AI-based integration of flexible diverse electricity aggregators in multiple electricity markets under uncertainty

Electricity aggregators and virtual power plants (VPPs) have emerged as critical mechanisms for enhancing grid asset utilization and enabling small-scale distributed energy resources to participate in wholesale electricity markets by removing the minimum capacity requirement of electricity markets. This study proposes a framework to integrate diverse energy resources including wind, storage, and demand response across day-ahead, intraday, and real-time markets. The research addresses the complex challenges of market participation, particularly in wind-penetrated markets where electricity prices exhibit hidden nonlinear correlations with wind power production. To manage these complexities, the study employs advanced deep learning methodologies to predict electricity market prices and wind power generation, utilizing these predictions as input for scenario generation. A robust modeling approach combines stochastic programming and robust optimization through a coherent risk measure of stochastic p-robust optimization. This methodology aims to mitigate operational risks of VPP inherent in multi-market energy trading. Empirical simulation results demonstrate the framework's effectiveness, revealing a strategic trade-off where a 6.81 % reduction in expected profit corresponds to a significant 45.75 % decrease in relative regret for electricity aggregators within the VPP network.