Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network

This paper proposes an enhanced augmented Lagrange Hopfield network (EALHN) for solving economic dispatch (ED) with piecewise quadratic cost functions. The EALHN is an augmented Lagrange Hopfield neural network (ALHN), a continuous Hopfield neural network with its energy function based on augmented Lagrangian function, enhanced by a heuristic search for determination of fuel type. The proposed EALHN solves the ED problem in two phases. In the first phase, a heuristic search based on the average production cost of generating units is used to determine the most suitable fuel type for each unit so that total maximum power generation from all units is sufficient for supplying to load demand. In the last phase, the ALHN is applied to find optimal solution corresponding to the chosen fuel types. The proposed method is tested on several systems with various load demands and the obtained test results are compared to those from many other methods in the literature. Test results have indicated that the proposed method is efficient and fast for the ED problems with multiple fuel types represented by quadratic cost functions.