Optimal Distribution Network Reconfiguration Implemented with Tie Line and Capacitor Using Improved Particle Swarm Optimization

The network reconfiguration technique has been used to reduce network losses, balance transformer loading, and restore the power supply of the outage area in distribution networks. With a large number of uncontrollable distributed generators (DGs) added to distribution networks, the reconfiguration technique can be applied to enhance the available delivery capability (ADC) of distribution networks. This paper presents a two-stage methodology for determining optimal network topologies for improving the ADC of distribution networks for supporting more renewable energies. The first stage is a group-based binary particles swarm optimization (BPSO), which takes advantage of the BPSO's global capability while improving its slowness in finding optimal solutions. The second stage is a hybrid of greedy-based and domain-knowledge-based methods. When applied to multiple scenarios of DGs, the proposed methodology can effectively deal with the uncertainty of the DGs' outputs and time-varying loading conditions. As a byproduct, the optimal network topology found by the proposed methodology also decreases the network power losses due to the increase of the ADC of the distribution networks. The IEEE 123-bus test network and a practical 1001-node distribution network are used to verify the proposed methodology, and the numerical studies demonstrate the effectiveness of the proposed methodology in greatly increasing the ADC via optimal network reconfigurations.

The advent of distributed generation (DG) units has posed many problems to the planning and operation of distribution networks (DS). The selection of the “best” site for the DG installation is a major problem in this context. Different strategies and methods have been developed in the recent years which deal with this problem. However, Complexity of solutions and inadequate manipulation have hindered their utilization by DS planners. This paper presents a robust and practical tool which helps the distribution network planner in getting the best possible location of DG unit taking into account both reliability of supply and voltage sag problems. The method suggested uses knowledge-base expert system (ES) to help in selecting the appropriate locations. The method suggested is based on economic and technical considerations. It investigates with economic loss due to both voltage sag and forced outage of DS components. It concerns with the effect of location of the added DG on the overall network performance during both steady-state when a forced outage occurring on one or more component of the DS and transients after sudden short-circuit. Using ES avoids mathematical complications of solving complex analytical model. By judicious rule-base ES, selection of optimum locations becomes clear and simple. The suggested method is explained and implemented through practical distribution network and its effectiveness is tested and discussed.

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current flow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value.

The worldwide increasing demand for electricity, coupled with governmental policy changes for green energy has led to significant interest in distributed generation (DG). Integrating DG into an electricity network, especially close to load centres, has many significant benefits but also brings with it many drawbacks such as voltage drop, and power losses. In this paper the impact of three different types of distributed generation (diesel generator, wind turbine and photovoltaic (PV)) on distribution networks' voltage profile and power losses is studied. NEPLAN software and the extended Newton-Raphson method have been used in the analysis. The obtained results show that different types of DG influence differently the distribution network and that their precise location and size are vital in reducing power losses and improving the voltage stability.

Optimal economic-driven planning of multiple DG and capacitor in distribution network considering different compensation coefficients in feeder’s failure rate evaluation

Smart grid technology has received ample attention in past years to develop the traditional power distribution network and to enable the integration of distributed generation units (DGs) to satisfy increasing demand loads and to improve network performance. In addition to DGs, integration of shunt capacitors (SCs) along with network reconfiguration can also play an important role in improving network performance. Besides, network reconfiguration can help to increase the distributed generation hosting capacity of the network. Some of the research in the literature have presented and discussed the problem of optimal integration of renewable DGs and SCs along with optimal network reconfiguration, while the network load variability and/or the intermittent nature of renewable DGs are neglected. For the work presented in this paper, the SHADE optimization algorithm along with the SOE reconfiguration method have been employed for solving the aforementioned optimization problem with consideration of uncertainty related to both the network load and the output power of the renewable DGs. Maximizing the hosting capacity (HC) of the DGs and reducing network power losses in addition to improving the voltage profile have been considered as optimization objectives. Five different case studies have been conducted considering 33-bus and 59-bus distribution networks. The obtained results validate the effectiveness and the superiority of the employed techniques for maximizing the HC up to 17% and reducing power losses up to 95%. Besides, the results also depict the effect of SC integration and the consideration of uncertainties on achieving the optimization objectives with realistic modeling of the optimization problem.

