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.     

An efficient optimal capacitor allocation in DG embedded distribution networks with islanding operation capability of micro-grid using a new genetic based algorithm

Capacitor banks are commonly used in electric distribution networks as a kind of reactive power sources. These sources are located in distribution networks for power factor correction, loss reduction, and voltage profile improvement. For these purposes optimal capacitor placement is needed to determine capacitors types, sizes and locations. Distribution system with Distributed Generation (DG) can have micro-grid that it will operate in both grid-connected and islanded modes of operation. The aim of this paper is to provide a method for optimal capacitor (fixed and switchable) placement in such a distribution network. The effect of different operation modes of DGs on the network is also investigated. The proposed method can guarantee the benefits of capacitor installation at different load levels. It is based on genetic algorithm (GA) with new coding and operators. Switching table of the allocated capacitors can be found through the proposed structure of the chromosome. Some case studies developed to illustrate the efficiency of the proposed method.     

Optimal multi objective placement and sizing of multiple DGs and shunt capacitor banks simultaneously considering load uncertainty via MOPSO approach

This study proposes a new application of multi objective particle swarm optimization (MOPSO) with the aim of determining optimal location and size of distributed generations (DGs) and shunt capacitor banks (SCBs) simultaneously with considering load uncertainty in distribution systems. The multi objective optimization includes three objective functions: decreasing active power losses, improving voltage stability for buses and balancing current in system sections. The uncertainty of loads is modeled by using fuzzy data theory. This method uses Pareto optimal solutions to solve the problem with objective functions and constraints. In addition, a fuzzy-based mechanism is employed to extract the best compromised solution among three different objective functions. The proposed method is implemented on IEEE 33 bus radial distribution system (RDS) and an actual realistic 94 bus Portuguese RDS and the results are compared with methods of Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Genetic Algorithm (NSGA), Multi-Objective Differential Evolution (MODE) and combination of Imperialist Competitive Algorithm and Genetic Algorithm (ICA/GA). Test results demonstrate that the proposed method is more effective and has higher capability in finding optimum solutions in cases where DG and SCB are located and sized simultaneously in a multi objective optimization.     

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.     

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network     

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。     

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.     

A novel method for optimal DG units capacity and location in Microgrids

Distributed generation (DG) has an overall positive impact on Microgrids. These DGs are usually located close to the load centers which lead to some benefits such as; system power loss and energy cost reduction; voltage profile and stability improvement; environmental friendliness, postponement system upgrading and reliability enhancement. In this paper, a novel combined method based on Genetic Algorithm (GA) and Intelligent Water Drops (IWD) is proposed to find location and capacity of DG in Microgrids for optimizing some objective functions. The objectives are minimizing network power losses, improving voltage regulation and increasing the voltage stability within the framework of system operation and security constraints in Microgrids. In this paper, DG units are modeled as generators that are able to inject just active power to network. The Intelligent Water Drops (IWD) algorithm is a new swarm-based optimization algorithm inspired by observing natural water drops that flow in rivers. A detailed performance analysis is carried out on 69-bus and 33-bus Microgrids to demonstrate the effectiveness of the proposed methodology.     

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。     

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.     

基于DG和多端VSC协调控制的交直流配电网优化运行

分布式电源(DG)大规模并网不仅带来了消纳问题,还使交直流网络的经济安全运行面临巨大挑战。基于此,文中提出一种基于DG选址和多端电压源换流器(VSC)协调控制的交直流混合配电网优化运行方法。针对DG选址,基于灵敏度分析方法提出一种节点网损灵敏度指标,利用网络中不同位置的负荷节点对网损敏感度不同的规律进行交流网侧的DG选址。进而建立以网络有功总损耗、节点电压偏移量和DG盈余量最小为目标的多目标优化模型,对多端VSC不同控制策略下的端口功率电压变量和DG的有功出力进行协调控制。仿真结果表明,所提优化运行方法能够提高网络运行经济性和安全性,并兼顾配电网对DG的消纳水平,为实际工程中的决策人员提供重要的参考。     

Reconfiguration and DG Sizing and Placement Using Improved Shuffled Frog Leaping Algorithm

Abstract

In this paper is proposed a reconfiguration methodology with the presence of Distributed Generation (DG), aimed at achieving the minimum power loss, minimum number of switching operation and minimum deviation of bus voltage while satisfying all constraints using improved shuffled frog leaping algorithm (ISFLA).The performance of the proposed method is examined on 33 and 69 bus IEEE test distribution systems. The ISFLA performance is evaluated with the well-known algorithm including of harmony search algorithm (HSA), refined genetic algorithm (GRA), particle swarm optimization (PSO), differential evolutionary (DE) and conventional SFLA. Simulation results showed that the total power loss and voltage bus minimum in primary distribution network can be reduced significantly. Also the results in different scenarios are showed that the simultaneous reconfiguration and DG placement method is better in less losses and also in more minimum voltage. Moreover, the ISFLA superiority is proved in comparison with the HAS, GRA, PSO, DE, and SFLA in view of more convergence speed and accuracy and also converges in less number iteration. Also, the performance of the proposed method is favorable compared to previous studies.     

