Cost minimization in allocation of reactive power sources

A LP-based method for the optimization of the investment and operational costs of the reactive power generation in a large electric power network is presented. The optimal solution is based on the application of decomposition and linear programming approaches. The global problem is decomposed into investment and operation subproblems using the Benders decomposition method. The revised simplex method is proposed for the solution of the investment subproblem. However, the Dantzig-Wolfe decomposition method is used to solve the operation subproblem. The formulation of the operation subproblem has been discussed in our previous studies. The method has been generalized to represent and solve multiple load levels and contingencies simultaneously in the operation subproblem. The results of applying this approach to the IEEE 30-bus system, a 60-bus system and a 180-bus system verify its robustness in solving the operation subproblem and the capability to converge quickly.     

Cross decomposition for multi-area optimal reactive power planning

This paper presents a novel decomposition based algorithm to solve the reactive power planning problem in large scale multi-area power systems. Extensive studies on the application of the Benders decomposition method (BDM) have been conducted to solve this complex problem. Nevertheless, these applications are based on the difficult evaluation of the plane cuts from the solution of the master problem. In this paper, the authors introduce the possibility of solving reactive power planning based on an iterative optimization process between the BDM subproblem and the subproblem of a Lagrangean relaxation decomposition method (LRDM). The overall process is referred to as a cross decomposition algorithm (CDA). The merits of the proposed approach are shown by the evaluation of plane cuts based on the solution of easy-to-solve subproblems. Moreover, the proposed approach is extremely efficient in multi-area (zone decomposition) power systems. Test results are demonstrated for the 3-area IEEE 30-bus and the 8-area 180-bus systems     

Multicriteria approach to optimal operation of power systems

The problem of the determination of preventive control in a steady state, intended to provide the secure operation of a power system, is considered in this paper. The problem is solved using the concept of multicriteria optimization, by minimizing the system generation cost, modified with penalty functions originating in inequality constraints due to operating limits. The penalty functions represent a security index, the value of which depends on the particular outage.A method for a preventive security control is derived, a new numerical procedure for the solution of the problem based on the ?-coupling method applied to steady state problems is presented, and some results of a study of the application of the proposed method on a 31-bus, 40-branch real power system are given.     

Optimal power system VAr planning

A technique is presented for planning the future reactive power requirements of a power system. The problem is formulated as a mathematical optimization problem based on a linearized power system model. An efficient dual simplex linear programming technique coupled with relaxation and contingency analysis is used for solution. The approach reduces the dimensionality of the problem significantly, thereby allowing for more rapid calculations and more case studies to be performed. The effectiveness of the scheme is demonstrated using the IEEE 30-bus and IEEE 188-bus systems     

Analysis of confidence bounds in power system state estimation with uncertainty in both measurements and parameters

A method for uncertainty analysis in power system state estimation is proposed. The uncertainty is expressed in both measurements and network parameters. Uncertainties in both measurements and parameters are known and bounded. The problem is formulated as a constrained non-linear optimization problem. To find the tightest possible upper and lower bounds of any state variable, the problem is solved by sequential quadratic programming (SQP) techniques. When applied to the 6-bus, IEEE 14-bus and IEEE 30-bus networks, the proposed method shows reliability and accuracy in estimating the uncertainty bounds in power system state estimation. In addition, assessment of time performance is carried out to demonstrate that the proposed method is suitable for either an on-line or an off-line application.     

Considering system non-linearity in transmission pricing

In this paper a new approach for transmission pricing is presented. The contribution of a contract on power flow of a transmission line is used as extent-of-use criterion for transmission pricing. In order to determine the contribution of each contract on power flow of each transmission line, first the contribution of each contract on each voltage angle is determined, which is called voltage angle decomposition. To this end, DC power flow is used to compute a primary solution for voltage angle decomposition. To consider the impacts of system non-linearity on voltage angle decomposition, a method is presented to determine the share of different terms of sine argument in sine value. Then the primary solution is corrected in different iterations of decoupled Newton–Raphson power flow using the presented sharing method. The presented approach is applied to a 4-bus test system and IEEE 30-bus test system and the results are analyzed.     

