离散粒子群与内点法结合的电力系统无功优化

基于离散粒子群优化算法与内点法,提出了一种新颖的混合策略来求解电力系统无功优化问题：不考虑无功优化中的离散约束,采用内点法求解得到初始解;根据优化变量的不同性质将无功优化问题分解为离散优化和连续优化2个子问题,并采用离散粒子群优化算法和内点法交替求解,使两者的优化结果互为基础、相互利用,从而保证了混合策略的整体寻优效率。以IEEE30和IEEE118节点作为试验系统,与常规的离散优化算法做比较,验证了该算法的正确性和有效性。     

离散粒子群与内点法结合的电力系统无功优化

基于离散粒子群优化算法与内点法,提出了一种新颖的混合策略来求解电力系统无功优化问题:不考虑无功优化中的离散约束,采用内点法求解得到初始解;根据优化变量的不同性质将无功优化问题分解为离散优化和连续优化2个子问题,并采用离散粒子群优化算法和内点法交替求解,使两者的优化结果互为基础、相互利用,从而保证了混合策略的整体寻优效率,以IEEE30和IEEE118节点作为试验系统,与常规的离散优化算法做比较,验证了该算法的正确性和有效性.     

基于蚁群算法和内点法的无功优化混合策略

基于蚁群优化算法与内点法,提出了一种新颖的混合策略来求解电力系统无功优化问题:不考虑无功优化中的离散约束,采用内点法求解得到初始解;根据优化变量的不同性质将无功优化问题分解为离散优化和连续优化2个子问题,并采用蚁群优化算法和内点法交替求解,使两者的优化结果互为基础、相互利用,从而保证了混合策略的整体寻优效率.最后以IEEE30和IEEE 118节点作为试验系统,与常规的离散优化算法做比较,验证了该算法的正确性和有效性.     

基于蚁群算法和内点法的无功优化混合策略

基于蚁群优化算法与内点法,提出了一种新颖的混合策略来求解电力系统无功优化问题：不考虑无功优化中的离散约束,采用内点法求解得到初始解;根据优化变量的不同性质将无功优化问题分解为离散优化和连续优化2个子问题,并采用蚁群优化算法和内点法交替求解,使两者的优化结果互为基础、相互利用,从而保证了混合策略的整体寻优效率。最后以IEEE 30和IEEE 118节点作为试验系统,与常规的离散优化算法做比较,验证了该算法的正确性和有效性。     

基于粒子群算法与内点算法的无功优化研究

提出了基于改进粒子群算法和预测-校正内点法的解耦无功优化算法。通过引入时代因子和邻近变异策略,同时采用分段处理方法对粒子群算法进行改进。运用预测-校正算法替代原-对偶内点,使得在内点法寻优过程中的迭代步长加大,同时避免寻优过程中振荡的出现。将改进粒子群算法和预测-校正内点算法分别用于无功优化的离散优化和连续优化子问题。将所提出的方法应用于IEEE30节点和IEEE118节点的系统。算例表明：与采用传统粒子群算法和原-对偶内点算法的混合无功优化相比,提出的方法在计算速度和优化效果方面都具有明显的优势。     

基于粒子群算法与内点算法的无功优化研究

提出了基于改进粒子群算法和预测-校正内点法的解耦无功优化算法.通过引入时代因子和邻近变异策略,同时采用分段处理方法对粒子群算法进行改进.运用预测-校正算法替代原-对偶内点,使得在内点法寻优过程中的迭代步长加大,同时避免寻优过程中振荡的出现.将改进粒子群算法和预测-校正内点算法分别用于无功优化的离散优化和连续优化子问题.将所提出的方法应用于IEEE30节点和IEEE118节点的系统.算例表明:与采用传统粒子群算法和原-对偶内点算法的混合无功优化相比,提出的方法在计算速度和优化效果方面都具有明显的优势.     

