Study on Unit Commitment Problems for Large Scaled Distributed Generators

本研究计划提出一种遗传演算法来解决热电机组的投入问题。机组承诺问题是电力系统中最难的优化问题之一，其搜索空间巨大。为了减小搜索空间，提出了单位积分技术。初始种群通常是随机产生的。然而，很难产生可行的解决方案。为了获得可行的初始解，根据荷载数据生成初始种群。因此，可以得到可行的初始解。约束条件、输出范围和运行成本因机组而异。根据最小上/下时间约束将单元分为几组。根据成本特点，通过数值计算确定小机组的运行计划。其他机组调度由遗传算法确定。为了获得更优的运行调度，引入了新的遗传算子。智能变异执行局部爬坡优化技术。仿真结果表明，该方法能在合理的计算时间内确定出满意的投运计划。

This research project presents the genetic algorithm solution to the thermal unit commitment problem. Unit commitment problem is one of the most difficult optimization problems in power systems as the search space is vast. To reduce search space, unit integration technique is proposed. The initial population is often generated randomly. However, it is difficult to generate feasible solutions. To obtain feasible initial solutions, initial population is generated based on load data. Therefore, feasible initial solutions can be obtained. Constraints, output range and operation cost varies with each unit. Units are classified into several groups based on minimum up/down times constraint. The operation schedule of small units is determined by numerical calculation based on cost characteristic. Other unit schedule is determined by genetic algorithm. To obtain more optimal operation schedule, new genetic operators are introduced. The intelligent mutation performs local hill-climbing optimization technique. From simulation results, the proposed method can be determined satisfactory commitment schedule in reasonable computation time.