TY - GEN
T1 - Development of Day-Ahead Peer-to-Peer Energy Trading with Time-Series Clustering
AU - Noorfatima, Nadya
AU - Jung, Jaesung
AU - Onen, Ahmet
AU - Yoldas, Yeliz
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Day-ahead trading of electricity has been applied to ensure the balance between the amount of electricity sold and bought. Even so, due to the intermittent distributed energy resources (DERs), the actual condition can be varied significantly, and forecasting can be costly in order to provide high accuracy to minimize losses. Hence, this paper proposes a novel model-based day-ahead peer-to-peer (P2P) energy trading with regionalized trading prices, which are determined through time-series clustering. To improve the determination of price regions, the data parameter is derived from the day-ahead condition, which is forecasted from network condition, trading capacity, and trading price of the P2P energy trading. The performance of the proposed model of day-ahead P2P energy trading is evaluated with respect to the market operation stability and optimality.
AB - Day-ahead trading of electricity has been applied to ensure the balance between the amount of electricity sold and bought. Even so, due to the intermittent distributed energy resources (DERs), the actual condition can be varied significantly, and forecasting can be costly in order to provide high accuracy to minimize losses. Hence, this paper proposes a novel model-based day-ahead peer-to-peer (P2P) energy trading with regionalized trading prices, which are determined through time-series clustering. To improve the determination of price regions, the data parameter is derived from the day-ahead condition, which is forecasted from network condition, trading capacity, and trading price of the P2P energy trading. The performance of the proposed model of day-ahead P2P energy trading is evaluated with respect to the market operation stability and optimality.
KW - day-ahead trading
KW - distributed optimization
KW - Peer-to-peer energy trading
KW - time-series clustering
UR - http://www.scopus.com/inward/record.url?scp=85183597150&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183597150&partnerID=8YFLogxK
U2 - 10.1109/ASEMD59061.2023.10369232
DO - 10.1109/ASEMD59061.2023.10369232
M3 - Conference contribution
AN - SCOPUS:85183597150
T3 - 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
BT - 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
Y2 - 27 October 2023 through 29 October 2023
ER -