基于一种不可行内点法的电力系统无功优化

在应用内点法进行线性规划时,尚不能保证它的全面收敛性。提出了一种新的算法来求解无功线性优化问题。利用潮流雅可比矩阵直接变换求取灵敏度系数,建立无功优化线性规划模型,同时采用一种不可行内点算法来直接求解该问题。IEEE 14节点、30节点、57节点系统的计算结果表明,该算法能有效求解无功优化线性规划问题,同时在初始点的选择上不要求从内点启动,迭代收敛次数稳定,对计算系统的规模不敏感。     

含分布式发电的配电网多目标无功优化策略研究

为以最省的无功设备投资、最大限度地保证系统经济运行,增加解决问题的灵活性,研究了含分布式发电(DG)的配电网多目标无功优化策略,即构建含DG的配电网多目标无功优化模型。运用自适应多目标粒子群(AMOPSO)算法求解此问题,一改传统将多目标问题转化为单目标求解的做法。为验证所提策略,以含DG的IEEE 33节点系统为例,将AMOPSO应用于以最小化系统有功网损和最小化无功补偿设备容量（投资）为目标函数的多目标无功优化问题。仿真表明,采用该策略为决策者提供了可供选择的多样性解;优化结果验证了所建多目标模型的优越性和所提算法应用的可行性和有效性。     

基于鲸鱼优化算法的无功优化调度

针对目前电力系统中的无功优化问题尚缺乏一种能兼顾求解的高效性与全局搜索最优性的方法,本文将一种新的启发式算法--鲸鱼优化算法(WOA)运用到电网无功优化调度中,以系统有功功率损耗最低为目标函数,通过引入惩罚函数建立无功优化模型,对IEEE-14节点系统与IEEE-30节点系统进行仿真,并利用单因素方差分析法(One-way ANOVA)将所得结果与之前的粒子群优化算法(PSO)及引入加速度系数的时变粒子群优化(PSO-TVAC)进行比较,研究表明WOA算法在迭代次数、搜索能力及收敛问题上的潜力,并证明了在解决电力系统无功优化问题上的鲁棒性和有效性,同时也为解决非线性约束问题提供了新途径。     

基于Benders分解的发电机无功备用优化方法

电压稳定裕度与系统无功备用密切相关,因此,提出一种无功源的无功备用优化方法。引入发电机参与因子来衡量发电机无功备用的重要程度,建立以系统无功备用最大化为目标的优化模型,并应用Benders分解将模型分解为发电机主问题和电容器子问题。IEEE 39节点系统算例验证了所提优化方法的有效性,优化方案大幅提高了系统电压稳定裕度。     

基于免疫禁忌混合算法的多目标最优潮流计算

针对电力系统多目标最优潮流计算问题,提出了一种基于免疫禁忌混合算法的多目标模糊优化潮流计算的新方法。该方法运用模糊集理论来构造评价函数,实现了多目标优化问题向单目标优化问题的转化,兼顾了各子目标,保证了较高的整体优化水平;采用求解精度高、使用灵活的免疫禁忌混合算法来进行单目标寻优,准确地获取了符合实际的全局最优解。通过对IEEE 30节点系统进行多次多目标最优潮流仿真计算,并将计算结果分别与单目标最优潮流计算及采用粒子群算法、免疫算法等其它算法的结果进行比较和分析,验证了该方法是解决电力系统多目标最优潮流问题的一种有效方法。     

基于改进PSO算法的电力系统无功优化

粒子群优化PSO(Particle Swarm Optimization)算法是一种简便易行、收敛快速的演化计算方法,但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和变异算子,提出了一种新的改进粒子群优化MPSO(Modified Particle Swarm Optimization)算法,并将其应用于电力系统无功优化,建立了相应的优化模型。对IEEE-14节点系统及某地区70节点实际电力系统进行了仿真计算,并与PSO算法作了比较,结果表明MPSO优化算法能有效地应用于电力系统无功优化．其全局收敛性能及收敛精度均较PSO算法有了一定程度的提高。     

Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization

The optimization is an important role in the wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. Using adaptive real coded biogeography-based optimization (ARCBBO), we present the optimization of various objective functions of an optimal power flow (OPF) problem in a power system. We aimed to determine the optimal settings of control variables for an OPF problem. The proposed approach was tested on a standard IEEE 30-bus system and an IEEE 57-bus system with different objective functions. Simulation results reveal that the proposed ARCBBO approach is effective, robust and more accurate than current methods of power flow optimization in literature.     

Maximum loadability of power systems using hybrid particle swarm optimization

The problem of voltage stability is one of the main concerns in the operation of power systems. There are different approaches to estimate the voltage stability of the system. One of these approaches is to find the margin from the current operating point to the maximum loading point of the system. Finding this maximum loading point can be formulated as an optimization problem. This paper utilizes the newly developed evolutionary particle swarm optimization in solving this optimization problem. Details of the implementation of the proposed method to two test systems (Ward-Hale 6-bus) and (IEEE 14-bus) are presented. The results are compared to those obtained by the widely used continuation power flow technique. Good agreement has been obtained proving the validity and applicability of the proposed method.     

