Strategic Bidding of Price-quantity Pairs in Electricity Market Based on Deep Reinforcement Learning
The domestic electricity market generally adopts the mechanism of"priority consumption and guaranteed procurement"to meet the requirements of renewable energy accommodations.Therefore,traditional energy will compete in the market with high uncertainty in the net load and maximize its profits through strategic bidding.However,existing research on strategic bidding only considers generators'bidding prices without bidding quantities.It ignores the initiative of competitors in the game,making it difficult to reflect the real market bidding behaviors of strategic generators.This paper proposes a deep reinforcement learning-based method for analyzing the bidding strategy of price-quantity pairs in electricity markets.First,to overcome the shortcomings of existing strategic bidding studies that only considered bidding prices without bidding quantities,a two-level bilinear bidding model for a generator considering price-quantity pairs is studied.Then,to consider the uncertainties of competitors'behaviors,probability mappings between typical bids of generators and net loads based on the K-Medoids clustering method and deep neural network are constructed,aiming to provide a bidding environment close to the real market for the strategic generator.Finally,to solve the two-level bilinear model of strategic bidding efficiently,a deep deterministic policy gradient reinforcement learning method considering incomplete information games and net load uncertainty is explored.Case studies verify the effectiveness of our model of price-quantity pairs,as well as the robustness of the proposed method to cope with changes in net load and competitors'behaviors in the electricity market.They can improve the bidding profits of the generator.
万方数据
维普
