最优潮流改进简化梯度法的研究及应用

研究和改进电力系统最优潮流问题的简化梯度法 ,并将改进简化梯度法应用于求解无功优化问题。在算法上结合电力系统的PQ解耦特性 ,采用简化梯度法和共轭梯度法的组合算法解算优化潮流问题 ,改进了传统意义上的简化梯度法和共轭梯度法 ,进一步提高了计算速度 ,获得了良好的收敛性 ,尤其是在接近最优点处。无功优化问题的计算实例证明了本文算法是有效的、可靠的。     

电力市场环境下的最优潮流

随着电力市场在全国范围内逐步推广，电力系统最优潮流分析在解决电力市场环境下的新问题发挥着越来越重要的作用。因此从电力市场入手，论述了最优潮流在电力市场环境下的意义，着重介绍了最优潮流模型、算法及在电力市场中的应用。     

基于微粒群优化算法的最优电力系统稳定器设计

传统电力系统稳定器的性能受其参数影响很大,为提高电力系统机电暂态模型的阻尼,文中提出了一种优化电力系统稳定器参数的新方法。该方法以两个特征值基目标函数为基础,采用改进的微粒群优化技术对电力系统稳定器进行参数优化。特征值分析和非线性仿真结果表明,经过参数优化的电力系统稳定器能有效抑制本地和区域间振荡,提高系统的鲁棒性。     

考虑最优潮流约束的机组组合并行解法

针对传统机组组合模型的不足,提出一种考虑最优潮流约束的机组组合模型并给出了其并行化解法.该法借助于扩展拉格朗日和变量复制技术,将原问题转换为其对偶问题,并利用附加问题原理将对偶问题分解为动态规划和最优潮流子问题.对于最优潮流子问题,采用预测校正内点法求解,同时在求解过程中,采用并行处理技术.IEEE118节点及IEEE300节点仿真结果表明,该方法性能稳定,收敛性好,并行处理后计算速度显著提高.     

基于粒子群优化算法和动态调整罚函数的最优潮流计算

在电力市场环境下,诸多问题(例如实时电价,网络阻塞等)都需要最优潮流作为理想的工具.本文应用了一种简单有效、且收敛性很好的演化计算算法--粒子群优化算法(PSO)进行最优潮流问题的求解.在求解过程中,根据约束条件的越界量大小,动态的调节其罚函数,避免其收敛到局部最小点.应用此算法对IEEE 30 节点系统进行最优潮流计算,并且与线性规划和遗传算法进行了比较,结果表明该算法能够更好的获得全局最优解,具有实用意义.     

基于粒子群优化算法与混合罚函数法的最优潮流计算

电力系统最优潮流的求解问题一直是电力市场研究的重点。该文介绍了一种新的演化优化算法,即粒子群算法(PSO)。该算法具有简单易实现,可调参数少的优点。笔者将其用于最优潮流的求解,结合混合罚函数来限制最优潮流的约束条件,使粒子群算法的寻优速度加快,迭代次数减少。通过在IEEE9节点和IEEE30节点上的仿真计算表明,该算法在优迭代速度和收敛精度上都取得了较好的效果。     

基于改进粒子群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,提出一种基于(非劣最优)Pareto解集的改进粒子群算法AL iPSO。用最优值评估选取法求取粒子和全局最优位置,解决目标函数间可能存在的冲突。并将关联度自适应学习应用于多目标优化,提出适合Pareto解特点的适应度设计和随机惯性权策略,克服PSO算法容易早熟而陷入局部最优解的缺点。通过对IEEE 6、IEEE 14节点系统多目标最优潮流计算,验证了该算法的有效性。     

基于改进粒子群优化算法的电力系统有功最优潮流

提出了一种基于改进粒子群优化算法的有功最优潮流模型及求解方法,采用了自适应罚函数法处理最优潮流问题的各种约束条件。通过对IEEE-30节点系统的仿真计算,并且与遗传算法进行比较,验证了提出的模型和方法的有效性。     

