Reconfiguration Of Electrical Distribution Systems Research Articles

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

Restoration of the service and loss minimization in electrical distribution systems

<p>Restoration of service during faults to healthy zones and the minimization of true or real power losses are the main aspects of the satisfactory operation of an electrical distribution system (EDS). These objectives can be achieved with artificially intelligent optimisation techniques through electrical distribution system reconfiguration. The restoration of service is an important part of ensuring a stable power supply. If there is a fault with the EDS, the damaged zone is separated, and the healthy system is restored without breaching limitations. In the distribution system, ohmic losses are 25-35%. It is essential to reconfigure the system toreduce these losses. In this paper, a hybrid artificial rabbits’optimisation and improved mayfly optimisation (ARO-IMO) technique is adopted for restoration of serviceand loss minimization. The method being suggested will be carried out on an IEEE 4-feeder network for restoration of service and on IEEE 33 and69 radial distribution networks (RDN) for diminution of losses.</p>

Distribution grid reconfiguration sensitive to transmission cost allocation

AbstractThe reconfiguration of electrical distribution systems has been addressed in numerous scientific studies. Thus, several techniques and models are considered, especially to reduce electrical losses and improve the voltage profile. However, it is clear that even with the increase in research, which has been accentuated since the last decade, the locational pricing and the transmission costs allocation is little explored. In this regard, the proposed method is a distribution system reconfiguration (DSR) optimization problem focused on minimizing the use of the transmission system. In order to validate the meta‐heuristic employed for the DSR, the IEEE 33 and 69 node test systems are used as a proof of concept. In addition, case studies with four substations, three different transmission cost allocation techniques, and transmission system capacity scenarios are analyzed. Numerical results showed an important transmission expansion deferral with consequent decrease in overall costs compared to the classic DSR. The transmission network use of system (TNUoS) charges can play a crucial role in reducing the total cost of the electrical system, promoting distributors to change their feeder patterns so that a larger share of loads access substations with lower charges.

Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints

Natural disasters have great destructive power and can potentially wipe out great lengths of power lines. A resilient grid can recover from adverse conditions and restore service quickly. Therefore, the present work proposes a novel methodology to reconfigure power grids through graph theory after an extreme event. The least-cost solution through a minimum spanning tree (MST) with a radial topology that connects all grid users is identified. To this end, the authors have developed an iterative minimum-path heuristic algorithm. The optimal location of transformers and maintenance holes in the grid is obtained with the modified Prim algorithm, and the Greedy algorithm complements the process. The span distance and capacity restrictions define the transformer’s number, where larger spans and capacities reduce the number of components in the grid. The performance of the procedure has been tested in the urban zone Quito Tenis of Ecuador, and the algorithm proved to be scalable. Grid reconfiguration is pushed through a powerful tool to model distribution systems such as CYMDIST, where the voltage drops were minor than 3.5%.

Optimal Reconfiguration of Electrical Distribution System by Whale Optimization Algorithm

The prime motto of the electrical power system is to provide the good and high quality power to the consumers. As the life in the society is expanding hugely, hence the need of the electrical power is additionally expanding suggestively. In this manner expanding the power generation as well as beating the significant issues in the electrical distribution system has turned into a test. The strange conditions can't be normal however when happened; the recuperation ought to be made as quickly as time permits. In this work, a modern artificial intelligence based algorithm is implemented for the reconfiguration of an electrical radial distribution network. This algorithm helps to bring down the active power loss and intensify the voltage profile of the network. This paper has proposed a nature-based guided metaheuristic Whale Optimization Algorithm (WOA). WOA is motivated by the smart foraging approach of the humpback whales. To ratify the efficiency of the proposed approach, WOA is successfully simulated on IEEE standard 69 bus and 119 bus system.

An Adaptive Approach for PEVs Charging Management and Reconfiguration of Electrical Distribution System Penetrated by Renewables

An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs' drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control

In this study, the structure of an electrical distribution network, which includes several electrical distribution feeders, is adaptively and dynamically reconfigured to minimise the daily operation cost of system that is under control of a local distribution company. Herein, for the first time, stochastic model predictive control (MPC) concept is applied in the problem to have dynamic and adaptability futures in the optimisation problem and to deal with the uncertainty and variability issues of renewable energy resources. The daily operation cost of distribution system comprises hourly energy loss cost of electrical feeders and switching cost of initially open and close switches installed on each feeder. The numerical study shows the existence of notable potential in reconfiguration of distribution system for energy loss and operation cost reduction. In addition, it is demonstrated that applying MPC in the optimal reconfiguration of distribution system results in better consequences in the presence of variable power of renewables. Moreover, it is proven that application of MPC in the problem increases the robustness and resiliency of optimisation procedure with respect to the prediction errors due to dynamic and adaptability characteristics of MPC.

