基于改进人工鱼群算法的大规模多目标机组组合优化

大型电力系统包含众多发电机组,且运行时需要考虑诸多方面的因素,其机组组合优化是一个多目标多约束的非线性大规模优化问题,现有方法存在诸多不足。人工鱼群算法在解决非线性优化问题时性能良好,但存在寻优效率低、可能陷入局部极值等缺点。针对这些不足,提出了改进的人工鱼群算法。该算法引入了可变视野,对人工鱼移动策略做出了调整并与遗传算法中的变异操作相结合。构建了兼顾经济性与环保性的多目标优化模型。为了解决机组规模扩大导致的计算时间过长问题,采用了分阶段的优化方法,将改进后的算法应用于启停安排阶段,确定机组启停状态后采用混合整数规划法进行负荷分配。针对最高包含1000台机组的大电网机组优化算例进行了模拟实验,实验结果表明:改进后的优化算法的收敛性和全局搜索能力均得到了提高,大规模机组组合的计算时间大大缩短。多目标条件下也取得了理想结果,验证了该方法的有效性。

Large-scale multi-objective unit commitment optimization based on an improved artificial fish swarm algorithm

Large-scale power systems contain a large number of generating units, and many factors need to be considered during their operation. Unit commitment optimization is a multi-objective and multi-constrained nonlinear large-scale optimization problem. Existing methods to solve this have many shortcomings. The artificial fish swarm algorithm has good performance in solving nonlinear optimization problems, but it has disadvantages such as low optimization efficiency and possibly falling into local extremes. In order to overcome these deficiencies, a modified artificial fish swarm algorithm is proposed. The algorithm introduces variable vision, adjusts the move strategy of artificial fish and combines the mutation operation in a genetic algorithm. A multi-objective optimization model considering both economy and environmental protection is constructed. A phased optimization method is adopted to solve the problem of overlong calculation time caused by the increase of unit scale. The modified algorithm is applied to the unit status arrangement phase, which determines the statuses of units. Then the mixed integer programming method is used to distribute the load. Simulation experiments are carried out for a large-scale power grid unit optimization with up to 1000 units. The experimental results show that the convergence and global search ability of the modified optimization algorithm are improved, the calculation time of large-scale unit commitment is greatly shortened, and ideal results under multi-objective conditions are also obtained. This verifies the effectiveness of the proposed method. This work is supported by the National Natural Science Foundation of China (No. 51677072) and the Fundamental Research Funds for the Central Universities (No. 2020MS120).