含大型风电场的电力系统多时段动态优化潮流

大型风电场的并网对电力系统的优化运行提出了新的挑战。该文对含风电场的电力系统优化潮流问题进行了研究，建立了多时段动态优化潮流模型，为了考虑风速随机变化的特点，提出了分时段策略，将风机在每个时段输出功率的期望值用于优化潮流的计算。文中对现有含风电场的潮流计算方法进行了分析，推导了异步风力发电机的无功－电压特性方程，在此基础上提出了含风电场的潮流计算新方法，并将其应用于提出的动态优化潮流模型中。算例表明该方法是有效的，具有一定的实用性。     

An Approach to Solve Transient Stability-Constrained Optimal Power Flow Problem Using Support Vector Machines

The Transient Stability-Constrained Optimal Power Flow (TSC-OPF) is a challenging optimization problem, and is the subject of several recent researches. This paper proposes a novel approach to solve TSC-OPF. In the proposed framework, Support Vector Machines (SVMs) are used to classify whether an operating condition satisfies predefined transient contingencies. A novel classification strategy is proposed to ensure the optimal solution satisfies all considered contingencies with certain security margin. Besides, the weight coefficients of the SVM are used as sensitivity measures in order to help optimization solver find solutions more effectively. The proposed approach is demonstrated for the New England system and the IEEE 300 bus system.     

一种求解最优潮流问题的改进粒子群优化算法

提出了一种新的基于可行保留策略和变异算子的改进粒子群优化算法来求解最优潮流问题。可行保留策略将最优潮流问题的目标函数和约束条件分开处理,使得只有可行的解才能指导粒子飞行,避免了粒子在不可行域中的无效搜索,提高了算法的搜索效率;变异算子以预定的概率选择变异个体,对粒子的位置进行高斯变异操作,使得粒子可以有效避免陷入局部最优,增强了算法的全局搜索能力。通过 IEEE 30节点系统对该算法进行了测试,结果表明,对于复杂的最优潮流问题,该算法优于进化规划算法和常规的粒子群优化算法。     

Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm

A Particle Swarm Optimization algorithm for finding the optimal location and sizing of Distributed Generation and Distribution STATicCOMpensator (DSTATCOM) with the aim of reducing the total power loss along with voltage profile improvement of Radial Distribution System is proposed in this paper. The new-fangled formulation projected is inspired by the idea that the optimum placement of the DG and DSTATCOM can facilitate in minimization of the line loss and voltage dips in Radial Distribution Systems. A complete performance analysis is carried out on 12, 34 and 69 bus radial distribution test systems and each test system has five different cases. The results analyzed using Loss Sensitivity Factor shows the optimal placement and sizing of DG and DSTATCOM in Radial Distribution System effectively improves the voltage profile and reduces the total power losses of the system.     

Optimal siting and sizing of UPFC using evolutionary algorithms

This paper addresses an application of evolutionary algorithms to optimal siting and sizing of UPFC which are formulated as single and multiobjective optimization problems. The decision variables such as optimal location, both line and distance of UPFC from the sending end, control parameters of UPFC and system reactive power reserves are considered in the optimization process. Minimization of total costs including installation cost of UPFC and enhancement of the loadability limit are considered as objectives. To reduce the complexity in modeling and the number of variables and constraints, transformer model of UPFC is used for simulation purposes. CMAES and NSGA-II algorithms are used for optimal siting and sizing of UPFC on IEEE 14 and 30 bus test systems. NSGA-II algorithm is tested on IEEE 118 bus system to prove the versatility of the algorithm when applied to large systems. To validate the results of transformer model of UPFC for optimal siting and sizing, results using other models are considered. In single objective optimization problem, CMAES algorithm with transformer model yields better results when compared to other UPFC models. The statistical results conducted on 20 independent trials of CMAES algorithm authenticate the results obtained. For validating the results of NSGA-II with transformer model for optimal siting and sizing of UPFC, the reference Pareto front generated using multiple run CMAES algorithm by minimizing weighted objective is considered. In multiobjective optimization problem, the similarity between the generated Pareto front and the reference Pareto front validates the results obtained.     

改进粒子群优化算法的电力系统最优潮流计算

标准的粒子群优化(PSO)算法一般不能兼顾收敛速度、全局探索能力和局部精细搜索能力,因此,提出了改进粒子群算法以解决电力系统的最优潮流计算问题,同时指出今后粒子群算法的研究方向.     

