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.     

基于免疫遗传算法的智能配电网分布式电源选址定容

合理地对分布式电源进行选址和定容对于智能配电网规划非常重要。文章在研究分布式电源规划的基础上,建立了以有功网损最小为目标函数的优化模型,用罚函数法将分布式电源规划问题转化为无约束问题,针对该模型特点,首次将免疫遗传算法应用到分布式电源选址和定容问题的求解中。该算法综合了免疫系统和遗传算法的优点,具有良好的全局收敛能力。对33节点配电测试系统进行仿真计算,将仿真结果与标准遗传算法进行比较。验证了免疫遗传算法具有一定的收敛性和适应性。     

A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems

Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.     

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。     

以低碳为目标的分布式发电选址定容方法研究

针对分布式电源选址定容中新能源发电容量的受限问题,提出了分布式电源的两轮规划方法,即首先对新能源发电进行选址定容,然后在此基础上优化配置其他类型的分布式电源。在考虑系统运行费用最低、电压质量最好的基础上,创新地加入了系统等效碳排放量最小这一节能目标,并将目标函数设为规划前后成本和收益之差,直观反映了配电网的经济效益。采用改进的自适应惯性权重粒子群算法,对IEEE33节点标准算例进行求解,最终给出了该网络的最优DG配置方案,分别以图、表的形式表明各种分布式电源的接入节点和接入容量,其中光伏发电占到了分布式电源总量的60%,结果分析表明网损、电压和碳排放指标均有较大改观。     

Optimum placement and sizing of DGs considering average hourly variations of load

This paper presents a multiobjective technique for obtaining optimal sizing of Distributed Generation (DG) units considering both technical and economical factors of the distribution system. The technical factors include real power loss reduction, line load reduction and voltage profile improvement and the economical factors consider optimal DG investment cost. Three different Distributed Generation systems solar photovoltaic, biomass and wind system are considered for integration with the existing distribution system. Since solar photovoltaic system is not available at night time, only biomass and wind systems are operated and for day time operation all the three distribution generation systems are considered. A new sensitivity index based on voltage sensitivity and apparent load power is proposed for identification of optimal locations for DG placement. The optimum sizing of DG units operating at unity power factor and lagging power factor is obtained using GA for different load levels considering daily average hourly loading aiming at improving the technical performance of the distribution system with optimum investment on DG units. Simulation results are presented to show the advantage of the proposed methodology in terms of technical performance and annual economical savings of the distribution system.     

Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

This paper addresses the optimal distributed generation sizing and siting for voltage profile improvement, power losses, and total harmonic distortion (THD) reduction in a distribution network with high penetration of non-linear loads. The proposed planning methodology takes into consideration the load profile, the frequency spectrum of non-linear loads, and the technical constraints such as voltage limits at different buses (slack and load buses) of the system, feeder capacity, THD limits, and maximum penetration limit of DG units. The optimization process is based on the Genetic Algorithm (GA) method with three scenarios of objective function: system power losses, THD, and multi-objective function-based power losses and THD. This method is executed on the IEEE 31-bus system under sinusoidal and non-sinusoidal (harmonics) operating conditions including load variations within the 24-hr period. The simulation results using Matlab environment show the robustness of this method in optimal sizing and siting of DG, efficiency for improvement of voltage profile, reduction of power losses, and THD. A comparison with particle swarm optimization (PSO) method shows that the proposed method is better than PSO in reducing the power losses and THD in all suggested scenarios.     

Optimal Installation of Different DG Types in Radial Distribution System Considering Load Growth

In power distribution network, the gradual increase in system load is a natural process, and it results in increased real and reactive power losses and reduced voltage profile. In this paper, optimal single and multiple installations of different types of distributed generation (DG) units are used to handle annual growth in system load, while satisfying system operational constraints. For load growth study, a predetermined growth in system annual load is considered. Minimization of system total real power loss is taken as the main objective, and optimal location and sizing of different DG types are determined using a hybrid configuration of weight-improved particle swarm optimization (WIPSO) with gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm. The effect of load growth is studied using standard 33-bus radial distribution system, and the results illustrate significant reduction in system real and reactive power losses, enhancement in system voltage profile, and improvement in load carrying capacity of distribution feeder sections. Moreover, the economic benefits of DG on system annual load growth are also established. Also, the effectiveness of the proposed algorithm is demonstrated by comparing the results with other evolutionary optimization techniques.     

