LSE's Demand Bidding Strategy Considering Dynamic Generator Interactions and Transitional Market Characteristics in Korea Electricity Market

Abstract

Korea's electricity market is undergoing significant reforms, including the introduction of a real-time market and two-way bidding systems. As these changes unfold, Korea's single Load Serving Entity (LSE), which manages annual wholesale electricity purchases of approximately 60 trillion won, faces critical challenges in determining optimal bidding quantities in the wholesale electricity market across different seasons and time periods. This study employs reinforcement learning using Multi-Agent Proximal Policy Optimization (MAPPO) to develop bidding strategies that reflect the special characteristics of Korea's electricity market, while utilizing SHAP (SHapley Additive exPlanations) to analyze the importance of various features in the demand bidding process. The simulation results reveal that marginal generators typically offer 40% below their actual costs in their minimum generation capacity, suggesting potential market price reductions with generation-side competition. LSE's bidding strategies show consistent underbidding patterns across seasons, where analysis of best and worst episodes reveals significant strategic divergence during high-demand seasons characterized by temporally differentiated aggressive bidding, but minimal variance during low-demand periods suggesting limited strategic impact. The SHAP analysis identifies the strategy index as the most influential factor in bidding decisions, with demand error and LSE's action emerging as secondary factors during peak and off-peak hours, respectively. This study provides meaningful market outcomes through reinforcement learning simulation despite the absence of real-time market and generators’ offer data, while providing valuable insights for future market design implementations by incorporating Korea's special market characteristics.