高压电网潮流优化的负荷矩新算法

本文以近年来国内外广泛应用的最优化技术为基础，提出了一种基于负荷矩的电力网规划设计的数学模型，利用微型计算机对负荷矩之和的计算来寻找线损最小、运行费用最低、投资最省的最优电网结构方案。在常德德山电网规划的实例中证明该方法是实用、理想的。     

Optimal location and sizing of capacitor placement in radial distribution system using Bacterial Foraging Optimization Algorithm

This study introduces a new long term scheduling for optimal allocation of capacitor bank in radial distribution system with the objective of minimizing power loss of the system subjected to equality and in equality constraints. In the proposed method the new integrated approach of Loss Sensitivity Factor (LSF) and Voltage Stability Index (VSI) are implemented to determine the optimal location for installation of capacitor banks. Bacterial Foraging optimization algorithm (BFOA) is proposed to find the optimal size of the capacitor banks. The proposed method is applied on IEEE 34-bus and 85-bus radial distribution system with all possible load changes. The load is varied from light load (50%) to peak load (160%) with a step size of 1% and optimization procedure is followed to entire period. The generalized equation obtained from the curve fitting technique is very much helpful for the Distribution Network Operators (DNOs) to adjust the capacitor size according to the load changes. The simulated results demonstrate well the performance and effectiveness of the proposed method.     

Optimal voltage control in distribution network in the presence of DGs

Nowadays, integration of new devices like Distributed Generation, small energy storage and smart meter, to distribution networks introduced new challenges that require more sophisticated control strategies. This paper proposes a new technique called Optimal Coordinated Voltage Control (OCVC) to solve a multi-objective optimization problem with the objective to minimize the voltage error at pilot buses, the reactive power deviation and the voltage error at the generators. OCVC uses Pareto optimization to find the optimal values of voltage of the generators and OLTC. It proposes an optimal participation of reactive power of all devices available in the network.OCVC is compared with the classical method of Coordinated Voltage Control and is tested on the IEEE 13 and 34 Node test feeders with unbalanced load. Some disturbances are investigated and the results show the effectiveness of the proposed technique.     

基于非线性互补方法的现代内点最优潮流算法

提出了基于非线性互补方法的最优潮流算法。引入非线性互补函数,将内点法中KKT条件的互补松弛条件约束转化为等式约束,并采用牛顿方法求解。该方法不必保证互补松弛变量为正数,可以从任意起始点出发,具有良好的收敛性。在确定最优步长的过程中,采用了新的效益函数,节省了大量的计算时间,并有效处理了算法在收敛过程中产生的振荡问题。数值计算结果表明,提出的算法具有很好的收敛性和计算效率,对于大规模电力系统具有很好的应用前景。     

Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm

This paper presents a novel approach to optimal placement of Phasor Measurement Units (PMUs) for state estimation. At first, an optimal measurement set is determined to achieve full network observability during normal conditions, i.e. no PMU failure or transmission line outage. Then, in order to consider contingency conditions, the derived scheme in normal conditions is modified to maintain network observability after any PMU loss or a single transmission line outage. Observability analysis is carried out using topological observability rules. A new rule is added that can decrease the number of required PMUs for complete system observability. A modified Binary Particle Swarm Optimization (BPSO) algorithm is used as an optimization tool to obtain the minimal number of PMUs and their corresponding locations while satisfying associated constraint. Numerical results on different IEEE test systems are presented to demonstrate the effectiveness of the proposed approach.     

Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm

Abstract—This article presents a hybrid algorithm based on the particle swarm optimization and gravitational search algorithms for solving optimal power flow in power systems. The proposed optimization technique takes advantages of both particle swarm optimization and gravitational search algorithms by combining the ability for social thinking in particle swarm optimization with the local search capability of the gravitational search algorithm. Performance of this approach for the optimal power flow problem is studied and evaluated on standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect fuel cost minimization, voltage profile improvement, voltage stability enhancement, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. Simulation results show that the hybrid particle swarm optimization–gravitational search algorithm provides an effective and robust high-quality solution of the optimal power flow problem.