Agent based simulation of centralized electricity transaction market using bi-level and Q-learning algorithm approach

The market participants face risks in market price fluctuations and uncertainties in the demand behaviour, regardless of the series of restructuring reforms of China power industry. To address this gap, the strategic pricing and bidding behaviour of generation and electricity selling enterprises in double-sided auctions of centralized electricity transaction market with elastic demand is examined. The market is modelled with two-level (Bi-level) mathematical optimization problem and Q-Learning algorithm. First, the upper level solves the profit maximization of the individual market players. Second, the lower level represents the market clearing at uniform transaction price by a market auctioneer using Lagrange relaxation method. Agent learning approach of QLearning algorithm is used to solve the two level mathematical problem. Both generation and electricity selling enterprises are modelled as Q-Learning agents with incomplete information about their counterparts. The simulation results show significant reduction in energy transaction price with the participation of flexible consumers in electricity market auctions, hence boosting the consumer savings. This study offers positive effects in reducing market prices in double-sided auction of electricity markets with elastic consumers using Q-learning algorithm as compared to a market with inelastic consumers.

Automated summary

This study examines the strategic pricing and bidding behavior of generation and electricity selling enterprises in double-sided auctions of centralized electricity transaction market with elastic demand using Q-learning algorithm. The simulation results show significant reduction in energy transaction price with flexible consumers participating in electricity market auctions, boosting consumer savings.