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.     

考虑DG并网逆变器和APF参与电压治理的SVG优化配置

为解决配电网中分布式电源(DG)高比例渗透引起的电压偏差问题,提高系统电压稳定性,提出一种DG并网逆变器和电压检测型有源滤波器（voltage detection based active power filter,VDAPF）参与电压治理的SVG优化配置策略。采用分区思想,提出基于社团理论的分区方法,选取各区域的主导治理节点作为SVG候选接入节点;建立SVG总投资成本最小和系统电压偏差治理效果最优的多目标SVG优化配置模型,并采用改进的遗传算法对配置模型进行求解;考虑DG并网逆变器和电压检测型APF剩余容量的不确定性,采用多场景分析技术构建一系列电压治理运行场景。以IEEE 33节点配电网结构为算例进行分析,验证了所提优化配置策略的有效性和合理性。     

Multiobjective optimization and decision-making for DG planning considering benefits between distribution company and DGs owner

Environmental concerns and fossil fuels uncertainties have resulted in promotion of multi-source and multi-type distributed generation (DG). However, the development of DG has brought new challenges to distribution system. This paper proposes a multiobjective optimization and decision-making methodology for determining size and site of multi-source and multi-type DG in distribution networks. The proposed method is based on the combination of analytical method and multi-objective optimization method and set pair of analysis (SPA). The comprehensive analysis of the loss sensitivity factor, voltage profile and reliability gave DG candidate locations. The multi-objective optimization method is based on an already-known but suitably modified Non-Dominated Sorting Genetic Algorithm (NSGA) to solve the constructed formulations, which include maximizing benefits of DG owner and Distribution Companies (DisCo) while meeting some constraints. The objective not only includes costs for DG investment, DG operation and maintenance, purchase of power by DisCo but also involving quantization for improvement of losses, voltage, reliability, etc. SPA, which is a multi-attribute decision analysis, is applied to obtain the synthetic priority of pareto solutions and carry out rank stability analysis. Furthermore, the proposed technique is applied to 37-bus distribution network. The results show that the proposed method is fast, reliable and available to determine size and site of DG as well as balance benefits between DG owner and DisCo.     

多目标无功优化的向量评价自适应免疫粒子群算法

为了克服粒子群算法在高维复杂问题寻优时容易陷入局部搜索的现象,提出了一种自适应免疫粒子群算法。该算法利用引入免疫系统的免疫信息处理机制和自动调整动量系数的自适应因子,从整体上达到系统的最佳控制方案。并将基于目标向量的个体评价方法与自适应免疫粒子群算法相结合,提出了基于向量评价的自适应免疫粒子群算法(vector evaluated adaptive immune particle swarm optimization,VEAIPSO)来解决多目标无功优化问题。通过引入静态电压稳定指标,建立了以系统有功损耗最小、节点电压偏移量最小及静态电压稳定裕度最大为目标的多目标无功优化模型。IEEE30和IEEE118节点系统算例仿真结果表明,该算法能有效地解决多目标无功优化问题,并具有良好的收敛稳定性和较高的寻优精度。     

Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem.     

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.     

Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine for optimal allocation of DG in radial distribution network

Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.     

Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs

With the consideration of time sequence characteristics of load and distributed generator (DG) output, a novel method is presented for optimal sitting and sizing of DG in distributed system. Multi-objective functions have been formulated with the consideration of minimum investment and operational cost of DG, minimum purchasing electricity cost from main grid and minimum voltage deviation. To solve the multi-objective optimization problem, an improved Non-dominated Sorting Genetic Algorithm II has been proposed. The compromised solution is extracted from the Pareto set using the fuzzy theory method. Several experiments have been made on the modified PG&E 69-bus and multiple actual test cases with the consideration of multiple DGs. The computational result and comparisons indicate the proposed method for optimal placement and sizing of DG is feasible and effective.     

Optimal Allocation of DGs and Reconfiguration of Radial Distribution Systems Using an Intelligent Search-based TLBO

This paper addresses an application of Teaching-Learning-Based Optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions.     

计及低碳效益的分布式发电优化配置

首先对电力系统各个环节进行低碳分析,针对配电网分布式发电DG(distributed generation)优化配置问题,提出了低碳费用、电压安全、有功网损这三个评估配电网效益的重要指标,在此基础上建立分布式发电多目标优化配置模型,应用最大满意度法将多目标规划转化成单目标规划问题,并用模拟植物生长算法PG-SA(plant growth simulation algorithm)对上述模型进行求解。算例表明该模型确定的分布式电源在配电网的位置和容量可有效减少碳排放、提高系统运行电压,降低有功网损。     

配电网中计及短路电流约束的分布式发电规划

在配电网中合理规划分布式发电（DG）对充分发挥DG的效益、抑制DG的负面影响具有重要意义。文中研究了配电系统中DG位置和容量规划问题,建立了考虑经济性和安全性的DG规划多目标模糊优化模型。目标函数由DG投资成本最小、系统网损最小和静态电压稳定裕度最大3个优化子目标组成,应用模糊理论将多目标规划转化为单目标规划问题。在模型中计及了短路电流约束,对配电网中计及DG影响的故障计算原理进行了分析。考虑到DG出力具有一定的间歇特点,模型中增加了系统的旋转备用约束,保证任意一台DG退出时,系统具有足够的功率来满足负荷要求。在43节点配电系统中进行了测试,表明了文中方法的可行性。     

