TY - GEN
T1 - Integrating Peer-to-Peer Energy Trading of Microgrids into Deregulated Electricity Market by Cascaded Model Predictive Control
AU - Lyu, Cheng
AU - Jia, Youwei
AU - Shi, Mengge
AU - Xu, Zhao
N1 - Funding Information:
This work was supported in part by National Natural Science Foundation of China (71971183 and 72071100), Guangdong Basic and Applied Basic Research Fund (2019A1515111173), Young Talent Program (Department of Education of Guangdong) (2018KQNCX223) and High-level University Fund (G02236002).
Funding Information:
This work was supported in part by National Natural Science Foundation of China (71971183 and 72071100), Guangdong Basic and Applied Basic Research Fund (2019A1515111173), Young Talent Program (Department of Education of Guangdong) (2018KQNCX223) and
Publisher Copyright:
© 2021 IEEE
PY - 2021/10
Y1 - 2021/10
N2 - Peer-to-peer (P2P) energy trading of microgrids is considered as a promising solution in reducing the carbon emission of renewable-embedded energy systems. This paper proposes a novel energy market framework for enabling the P2P local transactions under the deregulated market environment. The proposed market model is aimed to provide a practical mechanism design to incentivize optimal P2P energy trading in the existing electricity market. A cascaded model predictive control (MPC) problem formulation is devised for both the day-ahead market and adjustment markets. Finally, numerical results demonstrate the effectiveness of the proposed framework in integrating microgrid P2P trading into the electricity market.
AB - Peer-to-peer (P2P) energy trading of microgrids is considered as a promising solution in reducing the carbon emission of renewable-embedded energy systems. This paper proposes a novel energy market framework for enabling the P2P local transactions under the deregulated market environment. The proposed market model is aimed to provide a practical mechanism design to incentivize optimal P2P energy trading in the existing electricity market. A cascaded model predictive control (MPC) problem formulation is devised for both the day-ahead market and adjustment markets. Finally, numerical results demonstrate the effectiveness of the proposed framework in integrating microgrid P2P trading into the electricity market.
KW - electricity market
KW - microgrids
KW - model predictive control
KW - Peer-to-peer energy trading
UR - http://www.scopus.com/inward/record.url?scp=85128197113&partnerID=8YFLogxK
U2 - 10.1109/EI252483.2021.9713576
DO - 10.1109/EI252483.2021.9713576
M3 - Conference article published in proceeding or book
AN - SCOPUS:85128197113
T3 - 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021
SP - 114
EP - 118
BT - 5th IEEE Conference on Energy Internet and Energy System Integration
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
Y2 - 22 October 2021 through 25 October 2021
ER -
