Shiv P. Singh

Bio: Shiv P. Singh is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: AC power & Electric power system. The author has an hindex of 18, co-authored 75 publications receiving 1333 citations. Previous affiliations of Shiv P. Singh include University of Agriculture, Faisalabad & Banaras Hindu University.

Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Abstract: Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39 -bus New England system.

Optimal Sizing of Distributed Generation Placed on Radial Distribution Systems

Abstract: Distribution network planning identifies the least cost network investment that satisfies load growth requirements without violating any system and operational constraints. Power injections from distributed generation change network power flows, modifying energy losses. Determining appropriate location and optimal size of distributed generation with respect to network configuration and load distribution in the feeder is main challenge in the changing regulatory and economic scenarios. Among the benefits of distributed generation is the reduction in active power losses, which can improve the system performance; reliability, and efficiency. In this article, the multi-location distributed generation placement problem aims to minimize the total active power loss of radial distribution networks using a genetic algorithm based solution algorithm. This technical benefit of energy savings due to the reduction in active power loss can also be translated into economic benefits. The loss sensitivity to the ...

Allocation of Optimal Distributed Generation using GA for Minimum System Losses

Abstract: The distributed generation (DG) is one of the viable options for mitigation of problems of load growth, overloading of lines, quality of supply and reliability in tern extending equipment maintenance intervals and to reduce line losses. However, the line loss reduction is the obvious parameter easily expressible in terms of system parameters. Therefore, this paper aims to minimize active power loss by placing DG strategically in a radial distribution system. The problem is formulated as an optimization problem and solution is obtained using genetic algorithm (GA). The strategic locations are decided on the basis of loss sensitivity to active power injection at various nodes. This approach helps in reducing the computational efforts of selecting appropriate location(s). The performance of the method is tested on 33-bus test system and comparison of the results with a reported method reveals that the proposed method yields superior results. In addition, long term economic benefit of optimal DG placement is also demonstrated. Keywords: distributed generation, line loss reduction, optimal location, radial distribution networks.

Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research

Abstract: The integration of distributed generation (DG) units in power distribution networks has become increasingly important in recent years. The aim of the optimal DG placement (ODGP) is to provide the best locations and sizes of DGs to optimize electrical distribution network operation and planning taking into account DG capacity constraints. Several models and methods have been suggested for the solution of the ODGP problem. This paper presents an overview of the state of the art models and methods applied to the ODGP problem, analyzing and classifying current and future research trends in this field.

Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm

Abstract: Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.