This paper presents optimal reconfiguration of radial distribution network with integration of distributed energy resources (DER) using improved particle swarm optimization (PSO) algorithm. The benefits of DER integration in distribution system are reducing power losses, improving voltage profiles and load factors, eliminating system upgrades, and reducing environmental impacts. However, the presence of DER could also cause technical problems in voltage quality and system protection. Reconfiguration of distribution network is aimed to minimize power loss and to improve voltage quality in order to enhance the distribution system performance. In this study, reconfiguration method is based on an improved PSO. The method has been tested in a 60-bus Bantul distribution network of Yogyakarta Special Region province, Indonesia. The simulation results show the importance of reconfiguring the network for enhancing the distribution system performance in the presence of DER.

With the setting up of regulatory authorities the cost pressure on the electricity distribution companies will increase further in Germany. Due to political reasons the distributed generation (DG) has increased. Therefore, nowadays many distribution network companies face two important tasks: reduction of network costs to improve the network efficiency and integration of the large-scale DG into existing systems. A long-term planning of the distribution networks can provide an optimal target network for the efficiency analysis of distribution networks and the evaluation of effects, which are caused by the DG. Planning of distribution networks without the DG is known as a non-linear mixed integer problem. Additional technical constraints (such as voltage rising effect, short-circuit current, etc.) and stochastic characteristics of the DG make the planning problem become more complex. In this paper we propose a new efficient two-stage heuristic method, which is based on large neighborhood search and guided local search, for the planning problem of distribution networks with the DG. Technical modeling and stochastic characteristics of the DG are described. Applications of the proposed method for the planning of real German distribution networks with large-scale DG show that distribution network companies can use this method, not only for planning the future optimal network structure but also for analyzing the effects, which are caused by different DG scenarios

Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems

In an islanded microgrid with multiple distributed generations (DGs), the difference in line impedance may cause local voltage deviation, which leads to a series of problems such as lower power allocation accuracy and bus voltage drop under traditional droop control. In this respect, a method for optimizing the droop control using an improved particle swarm optimization (IPSO) is proposed. Firstly, the microgrid structure and influence of line parameters on traditional droop control strategy is analyzed. Then, an improved particle swarm optimization is proposed. Based on the basic particle swarm optimization (PSO) algorithm, a fuzzy inference system (FIS) is introduced to dynamically adjust the particle swarm optimization, which can effectively improve the global search ability and local search ability of the algorithm. After that, the improved algorithm is applied to the droop controller, simultaneously, the range of stable operation of the system is determined by small signal analysis. Finally, the simulation and experiment results show that the proposed improved droop control strategy can achieve accurate allocation of active and reactive power effectively while maintaining bus voltage and system frequency stability, enhance the dynamic performance and transient stability of the microgrid system.

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various kinds of DGs has been suggested in the current job. In this job, the ideal power factor for DG supply has been acquired, both the active power as well as the reactive power. In the proposed approach, different types of distribution generation (DG) supply both reactive and real power. For the optimal placement of DG sources, particle swarm optimization techniques have been used in this job. Each of these innovations has its own strengths and drawbacks. Most of the methods that have been proposed so far to formulate DG’s optimum placement problem only consider Type-I DGs, Type-II and Type-III DGs that are considered for optimal position in the existing research. In the reference, artificial bee colony algorithm was used to determine sites of DGs and condenser combinations and optimal size. The author used PSO method in the reference to determine the appropriate positioning of the DG’s and to maximize the savings of power loss and voltage profile in the distribution network.

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

For the past few years, to reduce system power losses and maintain operating constraints, such as voltage stability, network reconfiguration has been crucial in determining the radial operating framework. Distributed generation (DG) is typically used to generate energy close to the site of consumption. This technology generates energy that is affordable, in contrast to conventional energy production. To lessen energy losses as well as boost voltage characteristics, the adopted methodology is centered on reconfiguration and DG distribution in the radial distribution network (RDN). In this work, the loss sensitivity factor (LSF) is used to determine the right DG position in RDN. After identifying the bus for DG positioning, the artificial rabbits optimization (ARO) technique is used to ascertain the ideal reconfigured network and DG size to lessen energy losses and enhance the voltage profile for RDN. The employed methodology is investigated on IEEE-33 and 69 RDN, respectively, for two cases of considering only reconfiguration without distributing units of DG and reconfiguration with an allotment of three DG units. The latter case showed better results compared to the case of only reconfiguration.

Dual-layer peer-to-peer energy trading method with multiple SOPs pricing in distribution networks