eprintid: 10196832
rev_number: 10
eprint_status: archive
userid: 699
dir: disk0/10/19/68/32
datestamp: 2024-09-11 09:39:18
lastmod: 2024-09-11 09:39:18
status_changed: 2024-09-11 09:39:18
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Chan, Kevin
creators_name: Asef, Pedram
creators_name: Benoit, Alexandre
title: Intelligent Energy Management using Multi-Agent Dynamic Learning for Scheduling Commercial Electric Vehicle Charging Stations
ispublished: inpress
divisions: UCL
divisions: B04
divisions: F45
keywords: Electric vehicles, Enegry Management
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: For commercial electric vehicles (CEVs), an underexplored challenge is the complexity of demand and supply management, which is vital for the efficient operation and broader adoption of CEVs. By leveraging advanced smart grid technologies and intelligent energy management systems, the research endeavors to create a cost-effective software solution for optimizing the charging process. This study deploys proximal policy optimization (PPO) multi-agent deep reinforcement learning (MARL) within an actor-critic network architecture. Agents are responsible for managing the supply and demand of energy from two grids welcoming ten charging stations each pumping energy from the integrated uninterruptible power supply (UPS). Performance metrics are compared against a dynamic programming (DP) approach, serving as a benchmark. The DP model excels when prior information is readily available. In contrast, PPO agents exhibit remarkable robustness and adaptability in environments lacking such information obtaining 95% accuracy. These insights not only enrich the existing academic discourse but also establish new performance benchmarks for practical implementations.
date: 2024-11-01
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: https://ieeexplore.ieee.org/Xplore/home.jsp
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2311394
lyricists_name: Asef, Pedram
lyricists_id: PASEF45
actors_name: Asef, Pedram
actors_id: PASEF45
actors_role: owner
full_text_status: public
pres_type: paper
pagerange: 1-6
event_title: 59th International Universities Power Engineering Conference
event_location: Cardiff, UK
event_dates: 2nd-6th September 2024
book_title: Proceedings of the 59th International Universities Power Engineering Conference
citation:        Chan, Kevin;    Asef, Pedram;    Benoit, Alexandre;      (2024)    Intelligent Energy Management using Multi-Agent Dynamic Learning for Scheduling Commercial Electric Vehicle Charging Stations.                     In:  Proceedings of the 59th International Universities Power Engineering Conference.  (pp. pp. 1-6).  Institute of Electrical and Electronics Engineers (IEEE)    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196832/1/V2_IEEE_Conference_Smart_Grid_and_MARL_revision_pa.pdf