The economic load dispatch plays an important role in the operation of power system, and several models by using different techniques have been used to solve these problems. Several traditional approaches like dynamic programming, lambda-iteration and gradient method are utilized to find out the optimal solution of non-linear problem. More recently, the soft computing techniques have received m...

In order to maintain the stability and security of the power system, the uncertainty and intermittency of wind power must be taken into account in economic dispatch (ED) problems. In this paper, a dynamic economic dispatch (DED) model based on chance constrained programming is presented and an improved particle swarm optimization (PSO) approach is proposed to solve the problem. Wind power is re...

This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. ...

We develop a general framework for pricing electricity in order to optimally manage the electricity load of societal infrastructures that interact with power systems through their price-responsive electricity load. In such infrastructure systems, electricity is not the sole resource needed to serve users’ needs. Examples include cloud computing infrastructure or electric transportation networks...

Direct Search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require information about the gradient of the objective function while searching for an optimum solution. One of such methods is Pattern Search (PS) algorithm. This study examines the usefulness of a constrained pattern search algorithm to solve well-known power system Econo...

A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years due to its flexibility and the ability to avoid reaching local optima. This optimization approach has become a candidate approach for many optimization problems. Unfortunately, this attractive algorithm suffers several downsides including stagnation and slow convergence t...

In this paper, real-coded genetic algorithm with smart mutation (RCGA-SM)is proposed to solve the economic dispatch (ED) problem. In the proposed method, the required controllingprocess is accomplished on the total amount of chromosomes and consequently there is no need to use penalty cost function for controlling sum of variables in solving economic dispatch problem. This method will begin to ...

Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) an...

In this paper, Stochastic Economic Load Dispatch problem with Multiple Fuels is solved using Pareto distribution with local convex optimization by bacterial foraging approach. Bacterial foraging optimization is swarmed intelligence technique used to solve the problem of the power system. The algorithm is based on the group foraging behavior of Escherichia coli (E-coli) bacteria present in the h...

This paper presents a comparative study of classical method and soft computing optimization algorithm applied to solve Economic Load Dispatch problem with non-smooth fuel cost curves considering transmission losses, power balance and capacity constraints. The soft computing algorithm varies from the classical method in terms of the following basic factors. The genetic algorithm based approach p...