Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss or/and maximizes the system reliability while satisfying operating constraints. In this paper, a distribution network reconfiguration method is presented for both the indices of power loss reduction and reliability improvement. The enhanced genetic optimization algorithm is used to handle the reconfiguration problem so as to determine the switch operation schemes. Based on the information of a single loop caused by closing a normally open switch, we improve the algorithm on crossover and mutation operations of original Genetic Algorithms. The effectiveness of the proposed method is demonstrated on 33-bus, 69-bus, and 136-bus radial distribution systems.     

基于特征频带暂态无功功率的配电网故障选线新方法

为解决小电流接地系统在相电压过零点附近特征频带选取结果不准确、保护装置采样不同步等问题,详细分析了零模网络下配电线路的相频特性,并据此提出一种基于特征频带暂态无功功率相关性检测的选线新方法。首先,对各出线暂态零模电流与母线差分零模电压进行交叉小波变换,将二者相关性较强且相位差稳定的区域作为故障选线的时频窗。然后,利用时频窗内暂态电压电流信号的幅值与相位信息计算得到暂态无功功率。最后,根据特征频带内故障线路暂态无功功率与非故障线路暂态无功功率流向相反的特点,利用互相关函数算法对各出线暂态无功功率的相关性进行检测,确定故障线路。理论分析和仿真结果表明,所提方法不受故障初始角、同步误差、噪声干扰等因素的影响,对高阻以及弧光接地具有较强的适应性,且适用于分布式电源接入的配电网单相接地故障选线。     

Grey Wolf Optimizer for Optimal Sizing and Siting of Energy Storage System in Electric Distribution Network

One of the most important issues that must be taken into consideration during the operation of distribution network is improving the network reliability. This objective can be achieved by connecting energy storage systems (ESSs) to the network; the correct size and location of these ESSs cause enhancing of the system reliability. This paper proposes an efficient methodology based on the Grey Wolf Optimizer (GWO) to determine the optimal size and location of ESSs in a distribution network so as to minimize the total annual cost of system comprising the cost of energy not supplied (ENS), the ESSs' investment costs and operating costs. The proposed methodology is applied to two radial distribution systems, a 30-bus, 11-kV system and a 69-bus, 12.6-kV system. The results obtained via the proposed GWO are compared to those obtained via classical approaches, dynamic programming (DP), and meta-heuristic algorithms (PSO). In the case of a 30-bus network, the total cost is saved by 14.12% from the base case, network without ESSs; and, in the case of a 69-bus system, the cost is saved by 39.03%. Additionally, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms are programmed and their results are compared with those obtained via the proposed GWO. The obtained locations and sizes of ESSs encourage the usage of the proposed methodology due to its ease and efficiency in solving the optimization problem under study.     

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 design of hybrid power generation system and its integration in the distribution network

The inability of conventional energy sources to fully meet the rapidly increasing energy demands in today’s world has led to the growing importance of hybrid power generation systems that incorporate renewable energy sources. This work proposes an optimally designed multi-source standalone hybrid generation system comprising of photovoltaic panels, wind turbine generators, batteries and diesel generator. This design aims at minimizing emissions and cost, expressed in the form of the Net Present Value (NPV) of the system, while simultaneously maximizing its Energy Index of Reliability (EIR). The designed hybrid power generation system is further integrated into the distribution system as a Distributed Generation (DG); this is to optimally improve the performance of the distribution system by minimizing the total losses and the total voltage deviation of the distribution system. The combined cost and emissions incurred due to the energy purchased from the grid and the energy generated by DG are also reduced. For this purpose an improvised Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed taking care of contradicting objectives. The proposed optimization algorithms are implemented using MATLAB for a standard IEEE 69-bus distribution system, using an hour-wise annual data of Spain. The location and size of DGs and the type and number of each generating source of the hybrid system are considered as decision variables. The effectiveness of the proposed optimal design using the improvised MOPSO algorithm is established in comparison with Improved Hybrid Optimization by Genetic Algorithm (i-HOGA) results.     

A hybrid optimization approach for distribution capacitor allocation considering varying load conditions

This work presents a new algorithm based on a combination of fuzzy (FUZ), Forward Update (FWD), and Genetic Algorithm (GA) approaches for capacitor allocation in distribution feeders. The problem formulation considers three distinct objectives related to total cost of energy loss and total cost of capacitors including the purchase and installation costs and one term related to total cost of produced power under peak load condition. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed methodology of this article uses an iterative optimization technique based on Forward Update approach which is embedded in a Genetic Algorithm framework. The fuzzy reasoning supported by the fuzzy set theory is used for sitting of capacitors and the GA is employed for finding the optimum shape of membership functions. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder along with a 34-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem.     

