefas prévias em aprendizado por reforço multiagente
• Beneficiário: Felipe Leno da Silva
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado