含分布式电源的改进PSO算法配电网无功优化

在电网无功供电性能优化问题的研究中,针对包含分布式电源的配电网无功优化的特点,利用一种改进粒子群算法,对含分布式发电的配电网系统进行了无功优化的计算.考虑了电网损最小、节点电压和发电机无功出力的约束作为优化目标函数,采用粒子群算法,在其速度进化方程中引入了自适应惯性权重和收缩因子以提高,并运用遗传算法中的交叉技术,对PSO算法产生的粒子进行遗传交叉运算来改善全局搜索能力,并在迭代后期将其取消来提高计算速度,仿真结果对比表明,提出的优化算法能够有效地提高电网电压质量和减少功率损耗.     

考虑经济性和电压稳定性的含电子电力变压器的多目标最优潮流计算

为探究含电子电力变压器的电力系统最优潮流问题,在分析电子电力变压器简化模型、最优潮流的控制变量以及约束条件的基础上,建立了综合考虑经济因素和电压稳定性的含电子电力变压器的多目标最优潮流模型。模型中将减少发电成本和提高负荷裕度指标作为目标函数,考虑了电子电力变压器灵活的有功无功调节能力、有载调压变压器的电压调节能力、可调度负荷及可调无功电源的有功无功调节能力,提出使用基于遗传算法和内点算法的混合算法对最优潮流模型进行求解,算法的主要思想是以遗传算法为框架,对离散变量进行优化,在遗传算法的每一次迭代过程中,采用内点算法对每个体进行连续变量的优化和适应度评估。基于IEEE-14节点算例,分别进行了基于混合算法和基于内点法的最优潮流计算,计算结果验证了文章所提模型的合理性和算法的有效性。     

计及新能源接入的主动配电网多目标优化仿真北大核心

针对越来越多出力不确定的新能源及负荷大规模接入配电网,影响了主网运行的安全性与经济性。文章对风光出力进行了建模得到风光出力的概率密度函数,经离散变换得到各时段的出力期望值。建立了双层规划模型,上层规划以系统的网络损耗最小来确定分布式电源的安装位置;下层规划根据分布式电源的节点电压稳定性指数、投资效益指标建立的多目标优化数学模型来确定安装容量。采用改进的差分进化算法和量子粒子群算法对模型求解。最后在加入新能源的IEEE33节点系统上进行仿真,并分两阶段对所提模型进行分析,第一阶段分析所提模型对主网安全性的有效优化,第二阶段分析在考虑分时电价下此规划在经济效益方面的有效性。     

基于混合算法的配电网无功补偿协调控制方法研究

为了保证配电网的稳定运行,提出基于混合算法的配电网无功补偿协调控制方法。通过分析萤火虫算法与量子粒子群算法特点与实现关键,按照串联形式融合两种算法,架构出分为两个阶段的混合智能算法,并用群体替换算子做进一步优化,设定电容器购置费用、负载率以及总有功网损为目标函数,基于负荷率与目标函数间的层次关系,架构双层配电网无功补偿协调控制数学模型,将模型与混合算法结合,获取寻优结果,实现无功补偿协调控制。仿真实验表明,所提方法的收敛速度、降损效果以及节点电压质量均具有显著优越性。     

基于下垂特性的主动配电网PQ协调控制策略研究

本文在主动配电网背景下,针对大量间歇式分布式电源接入配电网导致的电压波动问题,从有功功率、无功功率协调控制角度,提出一种计算、控制、执行的分层控制策略,进行功率解耦控制。首先通过控制器采集的电压频率和储能系统的荷电状态的基础上确定下垂系数,再计算负荷功率的缺额并结合主动配电网的出力情况来测算储能系统输出的功率,最终采用Park变换获得输出信号。通过在建立的微型主动配电网模型上的仿真,验证了分布式电源输出有功、无功以及负荷电压的稳定性都得到一定优化,从而说明了该控制策略的有效性和可行性。     

分布式可再生能源发电并网的无功优化研究

提出了基于杂交粒子群优化算法的分布式可再生能源并网的无功优化算法,从网损和静态电压稳定裕度两个角度出发,构建了含分布式发电系统的配电网无功优化的数学模型.在美国PG&E 69节点配电系统上进行效验.结果表明,该算法收敛性好、精度高;分布式电源并网后能有效降低系统的有功网损,提高电压稳定性,对分布式电源并网运行具有一定的...     

基于改进粒子群算法的多目标分布式电源选址定容规划

通过分析分布式电源对配电网的影响,以有功功率损耗、电压质量及分布式电源总容量为优化目标,基于模糊理论建立了分布式电源在配电网中选址定容的多目标优化模型,并提出了一种改进粒子群算法进行求解。在算例仿真中,基于IEEE 14标准节点系统,采用MATLAB仿真工具对所提算法进行了测试,证实了所提算法全局搜索能力较强、收敛速度较快,并通过比较分析验证了该模型和算法的可行性及有效性。     

基于改进粒子群算法的电网系统无功电压控制北大核心

由于传统无功电压控制方法易出现局部最优问题,导致控制过程不稳定,无法有效调节负载突加和突卸的情况,提出基于模拟退火粒子群算法的无功电压控制方法。获取电网系统稳态运行时状态参数,根据无功出力点和节点电压两个状态变量构建目标函数,引入惩罚函数确定各变量约束条件;建立基本粒子群算法模型,得到粒子位置与速度更新公式;为避免陷入局部最优,利用模拟退火方式改进粒子群算法,结合适应度值判断粒子位置是否最优;输入电压控制变量极限值,设定迭代次数、运动速度等初始参数,反复更新粒子位置与速度,输出最佳控制值,实现无功电压的优化控制。仿真结果表明,所提算法迭代速度快,控制过程稳定,面对负载突加和突卸时,控制效果仍然较为理想。     

