Solving Unit Commitment Problems Using Priority List Method with Code Implementation Insights

The application of Priority List Method in Unit Commitment problems Unit Commitment (UC) is a core optimization problem in power system dispatch, aiming to minimize operational costs while meeting electricity demand by optimally scheduling generator unit status (on/off) and power output. For large-scale 1024-node systems, traditional optimization methods may face computational efficiency challenges, making the Priority List Method a practical choice due to its simplicity and efficiency. Core Algorithm Implementation Priority Ranking: Establish a static priority list by sorting all generating units based on economic indicators (e.g., average fuel cost) or technical parameters (e.g., minimum up/down time). Code implementation typically involves creating a sorting function that compares unit characteristics and returns an indexed priority array. Sequential Loading: Activate units sequentially starting from the highest-priority unit until system load and reserve requirements are met. This can be implemented through a while-loop that accumulates capacity while checking constraints. Dynamic Adjustment: Incorporate unit commitment constraints (e.g., minimum runtime) across time dimensions using forward/backward search algorithms to fine-tune startup/shutdown schedules. This requires implementing constraint-checking functions that validate temporal dependencies. Special Considerations for 1024-Node Systems Computational Efficiency: The method's near-linear computational complexity makes it suitable for large-scale systems. Implementation can optimize this using vectorized operations instead of nested loops. Network Constraint Simplification: Typically ignores transmission line capacity limits, focusing on economic unit prioritization. When necessary, can integrate with DC power flow models through interface functions. Data Preprocessing: Normalize unit parameters to prevent dimensional discrepancies from distorting priority rankings. This involves standardization functions applied to input data before sorting. Advantages and Limitations Analysis Strengths: Simple logic and fast solution speed, ideal for real-time applications or as initial solutions for advanced algorithms. The method can be coded with minimal computational overhead. Limitations: Cannot rigorously handle complex constraints (e.g., ramp rates, network security), potentially leading to local optima. Implementation should include validation checks for constraint violations. Practical applications often use this method as the first step in hybrid solution strategies, followed by further optimization using Lagrangian relaxation or dynamic programming. For 1024-node systems, parallel computing techniques are recommended to accelerate the priority evaluation phase, potentially through multiprocessing libraries that distribute sorting operations across cores.