Distribution Systems Reconfiguration for Voltage Stability Maximization by using Artificial Immune Systems

This paper proposes a methodology for reconfiguration of Electrical Distribution Systems (EDS) to maximize the system voltage stability. The proposed approach is based on the metaheuristic and bioinspired optimization technique called Artificial Immune System (AIS) and stability indexes. The optimization problem presents operational and network constraints, as well as the radial and connected operation and the nodal voltage limits. Comparisons between two indexes are made for evaluate their impact on the reconfiguration problem. Three case studies are presented to assess the proposed approach and the stability indexes.

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even non-commensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multi-objective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.

Optimal reconfiguration of distribution systems with representation of uncertainties through interval analysis

The present work presents an approach for optimal reconfiguration of electrical distribution systems (EDS) to minimize energy losses considering uncertainties in the load demand and in the wind based distributed generation (DG). The optimization algorithm applied to solve the reconfiguration problem is based on the bio-inspired metaheuristic Artificial Immune Systems (AIS). An interval power flow model is used to obtain an interval energy loss from the representation of the uncertainties. The interval loss is used to guide the AIS algorithm through the search space. Network and operational constraints as the radiality and connectivity of the network as well as different load levels are considered. Well-known test systems are used to assess the impact of the uncertainties representation in the reconfiguration problem.

Robust optimisation applied to the reconfiguration of distribution systems with reliability constraints

This study presents a mixed-integer second-order conic programming (MISOCP) model for the robust reconfiguration of electrical distribution systems, considering the minimisation of active power losses and reliability constraints. The uncertainty at the reliability data is considered by using a linear and adjustable robust approach. The indices used to evaluate the reliability of the system are the system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI), both of which are calculated as functions of the switch status (open or closed). Under radiality constraints, the solution generated by the proposed model is robust in terms of the reliability, i.e. the SADI and SAIFI limits imposed by regulators are not violated even if the uncertain data changes stochastically. The use of an MISOCP model guarantees convergence to optimality by using existing convex optimisation solvers. To evaluate the robustness of each solution generated by the proposed model, a set of Monte Carlo simulations were deployed. Finally, all tests were executed in a 136-node real distribution system.

Optimal reconfiguration of electrical distribution systems considering reliability indices improvement

Abstract This paper presents a mixed-integer second-order conic programming (MISOCP) model to solve the reconfiguration problem of electrical distribution systems, considering the simultaneous minimization of total active power losses and improvement of customer-oriented reliability indices. The reliability indices considered in this paper are the system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI), and the energy not supplied (ENS). Under radiality, the proposed model satisfies the operational constraints of the reconfiguration problem, i.e., the voltage magnitude limits of the nodes and the current capacities of the conductors are not violated. The use of an MISOCP model guarantees convergence to optimality via convex optimization software tools. A multi-objective optimization approach is used to generate a full Pareto front surface that shows the conflict between the active power loss minimization and the improvement of the reliability indices in the reconfiguration problem. Finally, in order to test and verify the proposed methodology, a 43-node test system and a real 136-node system were employed.

Considering safety issues in minimum losses reconfiguration for MV distribution networks

AbstractThis paper offers a new perspective over the traditional problem of the multiobjective optimal reconfiguration of electrical distribution systems in regular working state. The issue is indeed here formulated including also safety issues. Indeed, dimensioning the earth electrodes of their own secondary substations, distribution companies take into account the probable future configurations of the network due to transformations of overhead lines into cable lines or realization of new lines. On the contrary, they do not consider that, during normal working conditions, the structure of the network can be modified for long periods as a consequence of reconfiguration manoeuvres, with differences between the design current of the earthing systems and the fault current in certain substations significant. As a consequence, often distribution companies limit the implementation of the optimal reconfiguration layouts because they are unable to suitably evaluate the safety issue. In the paper, the problem is formulated including a further objective in order to account for the safety. A suitable constrained multiobjective formulation of the reconfiguration problem is therefore used aiming at: the minimal power losses operation, the verification of safety at distribution substations, the load balancing among the HV/MV transformers while keeping the voltage profile regular. The application carried out uses an NSGA‐II algorithm whose performance is compared to that of a fuzzy logic‐based multiobjective evolutionary algorithm. In the considered automated network, the remote control of tie‐switches is possible and their layout is the optimization variable. After a brief description of the optimal reconfiguration problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, test results on a large MV distribution network are reported and discussed. Copyright © 2008 John Wiley & Sons, Ltd.