考虑环境因素的分布式发电多目标优化配置

分布式发电优化布置与定容问题是智能电网发展中所面对的一个重要课题。该文在节点有功、无功网损微增率基础上,通过负荷功率法将两者结合,提出等效网损微增率的概念。通过计算该微增率并对其进行排序,可确定分布式发电(distributed generation,DG)的最优安装位置,并且最小化输电线路网损。对于DG定容问题,该文同时考虑了有功网损、电压改善程度和环境改善程度这3个重要指标,将DG优化容量确定问题转化为一个多目标非线性规划问题。采用目标逼近和二次序列规划方法对提出的算法进行求解。算例结果表明,采用该方法确定DG在系统中的布置位置和容量可有效提高系统运行电压,降低有功网损,减少电厂排放的污染气体。该方法对DG在规划阶段的选址和定容问题有着一定的实用价值。     

An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialist competitive algorithm and genetic algorithm

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation (DG) sources and capacitors banks in parallel are increased. Determining the installation location and capacity are two significant factors affecting network loss reduction and improving network performance. This paper, proposes an efficient hybrid method based on Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA) which can greatly envisaged with problems for optimal placement and sizing of DG sources and capacitor banks simultaneously. The objective function is power loss reduction, improving system voltage profile, increasing voltage stability index, load balancing and transmission and distribution relief capacity for both utilities and the customers.The proposed method is implemented on IEEE 33 bus and 69 bus radial distribution systems and the results are compared with GA/Particle swarm optimization (PSO) method. Test results show that the proposed method is more effective and has higher capability in finding optimum solutions.     

一种分布式电源选址定容的两阶段优化规划方法

针对预先未给定分布式电源(distributed generator,DG)待选接入点的DG选址定容规划,提出了一种以配电网运行成本最小为目标的两阶段优化规划方法:第一阶段,建立了一个基于权系数的DG选址模型,并运用有效集法进行求解,筛选出一组DG待选接入点组合;第二阶段,提出了一种动态混沌粒子群算法来求解第一阶段所得出的待选DG节点的定容问题,并将最优规划结果值返回给第一阶段,从而确定出DG最优的接入节点和接入容量。最后通过美国PGE 69节点系统验证了所提模型的可行性以及方法的有效性。     

基于配电网节点边际容量成本和时序特性的分布式电源规划

配电网的边际容量成本表述了负荷的增加或分布式电源(DG)接入配电网中引起供电设备所需容量的变化,从而引起的供电投资成本的变化。本文针对开环运行的配电网,通过灵敏度法构建灵敏度系数矩阵,对配电网的节点边际容量成本(LMCC)进行计算,并根据实际情况考虑LMCC的变化,将DG效益评估分为用户自备DG和电力部门DG配置。重点讨论电力部门DG配置情况下多类型DG选址定容的规划,并考虑了多类型DG和负荷的时序特性。最后运用MATLAB仿真软件在IEEE33节点配电网系统中进行了LMCC数值计算,利用粒子群算法进行了考虑时序特性的DG规划,并验证了模型和方法的合理性。     

计及可靠性收益的配电网柔性多状态开关及分布式电源综合优化配置

柔性多状态开关(flexible multi-state switch,FMS)是支撑新型电力系统背景下配电网运行控制能力提升和可再生能源接入渗透率改善的重要技术装备,针对现有配电网中FMS的规划并未详细考虑FMS与分布式电源(distributed generation,DG)的协调配置,以及对于FMS接入带来的可靠性提升考虑不足的问题。文章在规划配置阶段详细考虑了含FMS和DG的协同优化运行,并充分计及FMS接入带来的可靠性收益,构建了基于嵌套优化的FMS和DG选址定容双层优化配置模型。针对双层嵌套规划模型求解的复杂性,提出了基于改进粒子群算法和GAMS(general algebraic modeling language)算法相结合的模型求解策略,以改造后的IEEE 33节点算例系统为例,对所提出模型和方法的有效性进行了验证,结果表明提出的方法相比传统单一FMS规划具有更好的经济性,能最大程度地降低配电网年综合费用,同时减少用户因停电带来的损失。     

