Research on FTR Auction Model Incorporating Wind Power: Genetic Algorithm Source Code Based on Monte Carlo Simulation

This research paper investigates FTR (Financial Transmission Rights) auction models that incorporate wind power generation considerations. The study employs Monte Carlo simulation techniques combined with genetic algorithm implementations to perform robust analysis and experimental validation. The core implementation involves a genetic algorithm that optimizes FTR auction outcomes while accounting for wind power uncertainty through Monte Carlo scenarios. Key algorithmic components include: chromosome encoding for bid strategies, fitness functions evaluating revenue and risk metrics, and crossover/mutation operators for solution space exploration. The source code structure features modules for: wind power scenario generation using Monte Carlo methods, auction market simulation, genetic algorithm optimization routines, and result analysis frameworks. The implementation handles stochastic wind power outputs by generating multiple probability-weighted scenarios that feed into the FTR auction clearing process. The genetic algorithm implementation specifically includes population initialization with diverse bidding strategies, tournament selection mechanisms, and adaptive mutation rates to maintain solution diversity while converging toward optimal auction outcomes under wind power uncertainty conditions.