求解机组组合问题的嵌入贪婪搜索机制的改进粒子群优化算法。
An improved particle swarm optimization algorithm embedded with greedy search for solution of unit commitment.
该文针对机组组合问题，提出了一种新的混合粒子群优化算法。
This paper proposes a new hybrid particle swarm optimization method for unit commitment problem.
最后，将算法进行改进用于求解考虑机组爬坡速率的机组组合问题。
Finally, the proposed algorithm was improved for solving the unit commitment with ramp rate constrains.
文中提出了一种基于改进离散粒子群优化算法求解机组组合问题的新方法。
A solution to unit commitment via an enhanced binary particle swarm optimization (BPSO) algorithm is presented.
文章给出在不确定的荷载需求下机组组合问题的一些常见的随机规划模型和算法的综述。
In this paper we give a survey of several stochastic programming models and algorithms for unit commitment problem under uncertainty load demands.
通过分析火电机组的燃料耗量特性和机组启停特性，介绍了机组组合问题中各种成本的计算方法。
By analyzing the characteristics of fuel consumption and start-up cost of thermal power plant, describes the calculation for the various kinds cost of thermal power plant.
电力系统机组组合问题是一个典型的大规模混合整数的非线性组合优化问题，很难得到理论上的最优解。
Power system unit commitment, a problem of nonlinear commitment optimization with typical large-scale hybrid integers, is difficult to get an optimal solution in theory.
通过对测试系统的MATLAB仿真实验和结果分析验证了随机聚焦粒子群算法用于求解机组组合问题的有效性和优越性。
By simulating and analysis of the results of test system using MATLAB, effectiveness and superiority of SFPSO for solving the unit commitment problem are confirmed.
在为机组组合问题编制算法的过程中，因需要满足错综复杂的约束条件，容易使程序结构混乱、逻辑判断出错，最终导致算法求解失败。
During the programming of the algorithm for the unit commitment problem, the complex constraints often lead the program to bad structure or wrong result.
针对此问题，建立了考虑机群出力约束的机组组合模型，将问题转化为两层多下级无关联的主从递阶单目标决策问题。
The model is translated into a two level single object decision making problem with leader follower strategy with no relation between lower levels.
针对此问题，建立了考虑机群出力约束的机组组合模型，将问题转化为两层多下级无关联的主从递阶单目标决策问题。
The model is translated into a two level single object decision making problem with leader follower strategy with no relation between lower levels.
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