Abstract
Given the significance of the electricity industry in today’s world, the role of virtual power plants (VPPs) in electricity markets must be closely examined as a key issue. These power plants, utilizing a wide range of distributed generation resources, operate as a single entity in energy markets. This study investigates innovative pricing strategies for VPPs participating in both reserve and energy markets under market price uncertainty. VPPs aggregate diverse distributed energy resources and require robust optimization tools to effectively navigate dynamic market conditions. A genetic algorithm–based model is proposed to develop profit‐maximizing bidding strategies considering technical constraints, load demands, and price volatility. The model is applied to an 18‐bus test system across three scenarios: without uncertainty, with partial uncertainty, and with full uncertainty in market prices and generation. Results show that the proposed method improves total profit by up to 4.5% under uncertainty scenarios compared to baseline and effectively shifts operational decisions to maximize profitability, particularly during low‐price hours. This demonstrates the model’s robustness and strategic value in complex electricity markets.