铁路调度指挥综合信息系统在鄂钢的应用

根据钢铁企业的铁路运输特点,在同类企业现有铁路运输管理信息系统的基础上,开发了集行车组织、调度指挥、车地联控、车号识别、视频监控和统计查询等多方位为一体的铁路运输调度指挥综合信息系统,论述了其结构和主要管控功能。该系统在武钢集团鄂钢公司铁路运输中心试运行效果良好。     

邻域自适应粒子群算法求解地源热泵区域 能源系统鲁棒优化调度问题

针对地源热泵区域能源系统中冷热负荷和机组效能的不确定性, 本文提出了一种考虑双重不确定性的鲁棒优化调度方法. 首先, 基于多面体不确定模型描述调度模型中的鲁棒变量. 然后, 针对建筑冷热负荷不确定性, 采用对偶原理将双层优化模型等价为单层优化模型; 对于机组效能不确定性, 采用场景法进行分析. 最后, 采用多目标优化约束处理方法处理鲁棒优化调度模型中的约束条件. 同时, 为更加高效、准确求解所构建的优化调度模型, 提出了一种邻域自适应粒子群优化算法(NAPSO). 实验结果表明, 在制冷和制热工况下, 与经验运行策略相比, 本文所提方法可分别减少7.22%和5.55%的系统运行成本, 是一种解决地源热泵区域能源系统鲁棒优化调度的有效方法.     

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.     

无功优化问题三类解的物理特性

在有功调度方式给定的前提下,以有功网损最小作为度量指标,假设仅以连续的无功电源作为控制手段建立数学分析模型,应用非线性原对偶内点算法进行求解.从数学优化的角度按无功分布与电压水平之间牵制疏密程度定义了3种解的状态,即松弛解、局部制约解和临界制约解,分析了其物理本质、优化信息以及对电力系统运行性能的影响,并以三节点两端供电系统和IEEE30节点系统为例予以阐述,为有效地实施无功、电压调度与控制提供了一种思路.     

基于过程状态特征化的电容器动态规划与控制

研究了电容器规划与动态控制问题,首先建立了给定时段内配电网有功能量损耗的评估模型,并在考虑负荷变化的情况下,依靠过程状态特征化的概念构造了一个以系统能量损耗最小为目标的无功优化策略,适当的线性化处理和逐步修正思想大大简化了电容器规划与动态控制问题的数学模型,降低了求解难度,运用静态计算方法解决了动态优化问题,有效降低了动态无功优化所需的计算量。应用所提出的策略对IEEE-69节点配电系统进行了仿真计算,验证了所述方法的合理性和有效性。     

基于主从博弈的输配电网协同经济调度

针对市场环境下如何协调主动配电网与输电网的运行策略以实现二者共赢的问题,提出一种基于主从博弈的输配电网协同经济调度策略。首先,采用主从博弈架构建立了输配电网双层经济调度优化模型,其中上层为输电网价格出清模型,下层为考虑二阶锥交流潮流约束的配电网经济调度模型。然后,应用二阶锥重构技术、KKT(Karush-Kuhn-Tucker)条件、强对偶定理和线性松弛技术将所提双层模型转化为混合整数二阶锥单层规划模型进行求解。最后通过算例验证了模型和方法的有效性。结果表明,所提输配协同经济调度策略可以有效降低输电网电能及备用出清价格,同时提高了配电网运行经济性。     

基于混合智能算法的电力系统经济负荷分配

为解决复杂、不连续、不可导、非线性的电力系统经济负荷分配（economic load dispatch,ELD）问题,提出粒子群算法（particle swarm optimization,PSO）结合单纯形算法（simplex method,NM）的混合智能算法（simplex method-particle swarm optimization,NMPSO）。利用NMPSO解决ELD问题时,综合考虑了发电机组的阀点效应和系统网损,使ELD更接近实际情况,充分利用 PSO 随机性的全局寻优能力和 NM快速确定性的局部寻优能力,弥补了 PSO和NM分别单独应用时的不足。仿真结果表明,NMPSO应用于ELD问题,具有较好的优化效果。     

浙江电力调度MPLS VPN实现的探讨

根据浙江电力调度数据网络的应用环境和需求，提出采用MPLS VPN技术来实现各个应用系统的专用网络，并对MPLS VPN的实现方式进行了探讨。     

A new approach to secure economic power dispatch

This article presents a new nonlinear convex network flow programming model and algorithm for solving the on-line economic power dispatch with N and N−1 security. Based on the load flow equations, a new nonlinear convex network flow model for secure economic power dispatch is set up and then transformed into a quadratic programming model, in which the search direction in the space of the flow variables is to be solved. The concept of maximum basis in a network flow graph was introduced so that the constrained quadratic programming model was changed into an unconstrained quadratic programming model which was then solved by the reduced gradient method. The proposed model and its algorithm were examined numerically with an IEEE 30-bus test system on an ALPHA 400 Model 610 machine. Satisfactory results were obtained.     

面向启发式调整策略和粒子群优化的机组组合问题

提出一种启发式调整策略和粒子群优化相结合的新方法求解电力系统中的机组组合(UC)问题.算法将UC问题分解为具有整型变量和连续变量的两个优化子问题,采用离散粒子群优化和等微增率相结合的双层嵌套方法对外层机组启、停状态变量和内层机组功率经济分配子问题进行交替迭代优化求解.同时构造了关机调整和替换调整两个启发式搜索策略对优化结果进行进一步局部微调以提高算法解决UC问题的全局寻优能力和计算效率,从而有效改善解的质量.以10～100台机组组成的5个测试系统为算例,通过与其他算法结果进行比较分析,验证了该方法的可行性和有效性.仿真结果表明该方法解决大规模机组组合问题具有求解精度高和收敛速度快的优势.