Simple Unit Commitment (UC) Example Program

The Unit Commitment (UC) problem is a classical optimization challenge in power system operation and control. This problem primarily addresses how to合理安排 generator unit on/off status and power output allocation while meeting electricity load demand, aiming to minimize system operating costs.

The example program typically consists of three core modules:

Main Program Module Serves as the control center of the entire system, responsible for coordinating the operation flow of all submodules. Key functionalities include data input/output processing, parameter initialization, and algorithm scheduling. In code implementation, the main program reads fundamental data such as generator parameters and load curves, then invokes subsequent optimization modules for computation. This module often includes file I/O operations and serves as the primary entry point for the UC solver.

Economic Dispatch Module Under given unit commitment states, this module performs optimal power distribution calculations using the equal incremental cost principle or other optimization algorithms. The implementation must consider physical constraints like generator output limits and ramp rates, solving nonlinear programming problems to minimize generation costs. Code-wise, this typically involves constraint handling and mathematical optimization solvers (e.g., quadratic programming or linear programming implementations).

Dynamic Programming Module Employs multi-stage decision-making methods to handle unit startup/shutdown problems. The scheduling period is divided into multiple time intervals, constructing state transition equations and recursive relationships. Through forward or backward search algorithms, it identifies optimal unit commitment combinations. The programming challenge here involves handling integer variables and mitigating state space explosion issues, often requiring pruning techniques or heuristic approaches in the implementation.

Practical algorithm improvements typically incorporate techniques like priority list methods or Lagrangian relaxation to enhance computational efficiency. During programming implementation, additional practical constraints such as minimum up/down times and reserve capacity requirements must be considered, placing higher demands on algorithm robustness and computational performance.