基于遗传算法和内点法的无功优化混合策略

基于遗传算法与内点法，文中提出了一种新颖的混合策略来求解无功优化问题：不考虑无功优化中的离散约束，采用内点法求解得到初始解；根据优化变量的不同性质，将原无功优化问题分解为离散优化和连续优化2个子问题，并采用遗传算法和内点法交替求解。在遗传迭代的不同阶段，针对种群个体的不同特点，分别对遗传算法和内点法的具体实施方案进行了动态调整，使两者的优化结果互为基础、相互利用，保证了混合策略的整体寻优效率。IEEE30和IEEE118节点系统的仿真计算结果表明：与其他混合算法相比，该混合策略在计算速度和优化效果方面都具有明显的优势。     

改进粒子群算法在电网无功优化中的应用

粒子群算法作为一种随机搜索算法,适合解决电网无功优化问题。考虑到粒子群算法收敛速度过快,容易进入局部收敛,导致收敛精度不高,研究了粒子群算法的改进措施。建立了一个全面考虑实际约束条件和无功调节手段的无功优化数学模型,提出了采用改进粒子群算法求解电网无功优化问题的方法,以确定无功优化的最优方案。以IEEE14节点系统进行仿真分析,对3种不同方案进行了对比,结果表明所用方法寻优质量高,不仅节点电压满足系统运行要求,而且系统网损也有一定程度的降低,采用该改进粒子群算法进行电网无功优化行之有效。     

基于动态多种群粒子群算法的低压配电网电压无功优化

粒子群算法已在配电网无功优化领域中得到广泛应用,而基本粒子群算法在求解多约束条件的低压配电网电压无功优化问题时耗时过长。为解决这一问题,提出了利用动态多种群粒子群算法对低压配电网进行电压无功优化方案。动态多种群粒子群算法通过轮盘赌将粒子按照各节点电压合格、各节点无功补偿容量不超过预设值和系统总无功不过补偿这3个约束条件进行动态分组,粒子根据改进的粒子速度位置更新公式飞行搜寻,最后获得满足以上约束条件的电压无功优化问题最优解。本文提出的电压无功优化方案将分散并联电容器组与配电变压器调压相结合,与集中补偿无功方式相比,节点电压偏移程度更小、电网损耗更低。本文应用的约束优化粒子算法与基本粒子群算法相比,运行速度大幅提高,计算结果较为优化。     

基于分布式协同粒子群优化算法的电力系统无功优化

该文提出一种新颖的用于求解无功优化问题的分布式协同粒子群优化算法.考虑到大规模电力系统集中优化难度较大,采用分层控制中的分解-协调思想将大系统分解成若干个独立的子系统,有效地降低求解问题的复杂度,并采用混合策略在各子系统问进行协同进化.此外,子系统的无功优化采用了一种改进的粒子群优化算法,考虑了更多粒子的信息,能有效地提高算法的收敛精度和计算效率.对4个不同大小规模的系统进行的仿真计算结果表明该文提出的方法能够获得高质量的解,并且计算时间短,效率高,适合求解大规模电力系统的无功优化问题.     

A hybrid genetic algorithm-interior point method for optimal reactive power flow

By integrating a genetic algorithm (GA) with a nonlinear interior point method (IPM), a novel hybrid method for the optimal reactive power flow (ORPF) problem is proposed in this paper. The proposed method can be mainly divided into two parts. The first part is to solve the ORPF with the IPM by relaxing the discrete variables. The second part is to decompose the original ORPF into two sub-problems: continuous optimization and discrete optimization. The GA is used to solve the discrete optimization with the continuous variables being fixed, whereas the IPM solves the continuous optimization with the discrete variables being constant. The optimal solution can be obtained by solving the two sub-problems alternately. A dynamic adjustment strategy is also proposed to make the GA and the IPM to complement each other and to enhance the efficiency of the hybrid proposed method. Numerical simulations on the IEEE 30-bus, IEEE 118-bus and Chongqing 161-bus test systems illustrate that the proposed hybrid method is efficient for the ORPF problem.     

电力系统无功优化的LRS-PSO算法

提出一种应用局部随机搜索粒子群优化（LRS—PSO）算法求解电力系统无功优化的新方法。使用概率调用策略调用局部随机搜索（LRS）算子。给出了适合无功优化问题的LRS算子的具体实现以及应用LRS—PSO算法求解电力系统无功优化的步骤。对IEEE30节点测试系统进行了无功优化计算,并与标准遗传算法（SGA）、粒子群优化（PSO）算法的测试结果进行了比较。仿真结果表明,与SGA、PSO算法相比,应用LRS—PSO算法求解无功优化问题具有质量更高的解,收敛特性更好。     

