No abstract

Applications of distributed generation (DG) units is increasing in modern power systems and smart grids. An important issue with DGs is their optimal location and sizing within the distribution system. In this paper, a particle swarm optimization algorithm (PSO) is proposed for optimal placement and sizing of DG units in order to reduce network congestion and minimize locational marginal price (LMP) differences between various buses. The objectives include improvement of voltage profile and minimization of the investment and operation costs. The IEEE 14-bus test system is used to illustrate the effectiveness of the proposed GA with LMP-based objective function in relieving congestion and increasing total system benefit while reducing generation cost and improving voltage profiles. Main contributions are the inclusion of congestion management and social welfare in the DG placement/sizing problem considering non-radial (loop) networks. Somayeh Hajforooas received her B.S. and MS degrees in Electrical andComputer Engineering from Ferdowsi University, Mashhad, Iran and Islamic Azad University, South Tehran Brach, Iran, in 2005 and 2010, respectively. Her research interests include nonlinear and adaptive control, power system modeling, control and optimization. Mohammad A.S. Masoum (S'88-M'91-SM'05) received his B.S., M.S.

This paper presents a genetic algorithm (GA) to maximize total system social welfare and alleviate congestion by best placement and sizing of TCSC device, in a double-sided auction market. To introduce more accurate modeling, the valve loading effects is incorporated to the conventional quadratic smooth generator cost curves. By adding the valve point effect, the model presents nondifferentiable and nonconvex regions that challenge most gradient-based optimization algorithms. In addition, quadratic consumer benefit functions integrated in the objective function to guarantee that locational marginal prices charged at the demand buses is less than or equal to DisCos benefit, earned by selling that power to retail customers. The proposed approach makes use of the genetic algorithm to optimal schedule GenCos, DisCos and TCSC location and size, while the Newton-Raphson algorithm minimizes the mismatch of the power flow equations. Simulation results on the modified IEEE 14-bus and 30-bus test systems (with/without line flow constraints, before and after the compensation) are used to examine the impact of TCSC on the total system social welfare improvement. Several cases are considered to test and validate the consistency of detecting best solutions. Simulation results are compared to solutions obtained by sequential quadratic programming (SQP) approaches

The aim of this paper is presenting a genetic algorithm based method for congestion management and to maximize social welfare using one unit Static Synchronous Series Compensator (SSSC) in a double auction pool market based power systems. The aims are achieved by optimal locating and sizing one SSSC unit. In this paper, the GenCos cost functions are considered to be the quadratic form. Simulation outcomes on the modified IEEE 14 bus test system are used to show the impact of SSSC unit on the congestion management and social welfare maximization. Inclusion of the benefit functions of customer in the objective function and the utilization of a GA based algorithm for optimal locating/sizing of SSSC to guarantee fast convergence to the global optimal solution are the main contributions.

Congestion cost allocation is an important issue in congestion management. This paper presents a genetic algorithm (GA) to determine the optimal generation levels in a deregulated market. The main issue is congestion in lines, which limits transfer capability of a system with available generation capacity. Nodal pricing method is used to determine locational marginal price (LMP) of each generator at each bus. Simulation results based on the proposed GA and the Power World Simulator software is presented and compared for the IEEE 30-bus test system.