Approach For Network Reconfiguration Research Articles

Network Reconfiguration for Power Loss Reduction on Distribution feeder Using Particle Swarm Optimisation: Case study of 11kv feeder in Ilorin, Nigeria

The distribution system is the largest segment of the electrical power system and the final stage in delivering electricity to consumers. It experiences power losses which could be technical or non-technical due to its radial configuration and connected components. Technical losses occur as energy dissipation in the form of heat in the system components, which is unavoidable. It affects the efficiency of the system and increases operational costs. Therefore, the reduction is essential for adequate power supply and improvement in utility revenue generation. This study focuses on reducing power losses on 11 kV Taiwo distribution feeder in Ilorin using the Network reconfiguration approach. The feeder was modelled, and its power losses were evaluated based on the Backward Forward Sweep method suitable for the radial network. The reconfigured network was optimised using the Particle Swarm Optimisation technique. The study shows that the values of feeder real power losses before and after reconfiguration are 176.883 kW and 121.972 kW, respectively.Further improvement was, however, achieved when PSO was used for the reconfiguration, as the power loss value stands at 98.465 kW. This is 44.33 % reduction compared to the initial power loss value and 19.273 % after reconfiguration. Furthermore, the values of the reactive power losses before and after reconfiguration are 9.474 kVar and 6.527 kVar, respectively, amounting to a 31.11 % reduction. However, PSO reduces the value to 3.101 kVar, which is 67.27 % of the initial value. Thus, radial distribution network reconfiguration using PSO has proven to be a robust method for reducing power loss in the distribution network.

An effective network reconfiguration approach of radial distribution system for loss minimization and voltage profile improvement

Distribution system represents a crucial portion of power system as it is the connection between the bulk power transmission network and the customers connected at load point. Distribution networks are increasing in size and are spread too far. Low reactance to resistance ratio and large size of network leads to higher power losses in lines and low node voltage at customer end. High power losses reduce the efficiency and affect the economy. Therefore, in this paper an algorithm for reconfiguration of distribution system for loss reduction and voltage profile enhancement is proposed. The proposed reconfiguration approach has been applied to 16 bus radial distribution system (RDS).

Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance.

Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras.

Network Reconfiguration for Minimizing Power Loss by Moth Swarm Algorithm

This paper presents a network reconfiguration approach for minimizing power loss of the distribution system based on moth swarm algorithm (MSA). The MSA is a recent metaheuristic inspired from the navigational technique of moths in the dark for finding food sources. For searching optimal solution, MSA used three different mechanisms of generating new solutions consisting of Lévy-flights, Gaussian walks and spiral flight. The effectiveness of MSA is validated on two distribution systems consisting of the 33-nodes and 69-nodes. The simulation results are compared to particle swarm optimization and other available approaches in the literature. The calculated results on the test systems show that MSA can be an effective and reliable tool for the NR problems.

A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization

Rapid load restoration after large-scale power system blackouts is of virtual importance to reduce economic losses. Network reconfiguration is an essential foundation of load restoration. Incorporating the preference for different objectives, a network reconfiguration approach based on preference-based multiobjective optimization is proposed for network reconfiguration schemes. On the one hand, considering the influences of generators, lines and loads, three evaluation indicators are proposed as objectives to establish a preference multiobjective optimization model. On the other hand, a preference-based discrete nondominated sorting genetic algorithm II (PD-NSGA-II) is designed considering the preference and high discreteness of the suggested model. For efficient decision making, the algorithm is equipped with two sorts of population and two dominance relations to obtain solutions with required quantity and high quality. The simulation results demonstrate that the preference-based multiobjective optimization can reasonably leverage the tradeoff among different factors about network reconfiguration. Furthermore, comparison with other algorithms indicates the efficiency of PD-NSGA-II in solving network reconfiguration optimization problems.

