A Multi-Objective Unit Commitment Approach Using Genetic Algorithms

The Unit Commitment (UC) problem is a critical optimization challenge in power system operations, aiming to determine the optimal schedule of power generation units while minimizing costs and emissions. This paper proposes a multi-objective genetic algorithm (GA) approach to address the UC problem with dual objectives: minimizing operational costs and reducing emissions. The proposed method leverages the NSGA-II to effectively navigate the complex solution space, balancing conflicting objectives and ensuring all operational constraints are met. The algorithm's performance is validated on the 39-bus IEEE test system, demonstrating its ability to generate diverse Pareto-optimal solutions that offer decision-makers a range of viable trade-offs between cost and environmental impact. Results indicate that the GA-based approach outperforms traditional methods by providing a broader set of optimal solutions and efficiently handling the trade-offs inherent in multi-objective optimization. This study lays the foundation for future research incorporating network constraints, renewable energy resources, and energy storage into the multi-objective UC framework.