基于细菌群体趋药性的电力系统无功优化

提出了一种基于细菌群体趋药性的电力系统无功优化算法。细菌群体趋药性引入了群体信息交互策略,使得单个细菌在利用自身信息随机移动的同时,通过种群的信息交互,有效地改善了个体移动时的随机性和盲目性,加强了细菌趋于最优的移动策略。建立了基于细菌群体趋药性的无功优化数学模型,并对标准IEEE-6和IEEE-30节点测试系统进行了无功优化计算,通过结果分析表明,细菌优化算法在解决电力系统无功优化问题上,具有很好的应用前景。     

应用多波前法快速求解最优协调电压控制问题

将长期电压稳定场景下的协调电压控制问题用带有微分-代数方程约束的最优控制模型来描述,借助Radau排列技术将这个动态优化问题转化为大型非线性规划模型,并采用非线性原-对偶内点法求解.重点探讨如何应用多波前方法结合近似最小度排序提高求解稀疏线性修正方程的效率.以IEEE 17机162节点系统和新英格兰10机39节点系统作为算例,通过与近似最小度法和反向Cuthill-McKee法排序下三角分解结果进行对比,证实了所述方法在计算速度上的优越性.     

Differential search algorithm for solving multi-objective optimal power flow problem

In this paper, a simple and efficient nature inspired search method based on differential search algorithm (DSA) has been presented and used for optimal power flow (OPF) problem in power systems. By using the proposed DSA method, the power system parameters such as real power generations, bus voltages, load tap changer ratios and shunt capacitance values are optimized for the certain objective functions. Different types of single-objective and multi-objective functions on IEEE 9-bus, IEEE 30-bus and IEEE 57-bus power systems are used to test and verify the efficiency of the proposed DSA method. By comparing with several optimization methods, the results obtained by using the proposed DSA method are presented in detail. The results achieved in this work illustrate that the DSA method can successfully be used to solve the non-linear and non-convex problems related to power systems.     

随机风电接入的电力系统动态经济调度多场景协同优化

针对随机风电接入的电力系统动态经济调度问题,采用场景法应对随机风电接入带来的不确定性,并以发电总成本最小为优化目标,结合多学科协同优化算法的核心思想建立基于多场景解耦的电力系统动态经济调度协同优化模型。引入动态松弛算法求解模型的系统级优化问题,有效克服传统多学科协同优化算法的不足;采用网格计算工具并行求解由多场景构建的子学科优化问题,大幅提高求解规模和计算效率。含风电的IEEE 39节点系统仿真结果表明,所提模型是可行有效的,并且优化效果要优于基于GAMS-BARON求解器的传统场景法。     

Selection of TCSC location for secured optimal power flow under normal and network contingencies

This paper focuses on the optimal power flow solution and enhancement of system performance without sacrificing the security of the system via optimal location and optimal sizing of Thyristor Controlled Series Compensator (TCSC) when the system is operating under normal and network contingency conditions. The paper presents a secured optimal power flow solution by integrating TCSC with the optimization model developed under normal and contingency cases. The optimization model developed in this paper is solved by using linear programming method. New indices called Thermal Capacity Index (TCI) and Contingency Capacity Index (CCI) are proposed for placing the TCSC at appropriate location under normal and network contingency conditions respectively. Once the location to install TCSC is identified, the optimal setting of TCSC is determined through the software code written in MATLAB. The proposed approach is carried out on 6-bus, IEEE 14-bus and IEEE 118-bus test systems and the simulation results are presented to validate the proposed method.     

Fuzzy reactive power optimization in hybrid power systems

Reactive power control, which is one of the important issues of power system studies, has encountered some intrinsic changes because of the presence of the hybrid AC/DC systems. The uncertainty in determination of some ill-defined variables and constraints underlines the application of fuzzy set as an uncertainty analysis tool. Herein a fuzzy objective function and some fuzzy constraints have been modeled for the purpose of reactive power optimization then this fuzzy model is dealt with as a linear programming problem to be solved. Contrary to the separate modeling of the conventional AC/DC optimization methods, this study attempts to attain the most optimal solution by the simultaneous employment of the total contributing factors of both AC and DC parts. In this way, the conventional issue of the coordinated control of firing angle and the transformer tap of the DC terminals is resolved, yet the method provides more flexibility to gain the most optimal condition since it uses more control factor for solving the optimization problem. The proposed method is performed on the modified IEEE 14 and 30-bus systems; and it is shown to have less computational burden and further minimized objective function than the conventional method.