Optimal power flow using differential evolution algorithm

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results are compared with the results reported in the literature. The results show the effectiveness and robustness of the proposed approach.     

Optimal power flow using differential evolution algorithm

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of OPF control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. In addition, non-smooth piecewise quadratic cost function has been considered. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem for the systems considered.     

Optimal power flow solution of two-terminal HVDC systems using genetic algorithm

The usage extensively of high voltage direct current (HVDC) transmission links in recent years makes it necessary further work in this area. Therefore, two-terminal HVDC transmission link is one of most important elements in electrical power systems. HVDC link representation is mostly ignored and simplified for optimal power flow (OPF) studies in power systems. OPF problem of purely alternating current (AC) power systems is defined as minimization of the fuel cost to subjected equality and inequality constraints. Hence, OPF software of purely AC power systems is extended by taking into consideration power transfer control characteristics of HVDC links. In this paper, OPF problem of integrated AC–DC power systems is first solved by genetic algorithm that is a heuristic algorithm based on evolutionary. The proposed method is tested on two test systems which are the modified 5-node test system and the modified WSCC 9-bus test system. In order to show effectiveness and efficiency of the proposed method, the obtained results are compared to that reported in the literature.     

Power loss minimization in distribution systems using multiple distributed generations

In this work, an efficient analytical method is proposed for optimally allocating distributed generations (DGs) in electrical distribution systems to minimize power losses. The proposed analytical method can be employed for obtaining the optimal combination of different DG types in a distribution system for loss minimization. The validity of the proposed method is demonstrated using two test systems with different configurations by comparing with the exact optimal solution obtained from the exhaustive optimal power flow (OPF) algorithm. The calculated results and the comprehensive comparisons with existing methods prove the superiority of the proposed method in terms of accuracy and calculation speed. The proposed loss minimization method can be a useful tool for any general DG allocation problem since it provides effective and fast loss evaluation taking into account other benefits. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于遗传-蚁群算法的交直流配电网分布式电源优化配置

随着分布式电源（distributed generation，DG）受到广泛的关注与研究，分布式电源接入交直流配电网的规划问题日益突出。该文在分布式电源选址定容阶段充分考虑不同类型DG和负荷的时序特性，以DG运维费用、DG投资年等效费用、系统网络损耗费用、燃料费用、污染赔偿费用、环保补贴综合最小作为目标函数，同时加入电压、功率等约束条件，建立了DG的选址定容模型。根据遗传、蚁群算法各自的优劣势，提出了运用遗传-蚁群复合算法求解该优化模型。最后以改进的IEEE-33节点配电网作为算例，验证了所提模型的合理性及算法的有效性。     

Impact of the penetration of distributed generation on optimal reactive power dispatch

Optimal reactive power dispatch (ORPD) is a complex and non-linear problem, and is one of the sub-problems of optimal power flow (OPF) in a power system. ORPD is formulated as a single-objective problem to minimize the active power loss in a transmission system. In this work, power from distributed generation (DG) is integrated into a conventional power system and the ORPD problem is solved to minimize transmission line power loss. It proves that the application of DG not only contributes to power loss minimization and improvement of system stability but also reduces energy consumption from the conventional sources. A recently proposed meta-heuristic algorithm known as the JAYA algorithm is applied to the standard IEEE 14, 30, 57 and 118 bus systems to solve the newly developed ORPD problem with the incorporation of DG. The simulation results prove the superiority of the JAYA algorithm over others. The respective optimal values of DG power that should be injected into the four IEEE test systems to obtain the minimum transmission line power losses are also provided.     

求解非光滑燃料费用函数的基于进化规划的机组最优潮流

提出求解具有非光滑燃料费用函数的存在爬坡率限制的最优潮流方法。针对两次、阶梯形、联合循环机组的非光滑燃料费用函数,介绍一个基于进化规划的算法。在该算法中,为避免早熟,交叉操作随后代的数目非线性变化。介绍了所提出的进化算法应用于有线路约束的IEEE30节点系统和印度62节点系统的情况。以MVA为单位的线路潮流直接采用牛顿-拉夫逊法计算。算例证明所提出的进化算法简单,对求解具有非光滑燃料费用函数的存在很多约束的最优潮流问题有效。     

Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

This paper presents a fuzzy based hybrid particle swarm optimization (PSO) approach for solving the optimal power flow (OPF) problem with uncertainties. Wind energy systems are being considered in the study power systems. OPF is an optimization problem which minimizes the total thermal unit fuel cost, total emission, and total real power loss while satisfying physical and technical constraints on the network. When performing the OPF problem in conventional methods, the load demand and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy based hybrid PSO method is that the forecast load demand and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, total fuel cost, total emission, and total real power loss are developed to obtain the optimal setting under an uncertain environment. To demonstrate the effectiveness of the proposed method, the OPF problem is performed on the IEEE 30- and 118-Bus test systems.     

Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a Hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm

Abstract

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THD_V) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.     

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.     

Artificial bee colony algorithm for solving multi-objective optimal power flow problem

This paper presents a new and efficient method for solving optimal power flow (OPF) problem in electric power systems. In the proposed approach, artificial bee colony (ABC) algorithm is employed as the main optimizer for optimal adjustments of the power system control variables of the OPF problem. The control variables involve both continuous and discrete variables. Different objective functions such as convex and non-convex fuel costs, total active power loss, voltage profile improvement, voltage stability enhancement and total emission cost are chosen for this highly constrained nonlinear non-convex optimization problem. The validity and effectiveness of the proposed method is tested with the IEEE 9-bus system, IEEE 30-bus system and IEEE 57-bus system, and the test results are compared with the results found by other heuristic methods reported in the literature recently. The simulation results obtained show that the proposed ABC algorithm provides accurate solutions for any type of the objective functions.     

基于树木生长算法的含UPFC的最优潮流计算

最优潮流(OPF)计算是一个非凸优化问题,统一潮流控制器(UPFC)的引入增加了OPF问题的非凸程度,使得基于内点法的传统优化算法难以获取全局最优解。文中提出基于树木生长算法(TGA)的计及UPFC的最优潮流计算方法,将发电成本与有功网损、电压偏移加权作为目标函数,并考虑网络与UPFC设备的安全运行约束,优化了OPF模型。最后基于IEEE 30节点系统以及南京西环网116节点实际系统进行算例测试,对比TGA、粒子群与内点法的结果,并使用蒙特卡洛方法对不同的启发式算法分别进行50次计算,验证了TGA具有更好的求解精度与鲁棒性。     

An analytical approach for sizing and siting of DGs in balanced radial distribution networks for loss minimization

This paper presents a novel analytical approach to determine the optimal siting and sizing of distributed generation (DG) units in balanced radial distribution network to minimize the power loss of the system. The proposed analytical expressions are based on a minimizing the loss associated with the active and reactive component of branch currents by placing the DG at various locations. This method first identifies a sequence of nodes where DG units are to be placed. The optimal sizes of DG units at the identified nodes are then evaluated by optimizing the loss saving equations and need only the results of base case load flow. To find out the best location for DG placement, a computational method is also developed. The proposed method has been tested and validated on two IEEE test distribution systems (DSs) consisting of 15 and 33-buses and it has been found that a significant loss saving can be obtained by placing DG units in the system using proposed analytical method.     

Impact of Optimal Unified Power Flow Controller in Electrical Transmission Systems in Reducing Transmission Cost

This paper presents an approach to optimal placement of optimal unified power flow controller (OUPFC) in electrical transmission systems utilizing genetic algorithm (GA), while reducing transmission systems cost using MATLAB and MATPOWER. The proposed approach is based on optimal power flow (OPF) considering optimization of power systems operation conditions. In order to determine the appropriate place for installation of OUPFC, while considering power injection model of this controller, OPF is utilized to obtain multi-objective function of the optimization problem. In this regard, the objective function comprises generation cost, transmission cost, and OUPFC installation cost. The proposed approach is applied to IEEE 30-bus test system, where the obtained results demonstrate remarkable reduction in overall cost of power system.     

计及可入网电动汽车的分布式电源最优选址和定容

一些不确定性因素如可入网电动汽车（PEV）的随机充放电行为导致的负荷和输出功率不确定性、风电机组和太阳能电源输出功率不确定性,以及未来燃料价格波动和负荷随机变化,都会给分布式电源（DG）的选址和定容问题带来风险。在此背景下,采用机会约束规划方法来解决DG的选址和定容中这些不确定性因素所引起的风险。以DG的总成本（包括投...     

