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.     

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.     

Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type

This paper proposes a new long term scheduling for optimal allocation and sizing of different types of Distributed Generation (DG) units in the distribution networks in order to minimize power losses. The optimization process is implemented by continuously changing the load of the system in the planning time horizon. In order to make the analysis more practical, the loads are linearly changed in small steps of 1% from 50% to 150% of the actual value. In each load step, the optimal size and location for different types of DG units are evaluated. The proposed approach will help the distribution network operators (DNOs) to have a long term planning for the optimal management of DG units and reach the maximum efficiency. On the other hand, since the optimization process is implemented for the entire time period, the short term scheduling is also possible. The proposed method is applied to IEEE 33-bus test system using both the analytical approach and particle swarm optimization (PSO) algorithm. The simulation results show the effectiveness and acceptable performance of the proposed method.     

An efficient approach for the siting and sizing problem of distributed generation

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time.     

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.     

基于纵横交叉算法的波浪发电装置最大功率跟踪控制

针对波浪能最大功率点跟踪控制中,浮子水动力模型的非线性,使传统群智能算法存在局部最优问题,提出纵横交叉算法(CSO)控制方案。CSO的纵向交叉算子,在纵向交叉概率判定下进行个体维变量间的算术交叉,保证种群能够脱离局部最优状态; CSO的横向交叉算子完成个体间的随机配对与算术交叉,并将解空间全体分割成若干个子空间,每个子空间以配对个体为对角顶点,搜索子空间内部及邻域,实现精细的局部搜索能力。通过纵、横交叉算子的交替作用,任何有益于实现全局最优的信息,都将被迅速地分发到种群的各变量中,用以改变搜索路径。仿真表明,在波浪周期发生变化时,纵横交叉算法能够实现最大功率点跟踪,并提高收敛速度。     

Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique

This paper attempts to minimize power losses in radial distribution networks and facilitates an enhancement in bus voltage profile by determining optimal locations, optimally sized distributed generators and shunt capacitors by hybrid Harmony Search Algorithm approach. To overcome the drawback of premature and slow convergence of Harmony Search Algorithm (HSA) over multi model fitness landscape, the Particle Artificial Bee Colony algorithm (PABC) is utilized to enhance the harmony memory vector. In the first approach, the formulation echoes the determination of loss sensitivity factor to decide the sensitive nodes and thereafter decides on the optimal rating through the use of hybrid Algorithm. The second approach encircles the role of hybrid Algorithm to search for both the optimal candidate nodes and sizing of compensating devices by significant increase in loss reduction with the former approach. The procedure travels to examine the robustness of the proposed hybrid approach on 33 and 119 node test systems and the result outcomes are compared with the other techniques existing in the literature. The simulation results reveal the efficiency of the proposed hybrid algorithm in obtaining optimal solution for simultaneous placement of distributed generators and shunt capacitors in distribution networks.     

含分布式电源的配电网分区和主导节点选取方法研究

为了应对分布式电源及柔性负荷大规模接入给配电网电压控制带来的困难,提出一种基于改进的粒子群优化算法的配电网分区方法,通过分区使分布式电源主动参与到配电网的控制过程中。引入评价分区结果优劣的四个指标,综合考虑分区内无功/有功储备、区域内强耦合和区域间弱耦合,并结合无功/有功可控性和电压可观性,构建主导节点选取公式。该方法将分区转化为优化求解问题,使得分区结果能根据分布式电源运行参数和配电网结构的变化进行灵活调整。通过仿真证明了该分区科学合理,在该方法基础上的分布式电压控制能有效缓解柔性负荷大规模接入所带来的节点电压越限等问题,具有实际应用价值。     

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids utilizing renewable energy resources

Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net Present Value (NPV) is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme.     

Feasible method for making controlled intentional islanding of microgrids based on the modified shuffled frog leap algorithm

Intentional islanding is a feasible solution to improve the reliability of the smart distribution system with distributed generations (DGs) when the electrical connections between the smart distribution system and upstream network are lost. In this paper, a heuristic method is proposed for the intentional islanding of microgrids. In this method, some practical and important factors such as reduction of problem solution space; load controllability; load priority; bus voltage; line capacity constraints; and the ability to construct larger islands by the combination of islands are taken into account. The proposed method is a two-stage method. In the first stage, the intentional islanding problem is relaxed and in the second stage, the feasibility of the solution is verified. In the first stage, the intentional islanding problem is assumed as a series of tree knapsack problems (TKPs) and solved by the modified shuffled frog leap algorithm (SFLA). In the second stage, the power flow calculation is carried out to check the feasibility of the islands and essential modifications are provided. The proposed method is applied to IEEE 69-bus test system with 6 DGs. The results are compared with other methods and the effects of different methods on the system reliability indices are discussed. These comparisons indicate that the proposed method is feasible and valid.     

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.     

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。     

基于遗传算法的小水电站优化调度方法的研究与实践

针对以发电为主的小水电站单库和串联小水电站群,以水电站的发电引用流量为决策变量,以水电站在调度周期内发电量最大为目标,分别建立了优化调度的数学模型。基于浮点数编码的改进遗传算法用于对模型的优化计算,从而提高了算法的搜索效率。基于VC 编程设计了小水电站运行调度智能算法系统软件,用类CbestGA封装了求解一般水库调度问题的遗传算法,并应用于一个实际的小水电站调度,实验结果说明了遗传算法用于水库优化调度的可行性和有效性。