OPTIMAL SCHEDULING OF HYBRID ENERGY SYSTEMS USING LOAD AND RENEWABLE RESOURCES FORECAST

In this paper, the integration of an optimizer and a forecaster into the energy management system (EMS) of a hybrid renewable energy system is studied. The role of the optimal EMS is to select the best decision set for the operation of the system based on a 24-hour forecast, reducing power conversion losses and unnecessary battery charge discharge cycles. Different forecast methods have been chosen for the 24-hour forecast of load, wind speed, and solar irradiance. A Genetic Algorithm is used for the optimizer. The cost function for evaluating system performance accounts for the fuel consumed, battery degradation, the amount of load shed, and the startups of the diesel engine. The results of the simulation have shown about 50% reduction in the number of battery cycles while preserving the same level of diesel engine fuel consumption as compared to classical EMS.