用内点法编写的电力系统机组组合程序，用于求解电力系统机组组合和经济调度问题

基于遗传算法的机组组合

In this paper, we investigate the representation of wind power forecasting (WPF) uncertainty in the unit commitment (UC) problem. While deterministic approaches use a point forecast of wind power output, WPF uncertainty in the stochastic UC alternative is captured by a number of scenarios that include crosstemporal dependency. A comparison among a diversity of UC strategies (based on a set of realistic experiments) is presented. The results indicate that representing WPF uncertainty with wind power scenarios that rely on stochastic UC has advantages over deterministic approaches that mimic the classical models. Moreover, the stochastic model provides a rational and adaptive way to provide adequate spinning reserves at every hour, as opposed to increasing reserves to predefined, fixed margins that cannot account either for the system’s costs or its assumed risks.

yalmip解决电力系统机组组合问题范例1的资源分为详细介绍yalmip解决机组组合问题的文档和相应的代码。后续将继续推出相关范例

基于改进离散粒子群算法的电力系统机组组合问题

利用PSO算法，通过matlab平台实现，旨在解决机组优化问题，适合初学者了解粒子群算法及其应用，程序内有相关注释，为避免算法陷入局部最优，对相关算子做了适当改进。