Economic Load Dispatch with Renewable Uncertainties using PSO

In electrical power generation, our main aim is to generate the required amount of power with optimum cost. Due to increase in worldwide energy requirement and environmental issues like emission from fossil fuel, researchers have led to focus on the development of non-conventional energy resources. Renewable energy sources are good option to overcome the problem of conventional method of electricity generation. However, the major challenges with renewable energy sources are their nature of variability and uncertainty. The availability of renewable energy sources like solar and wind power is mainly depends on the weather conditions which are variable in nature. The uncertainties and variabilities of these renewable energy resources should be minimized for dispatching the power to the consumer side. In this proposed work, Economic Load Dispatch (ELD) problem is formulated incorporating wind and solar cost function. The first priority is given to renewable energy resources to supply the demand and after the utilization of whole renewable energy, the thermal plant supply remaining demand. The variability and uncertainty of renewable power incurs additional cost, termed as penalty cost. The effect of penalty cost on the total cost function has been shown in this work with the help of modified (with solar and wind sources) IEEE 30 bus system. Particle Swarm Optimization (PSO) algorithm is applied to allocate the thermal generating unit to fulfill the demand at minimum operating cost.