Multi‐objective approach to maximise loadability of distribution networks by simultaneous reconfiguration and allocation of distributed energy resources

In recent years, continuously increasing load demand and deficiency of capital resources vis-a-vis a competitive electricity market have forced transmission and distribution utilities worldwide to maximise the efficiency and utilisation of their existing infrastructure. This study presents a multi-objective approach to maximise the loadability of distribution networks by simultaneous reconfiguration and optimal allocation of distributed energy resources using a comprehensive teaching-learning-based optimisation algorithm. The proposed technique is based on the e-constraints method and uses a graphical approach for network reconfiguration. Loadability enhancement is validated on the IEEE 33-bus and 69-bus radial distribution systems. Several case studies are carried out to demonstrate the effectiveness of the proposed approach in reducing the network active power losses, improving the kVA loading margins and enhancing the voltage profiles while considering voltage and thermal limit constraints. Results show that maximum loadability is obtained with simultaneous network reconfiguration and multiple DER allocation with varying power factors.

The potential savings of SDN compliant sub-wavelength switching as a future carrier grade solution for the metropolitan network

Metropolitan area networks are currently challenged by a sharp increase of peak traffic demands, and would thus benefit from flexible network mechanisms able to ensure excellent Quality of Service (QoS) while limiting costs. In this paper, we evaluate the gain in Capital and Operational Expenditures (CAPEX and OPEX) brought by Time-domain Wavelength Interleaved Network (TWIN) thanks to a dynamic control plane implemented in a Software Defined Network (SDN) framework. The SDN compliant TWIN control plane attributes resources and activates the transmitters (Tx), receivers (Rx) and transceivers (TRx) according to traffic variations. The present paper first recaps how to dimension TWIN in terms of Tx, Rx or TRx to support a given traffic matrix. It is then shown that 20% resource over-provisioning allows to meet the metro level QoS requirements set by the Metro Ethernet Forum (MEF). Then, taking into account this over-provisioning value, a comparative study, assessing the OPEX and CAPEX savings for different configuration periods' duration (varying between 5 min and 6 h) is carried out. The obtained results show that the dynamic network re-configuration approach can bring some CAPEX savings and highly significant OPEX savings in a realistic metropolitan network scenario, even for rather large configuration periods (e.g. 1 h). Finally, using a control plane based on this configuration period, the CAPEX and OPEX for TWIN are compared with those for the current Optical Circuit Switching (OCS) transport technologies (both opaque and transparent). It is shown that TWIN CAPEX/OPEX savings are important even if the TWIN's Tx/Rx/TRx unit expenditure cost is many times more expensive than that of OCS.

A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm

This study aimed to minimize energy losses in traditional distribution networks and microgrids through a network reconfiguration and phase balancing approach. To address this problem, an algorithm composed of a multi-objective function and operation constraints is proposed. Network connection matrices based on graph theory and the backward/forward sweep method are used to analyze power flow. A minimizing energy loss approach is developed for network reconfiguration and phase balancing, and the particle swarm optimization (PSO) algorithm is adopted to solve this optimal combination problem. The proposed approach is tested on the IEEE 37-bus test system and the first outdoor microgrid test bed established by the Institute of Nuclear Energy Research (INER) in Taiwan. Simulation results demonstrate that the proposed two-stage approach can be applied in network reconfiguration to minimize energy loss.

Hierarchical Decentralized Network Reconfiguration for Smart Distribution Systems—Part II: Applications to Test Systems

A hierarchical decentralized network reconfiguration approach to minimize power losses for smart distribution systems was presented in Part I. In this paper, the proposed approach is applied to four test distribution systems to examine its performance. A demonstration system consisting of the test system and distributed intelligent agents is built using MATLAB/Simulink to illustrate how the decentralized approach is implemented. Simulation results of the proposed approach are then compared with results of a centralized implementation and the harmony search method. It is shown that the decentralized approach can achieve similar results as other methods with significantly reduced computation time. The impacts of time-varying loads and faults are also studied through dynamic network reconfiguration on the 118-bus test system. Simulation results illustrate that dynamic network reconfiguration with time-ahead planning can determine the optimal configuration for each operating period to significantly reduce system energy losses. The enhanced performance of the hierarchical decentralized approach is clearly established.

