Artificial neural network based planning of generation ready reserve capacity

Describes the use of an artificial neural network (ANN) to plan ready reserve (RR) capacity in a power system. The main role of the ANN is to estimate the needs for RR in the future on the basis of historical data such as maximal and minimum demand not supplied, and the average load that has strained the system. The article deals with the results of ANN capabilities to interpolate from the past data in order to meet the demands of the future planning for the ready reserve adequately. The ANN showed advantage over other techniques regarding evaluation of the ready reserve generators. It can evaluate the needs for the ready reserve correctly for the data expected in the future. Such possibility makes a good base for planning of the RR capacity maintaining a high level of security and adequacy of a power system.