考虑负荷不确定性的多目标交直流系统无功优化

提出了一种考虑负荷不确定性的多目标交直流系统无功优化方法,研究负荷模型的不确定性对交直流系统无功优化的影响。本文将系统有功损耗最小、系统电压稳定裕度最大作为目标函数,基于负荷误差的正态分布特征,并利用二层规划理论方法,建立考虑负荷不确定性的多目标交直流系统无功优化数学模型。针对交直流无功优化非线性、多变量、多约束的特点,采用内点法和遗传算法相结合的混合优化算法求解上层模型,下层模型采用实数编码的改进遗传算法求解。采用基于IEEE30标准节点改进的交直流算例对提出的方法进行验证,结果验证了本文提出方法的可行性及有效性。     

基于改进粒子群算法的配电网分布式电源规划

合理地对分布式电源进行选址和定容对于实现配电网网损最小是至关重要的.应用改进粒子群优化算法进行配电网分布式电源(DG)规划,并结合罚函数法将DG规划问题转化成无约束求极值问题,从而有效地提高了改进粒子群优化算法的全局收敛能力和计算精度.对69节点和33节点配电测试系统进行仿真计算,结果表明了论文采用的DG规划模型和改进粒子群优化算法的正确性和适用性.     

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。     

基于免疫进化细菌觅食算法的多目标无功优化

为了更好地解决电力系统多目标无功优化问题,分析了当前多目标无功优化算法存在的缺陷,提出了一种基于免疫进化的改进多目标细菌觅食优化算法。该算法求得的Pareto最优解分布均匀,收敛性和鲁棒性好。IEEE14,IEEE30节点测试系统的算例结果表明所提的算法在多目标无功优化中具有良好的效果,为各目标之间的权衡分析提供了有效工具,是一种求解多目标无功优化问题的有效方法。     

考虑需求响应影响的含分布式电源的配电网多目标协调规划

分析了考虑需求响应影响的含分布式电源(DG)的配电网多目标协调规划问题,建立了以DG综合投资成本(包括DG安装及运行维护成本、购电综合成本、需求响应运行成本、网络损耗成本和环境效益)最小为目标的模型,综合考虑网络系统安全约束和需求响应运行约束条件,实现DG位置和容量的协调规划。利用自适应粒子群算法对IEEE33节点系统进行求解,算例结果表明,该模型能有效提高可再生能源的利用率,提升含DG的配电网规划的经济性。     

基于GRASP-PR混合算法的电动汽车有序充电优化

大规模电动汽车(Electric Vehicle,EV)并网充电会对配电网造成过负荷、网损增大、电压越界等影响,为了减小EV充电对电网的冲击,文中构建一种多目标EV充电优化模型,并提出GRASP-PR混合算法进行求解。算例仿真采用IEEE 33节点配电网系统,对比研究所提算法、传统GRASP算法和无序充电的仿真结果,证明基于该混合算法有序充电能达到最小化配电网运营成本、负荷"移峰填谷"和升高节点电压水平的目标,有利于解决EV有序充电问题。     

分布式电源的优化配置

分布式电源（DG）的优化配置对充分发挥其优点,减少对电网的影响至关重要.介绍了3种典型的DG优化配置模型,对解决优化配置模型的数值优化算法及启发式优化算法进行了综述,概述了DG多目标优化模型的处理方法.针对DG配置的现状,提出今后计及可靠性、不确定性等因素的DG配置的发展方向.     

面向用户需求的DG与配网网架多目标联合规划

考虑电力用户的多样化供电需求和可再生能源DG出力随机性,提出了面向用户需求的DG与配网网架多目标联合规划方法。根据用户对供电电压质量和可靠性等多样化的需求,对负荷需求整理分类;根据可再生能源DG出力的概率模型和机会约束规划方法,处理不确定性问题;以年社会成本、电压偏移率和用户停电缺失量综合最优为目标函数,建立了DG与配网网架多目标联合规划模型。基于pareto优化理论,采用多目标混合粒子群算法和熵权修正的AHP-TOPSIS多属性决策策略相结合的方法,求解模型。最后,利用算例验证所建模型的合理性和有效性。     

Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system

The delivery of power from sources to the consumer points is always accompanied of power losses. Basically, active losses in distribution systems can be reduced by optimal reconfigurations of the network. Optimal capacitor allocation problem in reconfigured distribution network is a challenge of researchers for several decades. This paper presents a computationally efficient methodology namely, krill herd (KH) algorithm to find optimal location of capacitor and optimal reconfiguration in order to minimize real power loss of radial distribution systems. Moreover, the opposition based learning (OBL) concept is integrated with KH algorithm for improving the convergence speed and simulation results. In order to show the usefulness and supremacy, the conventional KH and proposed oppositional KH (OKH) algorithms are tested on 33-bus and 69-bus radial distribution networks. The simulation results of the proposed methods are compared with fuzzy multi-objective approach and non dominated sorting genetic algorithm (NSGA). The solution results show that OKH technique could generate better quality solutions and better convergence characteristics than those obtained by conventional KH algorithm and other existing optimization techniques available in the literature. Results also show the robustness of the proposed methodology to solve reconfigured distribution network (RDN) problems.