A fuzzy‐tuned genetic algorithm for optimal reconfigurations of radial distribution network

AbstractA large amount of power can be saved by optimal reconfiguration of radial distribution systems (RDS). Moreover, by transferring loads from the strongly loaded feeders to the lightly loaded ones, the reconfiguration of RDS can also achieve balancing of feeder loads and alleviate overload conditions. This paper presents a new approach for optimal reconfiguration of RDS based on a fuzzy‐tuned genetic algorithm by minimizing real power loss and improving loading pattern of the feeder. The proposed algorithm overcomes the combinatorial nature of the reconfiguration problem and deals with the optimization of a non‐linear, non‐continuous objective function. The main features of the proposed genetic approach for network reconfiguration are: preserving radial property of the network without islanding any load point by an elegant coding scheme and an efficient convergence characteristic attributed to tuned mutation using fuzzy logic. The proposed algorithm is tested for seven case studies on a 69‐bus RDS. Copyright © 2006 John Wiley & Sons, Ltd.

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration.

A geometrical approach for network reconfiguration based loss minimization in distribution systems

Network reconfiguration is one of the feasible methods for reducing the distribution network loss in which the power flow in the distribution network is altered by opening or closing the appropriate switches on the feeders. A geometrical approach for loss minimization is presented in this paper. In this method, each loop in a network is represented as a circle, which is derived from the relationship between the change of loss due to the branch-exchange and the power-flows in the branches. If there is no change of loss in the system, then all the circles touch each other at the (0,0) coordinate and the circles with no loss-change are called zero loss-change circles. The maximum loss-reduction loop in the network is identified by comparing the radii of all the zero loss-change circles. The corresponding loop of the largest zero loss-change circle gives the maximum loss reduction in the network. Then the possible branch-exchanges in the maximum loss-reduction loop are investigated by comparing the size of the circle for every branch-exchange. If the power losses are reduced due to a branch exchange, the size of the circle diminishes and hence the smallest circle gives the maximum loss-reduction and the corresponding branch-exchange is considered to be the best candidate for maximum loss-reduction. The performance of the proposed technique is tested on a 69-bus distribution system, and test results show that the method is found to reduce the computational effort and time considerably by reducing the numerous load-flow studies as compared to the method of Baran and Wu [IEEE Trans. Power Delivery, 4(2) (1989) 1401).

A Novel Approach for Network Reconfiguration Based Load Balancing in Distribution Networks

Network reconfiguration for load balancing is the determination of switching-options for a particular set of loads on a distribution system that improves load balancing the most and is performed altering the topological structure of distribution feeders by changing open/closed status of sectionalizing and tie-switches. By transferring loads from the heavily loaded feeders to the lightly loaded ones, network reconfiguration can balance feeder loads and alleviate overload conditions of a network. In this paper a novel method is proposed by formulating an algorithm to reconfigure distribution networks for load balancing. Systematic logical approach is used to determine the feasible switching combinations, select the status of the switches, and find the best combination of switches for maximum improvement in load balancing. The algorithm has been developed in two stages. In the first stage, a limited number of switching combinations is formed by trinary logic principle. A simultaneous switching approach is applied logically to find the best combination by selecting the appropriate switching options, connecting the switches accordingly, and assigning the loads for the newly formed combinations. In the next stage, the search is extended to investigate whether any other switching combination gives rise to maximum improvement in load balancing, compared to the improvement obtained in the first stage. This is carried out by considering one after another, the branches next to the open-branches of the best configuration, and the switching configuration that gives the maximum improvement in load balancing is identified. The proposed method has been tested on a 69-bus test system, and the test results indicate that the algorithm proposed is able to determine the appropriate switching-options of the optimal (or near optimal) configuration with less computation. This paper proposes a scheme to implement the simultaneous switching algorithm for real-time on-line control of three-phase distribution networks, with symmetrical loading per phase.