Neural-net based real-time economic dispatch for thermal power plants

This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal power plant units. The approach can take into account operational requirements and power network losses. The proposed economic dispatch uses an artificial neural network (ANN) for the generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal power systems, based on neural net theory for simplified solution algorithms and an improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories by applying neural net forecasts of power system load patterns.