Equilibrium Modelling and Analysis in Energy Market With a Novel Procurement Mechanism for Flexible Ramping Products

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.