TY  - JOUR
N1  - © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
SN  - 0306-2619
ID  - discovery10194877
AV  - public
JF  - Applied Energy
N2  - Electricity management systems are experiencing significant challenges due to the increased penetration of distributed energy resources. Electricity flows in distribution networks are transforming from unidirectional to bi-directional form. Consumers are transitioning to prosumers with different characteristics, where they take more active roles in electricity generation and consumption. Aggregators are vital financial intermediary agents in the power system transitions, as they could aggregate energy profiles of prosumers. The market competition between aggregators and interactions between prosumers and aggregators are complex and dynamic, which requires a holistic framework to model the market competition. This paper proposes an intelligent aggregation framework with edge computing, enabling decentralized competition for multiple aggregators and prosumers, which can be solved with a graph-based consensus algorithm. This study mathematically proves the proposed algorithm's convergence guarantee and convergence rate. In addition, the proposed framework is applied to an open-source dataset to demonstrate its applicability. Lastly, a benchmark analysis is conducted to show that the proposed algorithm has better communication complexity than the benchmark algorithms.
Y1  - 2024/11//
PB  - Elsevier BV
VL  - 373
A1  - Cheng, Xiaoyuan
A1  - Yao, Ruiqiu
A1  - Postnikov, Andrey
A1  - Hu, Yukun
A1  - Varga, Liz
UR  - https://doi.org/10.1016/j.apenergy.2024.123860
KW  - Intelligent aggregation
KW  -  
Prosumers
KW  -  
Energy transition
KW  -  
Edge computing
KW  -  
Distributed energy resources
KW  -  
Graph-based consensus algorithm
TI  - Decentralized intelligent multi-party competitive aggregation framework for electricity prosumers
ER  -