TY - GEN
T1 - Equilibrium Modelling and Analysis in Energy Market with a Novel Procurement Mechanism for Flexible Ramping Products
AU - Gao, Xiang
AU - Zhu, Ziqing
AU - Chan, Ka Wing
AU - Bu, Siqi
AU - Or, Siu Wing
AU - Zhang, Jiahao
N1 - Funding Information:
ACKNOWLEDGMENT This work was jointly supported by the Shenzhen Polytechnic (the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of SZPT (No.6022310042k), and the National Natural Science Foundation of China (No. 52077188).
Funding Information:
This work was jointly supported by the Shenzhen Polytechnic (the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of SZPT (No.6022310042k), and the National Natural Science Foundation of China (No. 52077188).
Publisher Copyright:
© 2023 IEEE.
PY - 2023/6
Y1 - 2023/6
N2 - Flexible Ramping Product (FRP) is a promising auxiliary service for providing short-term ramping capacity within a 5-15 minute time-slot, in response to the unpredictability of net load caused by the rising penetration of renewable generation. This paper comprises a comprehensive investigation of the procurement mechanism of FRP as well as an analysis of market equilibrium. Firstly, a novel joint market paradigm is proposed to reflect the willingness of generation companies (GENCOs) to supply energy and FRP, respectively. Then, a Markov Game (MG) model is developed to formulate the joint market bidding strategy optimization technique, with consideration of the uncertainty of renewable generation and load demand. An innovative Reinforcement-Learning-based algorithm is developed to solve the MG model with an estimate of the market equilibrium. In particular, it takes the privacy protection in the competitive bidding game into account during the simulation, and ensures the scalability of the algorithm by introducing the win-or-learn-fast (WOLF) scheme. Finally, two representative test markets are used to validate the proposed model and algorithm.
AB - Flexible Ramping Product (FRP) is a promising auxiliary service for providing short-term ramping capacity within a 5-15 minute time-slot, in response to the unpredictability of net load caused by the rising penetration of renewable generation. This paper comprises a comprehensive investigation of the procurement mechanism of FRP as well as an analysis of market equilibrium. Firstly, a novel joint market paradigm is proposed to reflect the willingness of generation companies (GENCOs) to supply energy and FRP, respectively. Then, a Markov Game (MG) model is developed to formulate the joint market bidding strategy optimization technique, with consideration of the uncertainty of renewable generation and load demand. An innovative Reinforcement-Learning-based algorithm is developed to solve the MG model with an estimate of the market equilibrium. In particular, it takes the privacy protection in the competitive bidding game into account during the simulation, and ensures the scalability of the algorithm by introducing the win-or-learn-fast (WOLF) scheme. Finally, two representative test markets are used to validate the proposed model and algorithm.
KW - bidding strategy
KW - FRP
KW - market equilibrium
KW - Markov Game
UR - http://www.scopus.com/inward/record.url?scp=85169415847&partnerID=8YFLogxK
U2 - 10.1109/PowerTech55446.2023.10202704
DO - 10.1109/PowerTech55446.2023.10202704
M3 - Conference article published in proceeding or book
AN - SCOPUS:85169415847
T3 - 2023 IEEE Belgrade PowerTech, PowerTech 2023
BT - 2023 IEEE Belgrade PowerTech, PowerTech 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Belgrade PowerTech, PowerTech 2023
Y2 - 25 June 2023 through 29 June 2023
ER -