TY - JOUR
T1 - Aggregated operation of heterogeneous small-capacity distributed energy resources in peer-to-peer energy trading
AU - Zhao, Xuesong
AU - Li, Long
AU - Tao, Yuechuan
AU - Lai, Shuying
AU - Zhou, Xingchao
AU - Qiu, Jing
PY - 2022/10
Y1 - 2022/10
N2 - Distributed energy resources (DERs) can provide flexibility and promote controllability for distribution networks. However, there are still some obstacles in the management of the small-capacity DERs. In this paper, an aggregated operation model of various heterogeneous DERs in peer-to-peer (P2P) energy trading is proposed. First, a supervised-learning assisted aggregation model of DERs is presented, which aims at estimating the aggregated parameters of heterogeneous DERs. Second, a P2P trading model is proposed for the aggregated DERs. The aggregators can trade with each other in a distributed manner so that privacy can be protected. Third, the fast alternating direction method of multipliers (F-ADMM) is utilized to accelerate the iteration process between the aggregators. Therefore, a quick decision can be made, and less information exchange is required between aggregators, which can reduce the communication burden. The effectiveness of the proposed methodology is verified in case studies. © 2022 Elsevier Ltd
AB - Distributed energy resources (DERs) can provide flexibility and promote controllability for distribution networks. However, there are still some obstacles in the management of the small-capacity DERs. In this paper, an aggregated operation model of various heterogeneous DERs in peer-to-peer (P2P) energy trading is proposed. First, a supervised-learning assisted aggregation model of DERs is presented, which aims at estimating the aggregated parameters of heterogeneous DERs. Second, a P2P trading model is proposed for the aggregated DERs. The aggregators can trade with each other in a distributed manner so that privacy can be protected. Third, the fast alternating direction method of multipliers (F-ADMM) is utilized to accelerate the iteration process between the aggregators. Therefore, a quick decision can be made, and less information exchange is required between aggregators, which can reduce the communication burden. The effectiveness of the proposed methodology is verified in case studies. © 2022 Elsevier Ltd
KW - Fast alternating direction method of multipliers
KW - Heterogeneous distributed energy resources
KW - Peer-to-peer trading
KW - Supervised-learning assisted aggregation model
UR - http://www.scopus.com/inward/record.url?scp=85127672472&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85127672472&origin=recordpage
U2 - 10.1016/j.ijepes.2022.108162
DO - 10.1016/j.ijepes.2022.108162
M3 - RGC 21 - Publication in refereed journal
SN - 0142-0615
VL - 141
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 108162
ER -