基于遗传算法和微分进化算法的分布式电源优化配置

配电系统中,分布式电源（DG）安装位置的选择、额定容量的确定对于电网规划、设计和投资至关重要,以10节点配电网系统为例,采用遗传算法和微分进化算法对分布式电源进行了优化配置,建立了DG的不确定性模型,并将其加入到优化分析中,给出了优化算法的求解程序。对含DG的配电网进行了潮流计算,分析了DG容量与系统总网损的关系。算例分析结果表明,优化配置有效改善了配电网的电压分布,减小了网损,提高了系统负荷率,说明了该优化配置方法合理、有效。     

计及电动汽车无功支撑能力的分布式电源与智能停车场联合规划方法

随着大量分布式电源(DG)及电动汽车(EV)接入配电网,为保证电网的高效、清洁、经济和安全运行,必须对二者进行合理的规划。为此,提出一种计及EV无功支撑能力的DG及智能停车场(IPL)联合规划方法。首先,基于有源配电网的基本物理结构,对EV动力电池的无功可调范围进行了推导。进一步考虑风电等间歇性DG出力、常规用电以及EV充电负荷时空分布的不确定性,通过构建发电—负荷场景以综合计及上述不确定性因素的影响。在此基础上,分别以系统投资和运行成本最小作为目标函数,构建了综合考虑DG和IPL选址定容的两阶段优化模型。根据模型特点,采用经典的遗传算法实现问题求解。以33节点配网系统为例,对所提模型的有效性进行验证。仿真结果表明,在配电网投资规划中充分考虑规模化入网EV的无功支撑能力,能够有效改善系统的电能质量,促进可再生能源高效利用,从而带来更好的经济效益。     

基于改进粒子群优化算法的DG准入容量与优化布置

为进一步优化配电网中分布式电源（distributed generation,DG）的准入容量和优化布置问题,以节点电压和线路载流量为约束条件建立了单电源和多电源准入容量的数学模型,以有功网损最小为目标函数建立了DG优化布置模型。为有效求解该模型,采用了基于粒子群优化（particle swarm optimization,PSO）算法和二次插值相结合的改进PSO算法,将该改进方法应用于IEEE 33节点标准算例,分别进行了DG的最优接入位置与最优容量的仿真,并与粒子群算法优化结果进行了对比,同时还分析了优化布置下的潮流分布。算例仿真结果表明该方法可有效减少DG接入后配电网的网损,提高配电网的供电质量。     

主动管理在含有分布式电源的配电网中的应用

介绍配电网主动管理(AM)的概念和原理,建立AM问题的最优潮流(OPF)模型;提出定量评估系统,采用多种技术经济指标评估AM对含有分布式电源(DG)的配电网的影响;并应用FCM聚类算法简化初始数据集.对改进的33节点系统的评估结果表明:应用AM能够有效地减少配电网的线损,改善电压分布,并可以提高DG投资者的净收益.     

Optimal Allocation and Operating Point of DG Units in Radial Distribution Network Considering Load Pattern

Distributed generation (DG) is a new approach for solving some problems of older power networks. Due to the increasing power demand in recent power systems, the importance of power loss reduction and maintaining system voltages within an acceptable range has given rise to the wide use of DG units in power systems. On the other hand, unplanned and non-optimal application such as installation and operation of DG units might cause other technical problems. In addition, it is important to consider the load pattern in the network, and the best decision for DG unit's operation must be chosen accordingly. In this paper, a method is introduced in order to make the optimal placement and find an optimal operating point for the DG units, which means the power output of DG units, considering the load pattern of the network. This load pattern has an average load of 24 hr a day, four seasons a year. In the proposed method, optimization has two goals: first, is optimizing the DG unit's placement based on improvement of the voltage profile, and the second is operating DG units with optimum power factor, minimizing power loss, and improving voltage profile, with regard to the load pattern. In order to solve this problem, the gravitational search algorithm and genetic algorithm are used. The proposed method is applied on the IEEE 33-bus test system, and the result shows the effectiveness of the proposed method. In order to solve the optimization problem, MATLAB software is used.