含低电压穿越型分布式电源配电网的短路电流计算方法

换流器型分布式电源(DG)在配电网中的应用使传统配电网的短路电流计算方法不再适用。根据DG在配电网故障点前后位置的不同,将DG处理为不同类型的故障等效模型,故障点上游DG采用低电压穿越故障等效模型,故障点下游DG采用恒定电流源故障等效模型。提出了一种基于叠加定理的短路电流迭代计算方法,在每一次迭代过程中,根据当前节点电压和DG的故障等效模型分别修正故障点上游DG和下游DG的输出短路电流,并利用节点电压方程求解配电网的短路电流和节点电压分布,直到满足收敛条件。通过对算例系统的分析计算,验证了所提方法的正确性。该方法可应用于含DG的大规模配电网的短路电流求解。     

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.     

含分布式电源的配电网电压越限薄弱环节识别方法

提出一种含分布式电源(DG)的配电网电压越限薄弱环节的识别方法。对电压越限原因进行机理分析,确定导致电压越限可能的原因及其电压敏感度;定义压降比系数,提出计及网损的各节点DG/负荷最大准入容量的计算方法;研究将各节点实际接入DG容量归算至馈线末端的方法,并将其与馈线末端的最大准入容量进行比较,识别配电网中易发生电压越限的薄弱节点及越限原因;在MATLAB上搭建IEEE 33节点系统验证所提方法的有效性和正确性。     

配电网交直流同线馈送方式的潮流分析

阐述了分布式直流电源借助现有交流配电网络进行交直流同线馈送的思想,讨论了该思想的基本原理和电路结构,并分析了单极运行方式下的潮流计算问题。针对分布式直流电源容量一般较小的特点,通过在直流网络与交流网络耦合处设立直流网络的“虚拟平衡机”,可以方便地求解该运行方式下的交直流潮流,具有简便快速的特点。算例结果显示,该方式较传统交流配电方式电压更均衡,网损更小。表明了交直流同线馈送方式的优越性、正确性和该方法求解潮流问题的有效性。     

分布式电源并网优化配置的图解方法

分布式电源(distributed generation,DG)除了具有调节潮流分布等常规作用外,还可被用来治理电压暂降和降低线损等。针对分布式电源引入配电网后最佳安装位置与容量计算的问题,该文基于链式配电网络、恒功率静态负荷模型和分布式电源的功率模型,并考虑DG对降低线损和调节电压的作用,提出一种图解与遗传算法相结合的计算方法。该方法采用图示求解大量方程,避免了传统算法繁琐的过程和过多的假设条件,并且通过基于电压不越界为约束的遗传算法确定分布式电源的最佳容量,有效避免了节点电压接近合格范围的上界。最后通过典型的仿真算例并与传统方法进行比较,充分证明所提方法的正确性和可行性。     

考虑越限风险的主动配电网中DG、SOP与ESS的两阶段协调规划

双碳和新型配电系统构建目标下优化多种灵活型资源位置与容量是实现该目标的重要技术路线。为此,兼顾规划运行的经济性和安全性,提出一种考虑越限风险的主动配电网中可再生分布式电源(distributed genevation,DG)、智能软开关(soft open point, SOP)、储能(energy storage, ESS)的两阶段协调规划方法。阶段1以综合成本与越限风险最小为目标优化DG、SOP与ESS的位置和容量。阶段2属于联合SOP、网络重构、有载调压变压器、电容器组、需求响应和储能多种调节手段的多目标运行优化。同时,以基于灰靶决策技术的LDBAS算法和二阶锥优化的混合方法为规划优化的求解工具。在IEEE 33节点配电系统上仿真,测试结果证明了所提两阶段协调规划模型能够有效地提高系统运行效率、增强灵活性、降低运行安全风险及经济成本。     

倒送功率约束下的分布式电源最大接入容量分析

分布式电源(DG)接入配电网后,低电压等级电网可能倒送功率至高电压等级电网,对电网造成不利影响。在各种影响因素中,倒送功率限制是影响DG接入电网的主要因素。从倒送功率约束的角度出发,结合节点电压及潮流约束,建立了以DG接入容量最大为目标的数学模型,给出了最大接入容量的计算方法,并分析了DG的接入对配电网有功网损的影响。考虑节假日对负荷的影响,给出了调整倒送功率约束以增大DG接入容量的建议。以一个33节点的10 kV配电网接入分布式光伏电源为例,验证了所提方法的可行性。     

A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models

In this paper a new and efficient hybrid multi-objective optimization algorithm is proposed for optimal placement and sizing of the Distributed generations (DGs) in radial distribution systems. A Multi-objective Shuffled Bat algorithm is proposed to evaluate the impact of DG placement and sizing for an optimal improvement of the distribution system with different load models. In this study, the ideal sizes and locations of DG units are found by considering the power losses, cost and voltage deviation as objective functions to minimize. Furthermore, the study is verified with voltage dependent load models like industrial, residential, commercial and mixed load models. The feasibility of the proposed technique is verified with the 33 bus distribution network and also the qualitative comparisons against a well-known technique, known as Non-dominated Sorting Genetic Algorithm II (NSGA-II) is done and results are presented.