A digital twin of multiple energy hub systems with peer-to-peer energy sharing

Shiyao Li, Yue Zhou, Jianzhong Wu, Yiqun Pan, Zhizhong Huang and Nan Zhou

Applied Energy, 2025, vol. 380, issue C, No S0306261924022918

Abstract: As climate change has become a global concern, the decarbonization of energy systems has become a prominent solution for CO2 emission reduction. The recent emergence of multi-energy hub systems (MEHs), characterized by interconnected energy hubs (EHs) and facilitated by energy sharing, presents a promising solution for seamlessly integrating a significant share of renewable energy sources (RESs) and flexibility among EHs. Faced with the intricate interplay and uncertainty of future energy markets, an extensive digital twin (EXDT) is proposed to perform predictive testing and evaluate the performance of MESs. This EXDT provides energy system operators with insights into the coordinated behavior of interconnected EHs under various future scenarios, thus contributing to smarter decision-making processes. Specifically, an array of scenarios including different decision-making strategies and P2P energy sharing strategies were considered. For each of these scenarios,"what-if" tests were conducted using a multi-agent reinforcement learning (MARL)-based method to model the stochastic decision-making process of EHs belonging to different stakeholders with access to local information. Uncertainties during operation can be mitigated using Markov Game (MG) by capturing knowledge from historical energy data. Subsequently, the economic and technical performance were evaluated using multidimensional evaluation indexes. The proposed MARL-based EXDT was applied to a representative 4-EH multi-energy system in China. Simulation results indicate that P2P energy sharing facilitates the local consumption of renewable energy, providing additional financial benefits and self-sufficiency to each EH and offering peak shaving to the upstream grid. Additionally, system performance under various decision-making and P2P sharing strategies was tested and evaluated to identify the impact of these strategies on system operation.

Keywords: Digital twin; Multi-energy management; Multi-agent deep reinforcement learning; Peer-to-peer energy trading; Decarbonization; Energy hub (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924022918
Full text for ScienceDirect subscribers only

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:eee:appene:v:380:y:2025:i:c:s0306261924022918

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.124908

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().