Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201

Voltage stability maximization based optimal network reconfiguration in distribution networks using integrated particle swarm optimization for marine power applications

1949-1956This paper addresses a novel method to optimize network reconfiguration problem in radial distribution network considering voltage stability maximization and power loss reduction without violating the system constraints. In nature inspired population based standard particle swarm optimization (PSO) technique, the flight path of current particle depends upon global best and particle best position. However, if the particle flies nearby to either of these positions, the guiding rule highly decreases and even vanishes. To resolve this problem and to find the global best position, integrated particle swarm optimization (IPSO) is utilized for finding the optimal reconfiguration in the radial distribution network. The performance and effectiveness of the method are validated through IEEE 33 and 69 buses distribution networks and is compared with other optimization techniques published in recent literature for optimizing network reconfiguration problem. The simulated results simulate the fact that to attain the global optima, IPSO requires less numbers of iterations as compared to the simple PSO. The present method facilitates the optimization of modern electric power systems by empowering them with voltage stability

Distribution systems optimization with computational intelligence algorithms

A dual particle swarm optimization - immune algorithm solution is presented in this paper to deal with the problem of optimum radial reconfiguration and reactive power compensation in distribution systems. The optimization problem uses as minimization function power losses in the distribution system – lines and transformers – and addresses constraints referring lower and upper voltage limits, nodal reactive power limits, topology supply constraints and the maximum number of capacitor banks. The analysis conducted for a pilot and a complex test system has proven the feasibility of the proposed method

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem

Distribution network reconfiguration in smart grid system using modified particle swarm optimization

One of the major characteristic of a smart protection system in Smart grid is to automatically reconfigure the network for operational conditions improvement or during emergency situations avoiding outage on one hand and ensuring power system reliability the other hand. This paper proposes a modified form of particle swarm optimization to identify the optimal configuration of distribution network effectively. The difference between the Modified Particle Swarm Optimization algorithms (MPSO) and the typical one is the filtered random selective search space for initial position, which is proposed to accelerate the algorithm for reaching the optimum solution. The main objective function is to minimize the power losses as it represents high waste of operational cost. The suggested method is tested on a 33 IEEE network using IPSA software. Results are compared to studies using other forms of swarm optimization algorithms such as the typical PSO and Binary PSO. 29% of losses reduction has been achieved during a less computational time

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF CAREER proposal are presented

A Comparative Study of Optimization Methods for 33kV Distribution Network Feeder Reconfiguration

Distribution Network Reconfiguration (DNR) has been a part of importance strategies in order to reduce the power losses in the electrical network system. Due to the increase of demand for the electricity and high cost maintenance, feeder reconfiguration has become more popular issue to discuss. In this paper, a comparative study has been made by using several optimization methods which are Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The objectives of this study are to compare the performance in terms of Power Losses Reduction (PLR), percentage of Voltage Profile Improvement (VPI), and Convergence Time (CT) while select the best method as a suggestion for future research. The programming has been simulated in MATLAB environment and IEEE 33-bus system is used for real testing. ABC method has shown the superior results in the analysis of two objectives function. The suggestion has been concluded and it is hoped to help the power system engineer in deciding a better feeder arrangement in the future

A Comparative Study of Optimization Methods for 33kV Distribution Network Feeder Reconfiguration

Distribution Network Reconfiguration (DNR) has been a part of importance strategies in order to reduce the power losses in the electrical network system. Due to the increase of demand for the electricity and high cost maintenance, feeder reconfiguration has become more popular issue to discuss. In this paper, a comparative study has been made by using several optimization methods which are Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The objectives of this study are to compare the performance in terms of Power Losses Reduction (PLR), percentage of Voltage Profile Improvement (VPI), and Convergence Time (CT) while select the best method as a suggestion for future research. The programming has been simulated in MATLAB environment and IEEE 33-bus system is used for real testing. ABC method has shown the superior results in the analysis of two objectives function. The suggestion has been concluded and it is hoped to help the power system engineer in deciding a better feeder arrangement in the future