EconPapers    
Economics at your fingertips  
 

Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization

Niphon Kaewdornhan, Chitchai Srithapon, Rittichai Liemthong and Rongrit Chatthaworn ()
Additional contact information
Niphon Kaewdornhan: Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Chitchai Srithapon: Department of Electrical Engineering, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Rittichai Liemthong: Business Development Engineer, Sermsang Power Corporation Public Company Limited, Bangkok 10300, Thailand
Rongrit Chatthaworn: Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

Energies, 2023, vol. 16, issue 5, 1-25

Abstract: Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.

Keywords: Electric Vehicle; energy storage; energy management; multi-agent optimization; reinforcement learning; solar PhotoVoltaic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/5/2357/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/5/2357/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:5:p:2357-:d:1084595

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2357-:d:1084595