Swarm-Intelligence-Based Optimal Planning of Distributed Generators in Distribution Network for Minimizing Energy Loss

This research work proposes a heuristic approach for the planning of Distributed Generator (DG) to minimize annual system energy loss. The particle-swarm-optimization-based method has been used as an optimization tool for determining the optimal size and allocating the DGs to minimize energy loss. In this approach, time-varying characteristics of electrical load demand have been considered to mimic real load scenario in the electrical distribution system. The proposed approach is generic and simple. It can provide optimal solutions to the distribution utilities to select multiple DGs in stages under various constraints. The effectiveness of the proposed approach is validated on 16-, 33-, and 69-bus radial distribution networks. The results are compared with those of already existing methods as suggested in the literature.     

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.     

An improved load flow technique based on load current injection for modern distribution system

This paper presents load current injection based improved load flow (LF) technique for modern distribution system. The proposed LF technique is derived by promulgating the concept of conventional backward forward sweep technique. This proposed technique uses a single load current to bus voltage (LCBV) matrix to perform both the backward and the forward sweeps of power flow calculation in a single step. Utilizing the concept LCBV, the bus voltages can be determined, directly, from the load current injections. Due to the distinctive solution technique of the proposed approach, the time-consuming lower–upper factorization and forward–backward substitution or any derivative operation required in the traditional LF technique are no longer necessary. These unique features make the proposed technique faster in operation. The proposed approach is flexible enough to accommodate any sort of change in network structure due to reconfiguration. Along with the modeling of various equipments of distribution system (viz. distribution transformer, voltage regulator, voltage dependent loads, distributed generations, etc.), special treatments for weakly meshed system are also presented in this work. It is experimented on several test systems of varying complexities (viz. 28-, 69- and 30-bus balanced radial distribution system (RDS), 10-bus and IEEE 34-bus unbalanced RDS and 33- and 69-bus weakly meshed system) and the obtained results demonstrate the effectiveness of the proposed approach while comparing to the existing methodologies. Furthermore, it is revealed that the proposed algorithm is, computationally, faster, robust and more suitable for modern distribution network than the contemporary techniques available in the state-of-the-art literature.     

Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete dynamic model of an islanded microgrid was developed. From stability analysis, the study reports that both location of DGs and choice of droop coefficient have a significant effect on small signal stability, transient response of the system and network losses. The trade-off associated with the network loss and stability margin is further investigated by identifying the Pareto fronts for modified IEEE 13 bus, IEEE 33 and practical 22-bus radial distribution network with application of Reference point based Non-dominated Sorting Genetic Algorithm (R-NSGA). Results were validated by time domain simulations using MATLAB.     

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.     

考虑功率四象限输出的配电网储能优化配置策略

高渗透率分布式电源引起电压偏移和波动等电压质量问题,同时可引发配电网网损增加。针对这一问题,以配置储能为解决方式,并以提高配电网经济效益和提升电压质量为目标,结合电池储能系统功率四象限输出运行策略,提出了配电网储能选址定容的优化配置策略。构建了以购电成本、网损成本和投资成本之和最小为目标的配电网经济运行模型,以15 min为时间尺度滚动计算,并以典型日内的总成本最低建立储能选址定容模型,同时对各节点电压的偏移和波动范围设置约束,使用混合整数二阶锥数学规划进行优化求解。以IEEE 33节点配电网为例进行仿真验证,结果证明了所提出优化配置方法的可行性与高效性。     

Optimal installation of three-phase active power line conditioners in unbalanced distribution systems

In this paper, a new solution algorithm based on a multiple gradient summation (MGS) and differential evolution (DE) approach for optimal three-phase active power line conditioners (APLCs) installation in unbalanced distribution systems is proposed. The active power line conditioners installation problem considers the individual and total harmonic voltage distortions as well as the commercially available discrete sizes of the APLCs limits to minimize the total sizes of three-phase APLCs. The imbalance of systems resulting from using asymmetrical connection transformers was taken into account. The effectiveness of the proposed method was demonstrated by its application to a 23-bus unbalanced radial distribution system.     

Improved meta-heuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.     

