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.     

U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

We propose novel techniques to find the optimal achieve the maximum loss reduction for distribution networks location, size, and power factor of distributed generation （DG） to Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network＇s total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in distribution networks, whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations. The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method （EIM） and recently published results. Results showed that the proposed technique can provide an optimal solution with less computation.     

Integrated approach of network reconfiguration with distributed generation and shunt capacitors placement for power loss minimization in radial distribution networks

This article presents the significance of efficient hybrid heuristic search algorithm(HS-PABC) based on Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) in the context of performance enhancement of distribution network through simultaneous network reconfiguration along with optimal allocation and sizing of distributed generators and shunt capacitors. The premature and slow convergence over multi model fitness landscape is the main limitation in standard HSA. In the proposed hybrid algorithm the harmony memory vector of HSA is intelligently enhanced through PABC algorithm during the optimization process to reach the optimal solution within the search space. In hybrid approach, the exploration ability of HSA and the exploitation ability of PABC algorithm are integrated to blend the potency of both algorithms. The box plot and Wilcoxon rank sum tests are used to show the quality of the solution obtained by hybrid HS-PABC with respect to HSA.The computational results prove the integrated approach of the network reconfiguration problem along with optimal placement and sizing of DG units and shunt capacitors as an efficient approach with respect to power loss reduction and voltage profile enhancement. The results obtained on 69 and 118 node network by hybrid HS-PABC method and the standard HSA reveals the effeciency of the proposed approach which guarantees to achieve global optimal solution with less iteration.     

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.     

基于改进共生生物搜索算法的林火图像多阈值分割

针对传统多阈值分割方法计算复杂度随着阈值个数的增加而增长,以及对给定图像进行多阈值分割操作时效率很低等问题,提出了一种基于共生生物搜索（SOS）算法结合Kapur熵的多阈值分割方法。首先将精英反策略（EOBL）引入到SOS算法的共栖阶段,从而改善传统SOS算法处理复杂优化问题时易陷入局部最优的问题;然后引入莱维飞行策略扩大SOS算法的的搜索范围,增强其搜索轨迹的随机性;最终将得到的改进共生生物搜索（MSOS）算法应用到林火图像最佳阈值的选取问题上。实验结果表明,与粒子群优化算法、和声搜索算法、蝙蝠算法等对比算法相比,所提算法能更好地分割图像,在实际工程问题中具有一定的实用性和价值。     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.     

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems

This study proposes an improved version of the Symbiotic Organisms Search (SOS) algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS). This improved algorithm integrated Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies with SOS for a better quality solution and faster convergence. To demonstrate and validate the new algorithm’s effectiveness, the authors tested QOCSOS with twenty-six mathematical benchmark functions of different types and dimensions. In addition, QOCSOS optimized placements for distributed generation (DG) units in radial distribution networks and solved five structural design optimization problems, as practical optimization problems challenges. Comparative results showed that QOCSOS provided more accurate solutions than SOS and other methods, suggesting viability in dealing with global optimization problems.     

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 new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems

This paper proposes a new multi-objective optimization algorithm based on modified teaching–learning-based optimization (MTLBO) algorithm in order to solve the optimal location of automatic voltage regulators (AVRs) in distribution systems at presence of distributed generators (DGs). The objective functions including energy generation costs, electrical energy losses and the voltage deviation are considered in this paper. In the proposed MTLBO algorithm, teacher and learner phases are modified. The considered objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm uses an external repository to save founded Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by compromising) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as genetic algorithm (GA), particle swarm optimization (PSO) and TLBO is extraordinary.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

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.     

