基于内点法的含风电场电力系统随机最优潮流

基于机会约束规划的随机最优潮流模型的求解一般采用智能优化算法,提出了一种内点法结合随机调节因子更新的方法,以求解基于机会约束规划的含风电场电力系统的随机最优调度模型。模型考虑了节点电压和线路功率的机会约束,并采用半不变量和Cornish-Fisher级数结合的方法来计算机会约束的概率分布。引入调节因子,对机会约束概率进行变形,将随机最优潮流问题转化为可采用内点法求解的最优潮流模型。提出了调节因子的更新方法,通过原始对偶内点法的参数结果更新调节因子来调节机会约束满足的概率,迭代求解机会约束随机最优潮流模型。最后,以IEEE14和IEEE118节点系统为例,验证了方法的正确性和有效性。     

一类整数线性规划的算法

提出了一类常见的整数线性规划的新算法,该算法不是沿袭求解线性规划的传统思路,从可行域的边缘整数点上寻找最优解,而是根据各变量对目标的贡献大小确定出分配变量,经有限次分配后可获得最优解．该算法计算量较小,计算效率高,且在有限步内可获得最优解．与目前的分枝定界法、割平面法相比,具有一定的优越性     

一类整数线性规划的算法

提出了一类常见的整数线性规划的新算法,该算法不是沿袭求解线性规划的传统思路,从可行域的边缘整数点上寻找最优解,而是根据各变量对目标的贡献大小确定出分配变量,经有限次分配后可获得最优解。该算法计算量较小,计算效率高,且在的限步内可获得最优解。与目前的分枝定界法、割平面法相比,具有一定的优越性。     

一种改进的最优潮流算法研究

提出一种改进的信赖域内点算法.新方法无须与潮流计算配合增加算法通用性,并由常规潮流获得初始点改善信赖域子问题可行性;此外,改进信赖域子问题模型提高计算精度,调整收敛判据加快计算速度;由现代内点法求解信赖域子问题,并构造简约修正方程减小计算量.用改进的方法求解电力系统中以发电费用最小为目标的最优潮流问题.通过对IEEE14-300测试系统的数值仿真实验,证明所提出的算法是正确和有效的.     

基于改进原对偶对数障碍法的最优潮流算法

在原对偶对数障碍法的基础上,提出一种改进算法用于解决最优潮流问题. 利用障碍参数对改进障碍函数的影响,使改进算法能有效地处理最优潮流问题中的不等式约束,避免了计算中对有效约束的识别问题. 将改进算法与原有算法用于IEEE 30节点系统无功优化问题的仿真计算,结果证明了改进方法是有效可行的,且与原有方法相比,可求得更优解.     

基于GA的毛细管空调系统水力计算方法的研究

本文在阐述了毛细管平面辐射空调系统特点的基础上,阐明了应用遗传算法(GA)求解多目标优化问题的思路;通过对毛细管水力计算模型的建立,将毛细管平面辐射空调系统的水力计算问题转化成了可以应用遗传算法(GA)的求解多目标优化问题的Pareto最优解;分析比较了权重系数变换法、并列选择法、排列选择法、共享函数法、混合法在求解水力计算模型多目标优化问题中的优缺点;提出了针对不同情况应选定的适宜方法.     

原-对偶内点法和预测-校正内点法在最优潮流的应用

最优潮流问题在数学上是一个带约束条件的优化问题,其模型包括目标函数以及等式约束条件和不等式约束条件。利用原-对偶内点法和预测-校正内点法进行最优潮流的计算,原-对偶内点法是在保持原始可行性和对偶可行性的同时,沿一条原-对偶路径寻找最优解。预测-校正法在进行泰勒展开时保留了高阶项,首先通过修正方程计算仿射方向,在计算得到仿射扰动因子后回代入修正方程得到校正方向,进而得到修正量。预测-校正法具有比原-对偶法更好的收敛性,用Matlab实现了原-对偶内点法和预测-校正内点法进行潮流优化计算,并用算例进行了验证。     

基于改进原对偶对数障碍法的最优潮流算法

在原对偶对数障碍法的基础上，提出一种改进算法用于解决最优潮流问题．利用障碍参数对改进障碍函数的影响，使改进算法能有效地处理最优潮流问题中的不等式约束，避免了计算中对有效约束的识别问题．将改进算法与原有算法用于IEEE 30节点系统无功优化问题的仿真计算，结果证明了改进方法是有效可行的，且与原有方法相比，可求得更优解．     

基于牛顿法的电力系统最优潮流计算

为研究电力系统最优潮流问题的可行算法,对牛顿法进行探讨并基于该算法进行最优潮流计算.由于最优潮流问题属于典型有约束条件的非线性规划问题,故引入二次罚函数处理约束条件,将牛顿法和二次罚函数结合并用MATLAB仿真平台进行算法编程,求出IEEE14节点标准系统的最优潮流计算结果,同时得出收敛时间和系统发电成本.实验结果表明:该方法的收敛性较好,计算速度较快;运用牛顿算法求解最优潮流,可使发电成本最小或功率损耗最小,从而达到优化资源配置,降低发电及输电成本的目的,具有很好的经济效益和社会效益.     

