17-05-2012, 02:08 PM
Economic load dispatch using particle swarm optimization
Economic load dispatch using PSO.doc (Size: 279.5 KB / Downloads: 88)
Introduction
Optimal power flow is a major tool in the power system world. As the name suggests, optimal power flows attempt to optimize the power system according to a specific function. This function is called the objective function and is generally minimized by the OPF program. The most common objective function is the sum of all production costs of the system; however other functions such as system losses may be used. The optimal power flow has been frequently solved using classical optimization methods. The constraints involved are the physical laws governing the power generation-transmission systems and
the operating limitations of the equipment. Conventional optimization methods are based on successive linearization’s using the first and the second derivatives of objective functions and their constraint as the search directions [1-4]. The conventional optimization methods
usually converge to a local minimum [5]. Recently, intelligence heuristic algorithms, such as genetic algorithm [6], evolutionary programming [7], have been proposed for solving the OPF problem,Particle swarm optimization (PSO) algorithm was developed through simulation of a simplified s system.
PSO an optimization tool
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr.Ebehart and Dr. Kennedy in 1995, inspired by social behaviour 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. The detailed information will be given in following sections. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. PSO has been successfully applied in many areas: function optimization, artificial neural network training, fuzzy system control, and other areas where GA can be applied.
Problem formulation
The most commonly used objective in the problem formulation is the minimization of the total cost of real power generation.
Applied to Economic load dispatch using PSO
When any optimization process is applied to the ELD problem some constraints are considered. In this work two different constraints are considered. Among them the equality constraint is summation of all the generating power must be equal to the load demand and the inequality constraint is the powers generated must be within the limit of maximum and minimum active power of each unit. The sequential steps of the proposed PSO method are given below.
Inequality constraints
If the power output of a generator for optimum operation of the system is less than a pre-specified value P min, the unit is not put on the bus bar because it is not possible to generate that low value of power from the unit hence the generator power P cannot be outside the range stated by the inequality P min ≤ P ≤ P max
Similarly the maximum and minimum reactive power generation of a source is limited the generator powers Pp cannot be outside the range stated by inequality,
Q p min ≤ Q P ≤ Q p max