A modified symbiotic organisms search (mSOS) algorithm for optimization of pin-jointed structures

The paper introduces a modified symbiotic organisms search (mSOS) algorithm to optimization of pin-jointed structures including truss and tensegrity ones. This approach is refined from the original SOS with five modifications in the following three phases: mutualism, commensalism and parasitism. In the mutualism one, benefit factors are suggested as 1 to equally represent the level of benefit to each organism, whilst the best organism is replaced by a randomly selected one to increase the global search capability. With the aim of improving the convergence speed, randomly created coefficients in the commensalism phase are restricted in the range [0.4, 0.9]. Additionally, an elitist technique is applied to this phase to filter the best organisms for the next generation as well. Finally, the parasitism phase is eliminated to simplify the implementation and reduce the time-consuming process. To verify the effectiveness and robustness of the proposed algorithm, five examples relating to truss weight minimization with discrete design variables are performed. Additionally, two examples regarding minimization a function of eigenvalues and force densities of tensegrity structures with continuous design variables are considered further. Optimal results acquired in all illustrated examples reveal that the proposed method requires fewer number of analyses than the original SOS and the DE, but still gaining high-quality solutions. Furthermore, the mSOS also outperforms numerous other algorithms in available literature in terms of optimal solutions, especially for problems with a large number of design variables.     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.     

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...     

An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration

Power loss reduction has an important role in operating electric distribution network system. There are a lot of methods for reduction power loss such as capacitor placement, distributed generation placement and electric distribution network reconfiguration (EDNR). Among these methods, the EDNR is an efficient technique to reduce power loss due to without taking any costs. However, the EDNR problem is a nonlinear, discrete problem and lots of extreme points. Therefore, it is necessary to have efficient methods for solving the EDNR problem. In this paper, an improved cuckoo search algorithm (ICSA) is proposed for solving the EDNR problem. In which, based on disadvantages of exploration and exploitation process of cuckoo search algorithm (CSA) for solving the EDNR problem, a local search mechanism is added to exploit candidate solutions existing around the current best solution. The calculated results on the simple distribution networks to complex distribution networks show that ICSA has ability for finding the global optimal solution with much smaller iterations and better quality of obtained solution compared with CSA and some other improved versions of CSA. The performance comparisons with other existing methods available in previous studies and the software of Power System Simulator/Advanced Distribution Engineering Productivity Tool (PSS/ADEPT) also lead to the better electric distribution network configuration with smaller total power losses. As a result, ICSA is a potential and reliable method for solving the EDNR problems.     

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

Abstract

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.     

基于CS优化的双簇头分簇路由算法研究

为了减少无线传感器网络(WSNs)分簇路由中簇头的能量消耗,提出了一种基于布谷鸟搜索(CS)优化的双簇头分簇路由算法.CS通过采用节点的剩余能量和节点之间的位置关系来构造适应值函数并选举出最优双簇头.其中,主簇头将数据进行融合,副簇头将融合的数据发送给基站,缓解了以往单簇头同时负责数据融合和传输的双重压力,使得整体能耗在各个节点的分配更均衡.仿真实验表明:与LEACH算法、粒子群优化(PSO)算法相比,CS算法在减小网络能耗以及延长网络生存周期上更具优势.     

A new method for optimal location and sizing of capacitors in distorted distribution networks using PSO algorithm

This paper presents an optimization algorithm for simultaneous improvement of power quality (PQ), optimal placement and sizing of fixed capacitor banks in radial distribution networks in the presence of voltage and current harmonics. The algorithm is based on particle swarm optimization (PSO). The objective function includes the cost of power losses, energy losses and those of the capacitor banks. Constraints include voltage limits, number/size of installed capacitors at each bus, and PQ limits of standard IEEE-519. Using a newly proposed fitness function, a suitable combination of the objective function and relevant constraints is defined as a criterion to select a set of the most suitable buses for capacitor placement. This method is also capable of improving particles in several steps for both converging more readily to the near global solution as well as improving satisfaction of the power quality constraints. Simulation results for the 18-bus and 33-bus IEEE distorted networks using the proposed method are presented and compared with those of previous works. In the 18-bus IEEE distorted network, this indicated an improvement of 3.29% saving compared with other methods. Using the proposed optimization method and simulation performed on the 33-bus IEEE distorted network an annual cost reduction of 31.16% was obtained.     

A survey of symbiotic organisms search algorithms and applications

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.