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。     

基于模拟退火遗传算法的分散式风电选址定容

分散式电源的优化配置是智能电网发展的重要课题之一。对分散式电源进行合理的选址和定容对于配电网规划非常重要。以分散式风力发电为平台,考虑分散式风力发电机的接入对配电网网损及电压质量的影响,应用计及风力发电机的配电网潮流计算方法获得配电网的相关电气参数,并在此基础上以提高电压质量和网损最小为目标,将模拟退火遗传算法应用到寻优算法中。最后举一算例进行验证。     

Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method

A great number of methods have been proposed for distributed generation (DG) placement in distribution networks to minimize the power loss of Medium Voltage (MV) lines. However, very few researches have been done for network configuration in parallel with the DG siting and sizing for the maximum system loss reduction. In this paper, a heuristic method based on “uniform voltage distribution based constructive reconfiguration algorithm” (UVDA) is proposed for the simultaneous reconfiguration and DG siting and sizing. The results obtained from the application of the proposed method on two well-known distribution networks and a real network clearly verify the robustness of the contributed technique. The simulation results demonstrate that the proposed approach is able to find the best solution of the problem found so far. Also, the presented method is applicable to real large-scale distribution systems to find the optimal solution in a very short period of time.     

计及电动汽车充电模式的主动配电网多目标优化重构

电动汽车的入网会影响到电网的经济性和安全性,而配电网重构是电网优化运行的有效措施。根据主动配电网(ADN)的特点,提出了含分布式电源(DG)和电动汽车充电的优化重构模型。通过有功网损灵敏度确定DG的安装位置和容量,构造出DG出力和EV充电的多时段概率模型。建立有功网损、电压偏移指标(VSI)和开关操作次数的多目标优化数学模型以确定系统的最佳重构方案,并在IEEE33节点标准配电系统中,采用引入小生境技术的改进多目标粒子群算法(IMPSO)进行计算,提高了算法的全局寻优能力。考虑了电动汽车无序充电和智能充电两种模式,对比不同场景下得出的结果,验证了该方法的实用性和有效性。     

A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market

This paper introduces a new framework included mathematical model and a new software package interfacing two powerful softwares (MATLAB and GAMS) for obtaining the optimal distributed generation (DG) capacity sizing and sitting investments with capability to simulate large distribution system planning. The proposed optimization model allows minimizing total system planning costs for DG investment, DG operation and maintenance, purchase of power by the distribution companies (DISCOs) from transmission companies (TRANSCOs) and system power losses. The proposed model provides not only the DG size and site but also the new market price as well. Three different cases depending on system conditions and three different scenarios depending on different planning alternatives and electrical market structures, have been considered. They have allowed validating the economical and electrical benefits of introducing DG by solving the distribution system planning problem and by improving power quality of distribution system. DG installation increases the feeders’ lifetime by reducing their loading and adds the benefit of using the existing distribution system for further load growth without the need for feeders upgrading. More, by investing in DG, the DISCO can minimize its total planning cost and reduce its customers’ bills.     

Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches

Integration of renewable energy based distributed generation (DG) units provides potential benefits to conventional distribution systems. The power injections from renewable DG units located close to the load centers provide an opportunity for system voltage support, reduction in energy losses, and reliability improvement. Therefore, the location of DG units should be carefully determined with the consideration of different planning incentives. This paper presents a comparison of novel, combined loss sensitivity, index vector, and voltage sensitivity index methods for optimal location and sizing of distributed generation (DG) in a distribution network. The main contribution of the paper is: (i) location of DGs based on existing sensitivity methods, (ii) proposing combined power loss sensitivity based method for DG location, (iii) modified Novel method for DG location, (iv) comparison of sensitivity methods for DG location and their size calculations, and (v) cost of losses and determining cost of power obtained from DGs and the comparison of methods at unity and lagging power factors. The results show the importance of installing the suitable size of DG at the suitable location. The results are obtained with all sensitivity based methods on the IEEE 33-bus and 69-bus systems.     

配电网分布式电源最优配置研究*

针对配电网中分布式电源的选址与定容问题,以分布式电源的渗透率最高、配电网的网损改善率最大及电压质量改善率最大三个目标构成目标函数,利用判断矩阵确定权重建立多目标优化模型。提出一种改进的果蝇优化算法对配电网中分布式电源的接入位置和接入容量进行优化,IEEE 33节点配电网络的仿真结果表明:与遗传算法和基本果蝇优化算法相比,改进的果蝇优化算法在收敛速度和求解精度两个方面都具有较为明显的优势,验证了上述优化配置模型及改进算法的实用性与有效性。