基于内点法与改进遗传法的无功规划优化混合算法

建立了利用调节发电机端电压、可带载调压变压器分接头与静止电容器组的补偿容量来获得系统年综合费用最小的无功规划优化数学模型。将此问题分成连续优化和离散优化两个子问题,采用非线性内点法和改进遗传算法交替求解的混合算法。在迭代的不同阶段,分别对内点法和改进遗传算法进行收敛条件改进,使两者的优化结果互为基础、相互利用,保证了混合算法的整体寻优效率。118节点系统的无功优化计算表明,所提算法可有效提高单一算法的收敛性能和运算速度。     

Decomposition-coordination interior point method and its application to multi-area optimal reactive power flow

A decomposition-coordination interior point method (DIPM) is presented and applied to the multi-area optimal reactive power flow (ORPF) problem in this paper. In the method, the area distributed ORPF problem is first formed by introducing duplicated border variables. Then the nonlinear primal dual interior point method (IPM) is directly applied to the distributed ORPF problem in which a Newton system with border-matrix-blocks is formulated. Finally the overall ORPF problem is solved in decomposition iterations with the Newton system being decoupled. The proposed DIPM inherits the good performance of the traditional IPM with a feature appropriate for distributed calculations among multiple areas. It can be easily extended to other distributed optimization problems of power systems. Numeric results of five IEEE Test Systems are demonstrated and comparisons are made with those obtained using the traditional auxiliary problem principle (APP) method. The results show that the DIPM for the multi-area OPRF problem requires less iterations and CPU time, has better stability in convergence, and reaches better optimality compared to the traditional auxiliary problem principle method.     

Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization

In this paper, a Biogeography Based Optimization (BBO) technique is introduced to solve multi-constrained optimal reactive power flow (ORPF) problem in power system. ORPF is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. The feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over other well established population based optimization techniques like conventional particle swarm optimization (PSO), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA) and interior point based OPF (IP-OPF).     

PCPDIPM based optimal reactive power flow model with discrete variables

This paper presents a new approach to deal with the optimal reactive power flow (ORPF) problem with the discrete control variables. First, a quadratic ORPF model based on augmented rectangular coordinates is established by treatment with the TLC branch; and then quadratic penalty functions are incorporated into the proposed model to handle the discrete control variables; at last, the predictor corrector primal dual interior point method (PCPDIPM) is used to implement the optimization.In the PCPDIPM based ORPF solution, the quadratic discretization formulation results in the constant Hessians that all have elements of 1, or −1, or the penalty factor, and mostly being zero, thereby accelerating the entire optimal process significantly. Experimental results are provided comparing the performance of the proposed discretization approach with that of the conventional one.     

考虑发电机安全运行极限的非固定分段无功优化模型及其算法

针对目前无功优化中没有根据不同发电机运行区域建立相应的无功辅助费用的问题，考虑了发电机安全运行极限约束，按照无功输出能力的不同，把发电机运行域分为了4个区，并给出了各个区域的发电机无功辅助费用计算函数。建立了以系统有功网损费用与发电机无功辅助费用之和最小为目标函数的无功优化模型，其对应的优化问题是一个具有非固定分段特点的非线性混合整数规划问题。文中提出了2种优化算法来求解该优化问题：结合启发式规则的混合整数规划内点法HEUIPM，其计算速度虽快，但为局部优化算法，并且在某些情况下存在不收敛的可能性；基于非线性内点法和免疫遗传算法所提出的启发式混合随机优化算法IPMIGA，该算法是全局优化的, 没有收敛性问题, 但其计算速度比HEUIPM慢很多。所以文中将2种方法结合起来，在程序设计时, 先用HEUIPM算法, 遇到不收敛时自动转到IPMIGA算法。对节点数从14到171的5个测试系统进行了仿真计算，结果验证了所提算法的有效性。     

Determination of load shedding to provide voltage stability

A computationally simple algorithm is developed for studying the load shedding problem in emergencies where an ac power flow solution cannot be found for the stressed system. The proposed algorithm is divided into two sub-problems: restoring solvability sub-problem and improving voltage stability margin (VSM) sub-problem. Linear optimization (LP)-based optimal power flow (OPF) is applied to solve each sub-problem. In restoring solvability sub-problem, rather than taking restoring power flow solvability as direct objective function, the objective function of maximization of voltage magnitudes of weak buses is employed. In VSM sub-problem, the traditional load shedding objective is extended to incorporate both technical and economic effects of load shedding and the linearized VSM constraint was added into the LP-based OPF. Case studies with a real 682 bus system are presented. The simulation results show that the proposed load shedding algorithm is effective, fast in finding the load shedding scheme to solve the problem of restoring solvability and improving VSM.