<scp>GA</scp> ‐Based Optimization Bidding Strategy for <scp>PV</scp> Power on Spot and Balancing Markets Considering Risk Factors

The integration of photovoltaic (PV) power generation into the electricity market presents unique challenges due to the intermittent and unpredictable nature of solar energy. This study focuses on optimizing bidding strategies for PV power on both the spot and balancing markets using genetic algorithms (GA). The objective is to maximize total bidding profits while minimizing the risks associated with PV output and market price uncertainties. The primary objective of this research is to develop an optimized bidding strategy that maximizes the total revenue from participating in the spot and balancing markets while considering the inherent risks associated with PV output and market price fluctuations. In this study, a GA‐based optimization model is developed for PV power bidding on the spot and balancing markets. The results of the numerical simulation validate the model developed and highlight the impact of market risks and PV power output uncertainties on bidding strategies and revenues. The results suggest that incorporating realistic forecast errors and market risks into the bidding strategy enhances the profitability and reliability of the participation of PV power in the electricity markets. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.