含大规模光伏并网的配电网电压调控策略研究

光伏发电的快速发展及其在配电网的大规模并网给配电网的运行带来了重大的影响。为保证大规模光伏并网后的配电网电压在规定的范围内,在分析光伏有功出力和无功调节特性的基础上,本文提出了一种配电网电压主动调控策略,根据系统负荷预测和光伏出力预测获取下一时段所需的无功功率调整量,以常规无功补偿装置为优先、光伏逆变器无功调整为辅,各光伏无功调整量的分配采取经济性最优的方式。建立含光伏并网的配电网电压仿真计算实例,通过与其他电压调控策略的对比分析验证了本文策略的有效性和优越性。     

基于混合算法的含分布式电源的配电网优化

考虑到分布式电源的选址与定容对配电网有着重要影响意义,针对分布式电源的接入对配电网系统能量损耗和各节点电压影响的问题,首先建立了以有功功率损耗和系统节点电压的目标函数优化模型,提出了充分整合引力搜索算法(GSA)的勘探能力和粒子群(PSO)的开采能力的混合算法(PSOG-SA),同时确定权重系数,最后采用IEEE-33标准节点配电网模型进行了仿真实验,通过和其他两种算法的比较,验证了配电网系统在该算法下的有效性和可靠性.算例分析表明,合理的DG接入能够一定程度上降低系统有功功率损耗,改善节点电压.     

A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network

Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacity can be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed.     

Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis

Placement of optimally sized distributed generator (DG) units at optimal locations in the radial distribution networks can play a major role in improving the system performance. The maximum economic and technical benefits can be extracted by minimizing various objectives including yearly economic loss which includes installation, operation and maintenance cost, power loss as well as voltage deviation throughout the buses. The present problem is analysed considering these multi-objective frameworks and presents the best compromise solution or Pareto-optimal solution. Several equality and inequality constraints are also considered for the multi-objective optimization problem. In this paper, a novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi-objective problem in order to avoid premature convergence. Performance of population based meta-heuristic techniques largely depends on the proper selections of control parameters. It is reported that wrong parameters selection may lead to premature convergence and even stagnation. The proposed technique uses logistic mapping to generate chaotic sequence for control parameters. The proposed algorithm is implemented on IEEE-33 and IEEE-69 bus radial distribution systems for verifying its effectiveness. A comparative analysis with other modern multi-objective algorithms like NSGA-II, SPEA2 and MOPSO is also presented in this work. It is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile.     

Study impact of various load models on DG placement and sizing using backtracking search algorithm

In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation.     

基于矩阵算法的主动配电网交直流混合网络供电恢复策略研究

考虑到直流输电技术的发展,直流配电应用于配电网中是未来配电网的研究重点。本文为解决主动配电网交直流混合网络的供电恢复问题,提出含直流配电线路的主动配电网供电恢复方案,当交流配电线路出现故障时,考虑直流侧配电线路网损、换流器以及分布式电源影响下的供电恢复方案;当直流配电线路出现故障时,考虑转换直流侧运行方式或让失电的直流负荷转入计划孤岛运行模式,以保证直流侧重要负荷的持续供电,分别提出交流侧和直流侧约束条件,并在约束条件下选取网络损耗、线路末端电压越限节点个数以及开关操作次数作为指标构建目标函数,依据矩阵算法对供电恢复过程的不同恢复方案进行大数据分析与处理,得出最优方案。通过改进的IEEE 123节点算例证明,提出的方案能够有效的解决主动配电网的供电恢复问题。     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles

Due to the intermittent characteristics of wind and solar distributed energy resources and moreover, uncertainties in the power demand, the conventional power-flow methods could not cope with the active distribution networks and microgrids. Using some statistical methods like Mont Carlo simulation is always a reliable solution. However, it is time-consuming and cannot be applied to the large power systems. In this paper, a novel is proposed for robust probabilistic power-flow in radial and meshed electric power systems including renewable energy resources. The ability of radial basis function artificial neural networks for nonlinear mapping is exploited with an acceptable level of accuracy, and even exact to solve nonlinear equation set of power-flow analysis. This ability improves the speed of the algorithm because unlike conventional methods, the proposed method does not require calculating partial derivatives and inverse Jacobian matrix. The proposed method includes all types of buses, i.e. PQ, PV and Slack buses. The probability density function and cumulative distribution function for some of power system variable are compared with the other probabilistic power-flow methods for different test systems and the results validate its authenticity, robustness, efficiency and accuracy.     

基于序列二次规划法的分布式电源优化配置研究

针对分布式电源配置对配电网的影响,提出一种带二阶项配网潮流约束的方法解决分布式电源优化配置问题,以实现分布式电源价值的最大化。从降损角度出发建立优化配置的数学模型,并用序列二次规划求解优化问题。在充分发挥序列二次规划法收敛性好的基础上,提高计算精度,并适用于各种复杂的配电网络。以IEEE33节点系统为例,验证所提方法在分布式电源优化配置问题求解中具有很强的全局搜索能力,可以有效、准确地实现分布式电源的最优配置,计算过程简单可靠,具有实用价值。