Genetic Algorithm-Based Piecewise Bidding Transaction Algorithm for Power System Electricity Markets

This paper provides high-quality research accompanied by complete MATLAB source code implementing the genetic algorithm-based piecewise bidding transaction algorithm for power system electricity markets. The algorithm implementation includes comprehensive validation tests conducted on two common bidding curve patterns: step-type bidding curves, characterized by discrete price-quantity intervals, and linear bidding curves featuring continuous price-quantity relationships. The MATLAB implementation features adaptive genetic operators including tournament selection, simulated binary crossover, and polynomial mutation to handle different bidding curve characteristics. Computational results indicate that while the optimization effectiveness varies moderately with bidding curve properties - with step curves requiring more generations for convergence and linear curves demonstrating smoother optimization landscapes - both scenarios achieve significant cost reductions in electricity procurement.

Further investigation extended the algorithm's application to additional bidding curve configurations. The research demonstrates that the algorithm maintains satisfactory optimization performance across various bidding curve formats, including polynomial curves and hybrid bidding structures. The MATLAB code incorporates flexible curve parsing modules that can adapt to different bidding curve definitions through configurable parameter files, enabling easy extension to new market scenarios. This adaptability suggests broad applicability across diverse electricity market structures and bidding mechanisms.

In conclusion, our research validates the effectiveness and feasibility of the genetic algorithm-based approach for piecewise bidding transactions in electricity markets. The algorithm demonstrates excellent performance not only with step-type and linear bidding curves but also maintains robust optimization capabilities with other curve types. The implementation includes detailed documentation on parameter tuning for different market conditions, providing valuable support for electricity market development and operation. The open-source MATLAB code allows researchers and practitioners to modify genetic algorithm parameters, adjust fitness functions, and extend the framework to accommodate evolving market requirements.