Optimal Dispatch of Power Generation Using Artificial Neural Network

In the practical power system, power plants are not at the same distance from the centre of load and their fuel costs are also different. Generally the power generation capacity is more than the total load demand and losses under normal operating conditions. Thus there is a need to find an effective real and reactive power scheduling to power plants to meet load demand as well as to minimize the operating cost. There are many Optimal Power Flow methods used to determine Optimal Dispatch of Power Generation and one such method is Newton method. This method is well-known in the area of Power Systems and provides a very powerful method because of its rapid convergence near the solution. This method will lead to an economic operation of the power plant and yields an economic dispatch of real power generation. As this is too slow, we proposed a soft computing based approach i.e. Back Propagation Neural Network (BPNN) for determining Optimal Dispatch of Power Generation. This method provides fast and accurate results compared with the above conventional method.