基于粒子群与模拟退火相结合的无功优化算法

针对电力系统无功优化采用粒子群算法容易陷入局部最优、模拟退火算法约束条件多和收敛速度慢的问题,提出一种新的基于粒子群与模拟退火相结合的算法.该算法根据粒子群的易实现性、快速收敛性及模拟退火的全局收敛性,进行协同搜索,求取系统无功优化的解集.对IEEE14、57、118节点系统进行了无功优化仿真计算,结果表明该算法原理简单易实现,计算效率高且能获得质量更高的解.     

电厂循环水系统优化控制及其遗传算法求解

循环水系统作为火电厂的重要组成部分,其优化运行问题已日益引起人们的高度重视.在分析调速水泵的运行特性和高效区约束条件的基础上,给出了基于最小轴功率的循环水系统效率优化模型,并用遗传算法求解,得到最优决策向量.实验结果表明:该优化模型和求解算法能够有效地寻求循环水系统的最优运行组合和最佳调速比,使水泵运行在高效区内,提高循环水系统运行效率;与约束变尺度算法相比,遗传算法更具优越性,其求解速度较快,求解质量较高     

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network. Foundation item: Project (70671039) supported by the National Natural Science Foundation of China     

基于APSO算法的水火电力系统模糊多目标优化调度

建立了以火电厂运行成本最低、污染气体排放最小和水电厂发电效益最大为目标的水火电短期多目标优化调度模型,以3个火电厂与4个梯级水电厂的组成的水火电系统相关数据为实际算例,采用基于反捕食粒子群算法的交互式模糊满意度多目标决策方法,建立不同的决策模型将多目标问题转化为单目标求解,决策者可根据不同的主观效益诉求得到满意的调度方案,避免了人为选取目标权重的随意性,决策计算过程简单实用。算例结果表明,所建模型的正确性和该方法的可行性。     

A new method on hydrothermal scheduling optimization in electric power market

The hydrothermal scheduling in the electric power market becomes difficult because of introducing competition and considering sorts of constraints. An augmented Lagrangian approach is adopted to solve the problem,which adds to the standard Lagrangian function a quadratic penalty term without changing its dual property,and reduces the oscillation in iterations. According to the theory of large system coordination and decomposition,the problem is divided into hydro sub-problem and thermal sub-problem,which are coordinated by updating the Lagrangian multipliers,then the optimal solution is obtained. Our results for a test system show that the augmented Lagrangian approach can make the problem converge into the optimal solution quickly.     

Solvability identification and feasibility restoring of divergent optimal power flow problems

Optimal power flow (OPF) has been considered as an important problem in power systems. Although several excellent algorithms, such as Newton method and interior point method, have been developed to solve the OPF problem, divergences still often occur. Till now, few works have focused on the solvability identification and feasibility restoring of divergent OPF problems. In this paper, we propose a systematic approach to identify the solvability of divergent OPF problems, and restore a feasible solution for unsolvable OPF cases. The proposed approach consists of two phases: solvability identification phase (SIP) and feasibility restoring phase (FRP). In SIP, a novel methodology based on problem transformation and active set is adopted to identify the solvability of divergent OPF problem. If a feasible solution can be obtained in SIP, then this divergent OPF problem is solvable, otherwise, FRP is used to restore a feasible or optimal solution by relaxing soft constraints and load shedding. In FRP, a feasibility restoring model is presented, and a priority-listing strategy of restoring actions is proposed to restore the unsolvable OPF problems. Numerical studies indicate that the proposed SIP and FRP are reliable to diagnose the solvability of the divergent OPF problems, give an index to measure the unsolvability, and restore an unsolvable OPF case. Supported by the National Natural Science Foundation of China (Grant No. 50507018), the Key Project of Chinese Ministry of Education (Grant No. 107063) and the Natural Science Fund of Zhejiang Province (Grant No. R1080089)     

基于免疫算法的电力线通信网接入点规划方法

为了保证电力系统安全有效地运行,需要对接入点位置进行部署规划。首先建立接入点位置优化模型,然后综合考虑经济性、可靠性、网络时延和N-1原则,提出了一种基于免疫算法的配用电通信网接入点规划方法。仿真结果表明,该方法能在不同网络条件下进行接入点部署规划,并在保证可靠性和经济性的条件下缩短网络时延。     

具有相同机组的电力系统经济调度参数扰动新方法

以一个具有3个相同机组的小型电力系统为例介绍了在Lagrangian松弛框架内,求解具有相同机组的电力系统会产生对偶解严重偏离系统最优可行解的震荡现象,这为由对偶解构造可行解制造了障碍。为了克服这种震荡,本文提出了一种参数扰动新方法。