Version 1
: Received: 1 October 2024 / Approved: 1 October 2024 / Online: 1 October 2024 (16:09:54 CEST)
How to cite:
Wilk, P.; Wang, N.; Li, J. Multi-Agent Reinforcement Learning for Smart Community Energy Management. Preprints2024, 2024100082. https://doi.org/10.20944/preprints202410.0082.v1
Wilk, P.; Wang, N.; Li, J. Multi-Agent Reinforcement Learning for Smart Community Energy Management. Preprints 2024, 2024100082. https://doi.org/10.20944/preprints202410.0082.v1
Wilk, P.; Wang, N.; Li, J. Multi-Agent Reinforcement Learning for Smart Community Energy Management. Preprints2024, 2024100082. https://doi.org/10.20944/preprints202410.0082.v1
APA Style
Wilk, P., Wang, N., & Li, J. (2024). Multi-Agent Reinforcement Learning for Smart Community Energy Management. Preprints. https://doi.org/10.20944/preprints202410.0082.v1
Chicago/Turabian Style
Wilk, P., Ning Wang and Jie Li. 2024 "Multi-Agent Reinforcement Learning for Smart Community Energy Management" Preprints. https://doi.org/10.20944/preprints202410.0082.v1
Abstract
This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.
Keywords
Reinforcement Learning; energy management; multi-agent; electric vehicle
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