Novel Clustering-based Hidden Markov Model (CHMM) optimization approach for optimal PMU placement

A Phasor Measurement Unit (PMU) is an important device for monitoring the wide-area power distribution network. Placement of the PMUs across the network enables reliable monitoring of the network to identify the faults in the bus system. Due to the increase in the installation cost of the PMUs, optimal placement of the PMU is a significant task. But the existing techniques do not provide the optimal solution for the PMU placement. To overcome this issue, this paper proposes a novel Clustering-based Hidden Markov Model (CHMM) optimization approach to achieve optimal placement of PMUs in the power distribution network. Optimal PMU placement is achieved by applying cluster formation in the bus system to extract the data with neighboring buses. Best optimal position for placing the PMU is estimated by using Fuzzy logic-based rale formation to update the binary table of the bus system. The HMM approach is used for updating weight in the cluster formation. Our system is implemented in various bus systems like IEEE 28-bus system, 69-bus system and also in Karnataka 155-bus system. The proposed approach is implemented in the IEEE bus system and Karnataka Power Transmission Corporation Limited (KPTCL) bus system and compared with the existing approaches, based on the total number of PMU placement. The proposed approach achieves better performance in the optimal placement of PMU than the existing optimization algorithms.     

A Radial Path Building Algorithm for Optimal Feeder Planning of Primary Distribution Networks Considering Reliability Assessment

Optimal feeder routing is an important part of general optimal distribution network planning. This article proposes a new algorithm for optimal feeder routing using an easy step-by-step, radial path building algorithm, ensuring minimization of the total system planning cost. Furthermore, reliability assessment is carried out to obtain the most reliable feeder routing to acquire less interrupted network structure with different feeder configurations. The proposed approach is also tested for optimal feeder routing with variations in the number of substations, which provides information on the trade-off between optimality and reliability of the system configuration. Moreover, the concept of principle of optimality is effectively used to make the proposed approach computationally more efficient and useful. The extensive test results reflect the potential ability of the proposed approach for optimizing the network structure in power distribution networks.     

含电动汽车和智能软开关的配电网动态重构

提出含分布式能源、智能软开关(SOP)和电动汽车(EV)有序接入的配电网重构策略。基于上班族的出行习惯模拟EV的无序充电模型,并构建配电网重构下的SOP模型;对于接入的EV无序充电负荷,采用拉格朗日松弛分散式优化算法和虚拟电价进行EV的有序调度;以最小化网损费用,SOP运行费用,弃风、弃光费用与开关动作费用之和为目标函数,通过大M法和二阶锥松弛将配电网重构模型转化为混合整数二阶锥规划模型,采用CPLEX求解器进行求解。IEEE 33节点标准系统的仿真结果表明,在配电网动态重构中采用SOP代替传统开关能够提升配电网运行的经济性,同时采用所提有序调度方法优化EV充电可以改善配电网电压质量。     

考虑运行策略和投资主体利益的电转气容量双层优化配置

电转气(P2G)技术的日益成熟,促进了电网和天然气网间的耦合,使两者间实现大规模互联成为可能。文中利用条件风险价值(CVaR)理论,对风电不确定性给电—气互联系统带来的运行风险及其成本进行了分析。在计及风力发电企业和电—气互联系统两个利益主体后,构建了P2G设备容量配置双层规划模型,以风力发电企业净利润作为上层目标,电—气互联系统运行成本为下层目标。并通过基于灾变遗传算法和内点法的混合求解算法进行仿真求解。利用IEEE 39节点电网和修改的比利时20节点天然气网组成的仿真系统,验证了配置P2G设备来提高风电消纳率和降低系统弃风风险的可行性。并进一步对比分析了置信度和弃风风险成本系数对P2G配置策略及系统运行的影响。     

Reliability planning for composite electric power systems

This paper proposes a reliability design approach using network flow technique, genetic algorithms, and Monte Carlo simulation for composite electric power systems. With increased emphasis on reliability design and cost control in electric power system planning and operation, particularly in composite electric power system, we are therefore striving to achieve an optimal reliability design solution under a reliability/cost implemented model. Because the floating-point representation is more efficient than the binary representation in genetic algorithms application, and the former also has more robust operators to locate near optimal solutions in most cases, we will employ the floating-point representation. The proposed method primarily finds out the optimal values of reliability indices for the components such that the objective function composed of interruption cost and installation cost is minimized. The reliability indices adopted include expected demand not served (EDNS) and forced outage rate (FOR). Application of the proposed method is demonstrated using a 23-bus test system.     

基于两种仿生算法的低碳主动配电网规划

分布式电源（DG）为未来配电网的规划问题带来了许多新的挑战。在传统配电网规划基本要求的基础上,结合主动配电网相关标准,对主动配电网进行了新的规划。规划过程中,将选择投入运行的电网网架和分布式电源出力大小作为优化变量,将电网的碳排放环境成本作为评判标准。以14节点网络系统为例,将遗传算法（GA）和布谷鸟搜索（CS）算法相结合,并根据电网规划实际情况对算法做了改进。最终在MATLAB环境下,结合MATPOWER潮流计算工具,实现了主动配电网的低碳规划。算例结果表明,该规划方法简单有效,能够为低碳经济下主动配电网的进一步发展提供参考和依据。