Particle swarm optimization technique To solve stochastic multi objective Power system optimization problems

Abstract

In developing countries electricity consumption is increasing rapidly newlineand there is always a mismatch between power generation and the load newlinedemand Utilization of available resources in an economic way has become newlineincreasingly important due to the increasing cost of generation transmission newlineand lack of funds for further expansion so economic dispatch of power to the newlinecommitted units subject to technical constraints is important Economic newlineDispatch problem can be solved by using either deterministic or stochastic newlineApproach The deterministic approach assumes exact knowledge of available newlinegeneration capacity and load newlineBut in real time operation there are many uncertainties and newlineinaccuracies present due to errors in load forecasting and measuring of input newlinedata which lead to deviation from original operating condition The newlineprobabilistic or stochastic approach on the other hand recognizes the effect of newlinerandomness in both load and available generation capacity This approach newlinerequires knowledge of the statistical characteristic factors such as mean newlinevariance covariance coefficient of variation correlation coefficient of the newlinevariables of interest So fixing production cost of power generated based on newlinethe cost calculated by the deterministic model is not accurate and may lead to newlineloss for the power generating utilities newline newline