Generation scheduling methodology for thermal units with wind energy system considering unexpected load deviation

This paper presents a methodology of short term generation scheduling (unit commitment) for thermal units integrated with wind energy system considering the unexpected deviation on load demand. The deviation in load demand occurs mainly due to variation in temperature which in turns yields error in load forecasting. Since the usual unit commitment (UC) scheduling as well as economic power dispatch procedures are based on predicted load demand, the sudden deviation results non optimal solution and hence increases the thermal unit fuel cost. This method tracks down the load deviation at a particular hour and using a sophisticated load forecasting technique (based on neural network) re-predicts the load demand for the hours to come. This way a relatively accurate load forecasting is achieved and the learning process of neural network is improved which will eventually reduce the fuel cost. Meanwhile the fuel cost is further minimized by the inclusion of wind energy system with the base thermal unit system. A genetic algorithm (GA) is used to solve the UC problem with some useful problem specific operators. Simulation results show the effectiveness of this proposed method considering various cases temperature deviations.