Economic Dispatch with Multiple Fuel Types by Enhanced Augmented Lagrange Hopfield Network

Abstract

This paper proposes an enhanced augmented Hopfield Lagrange neural network (EALHN) for solving economic dispatch (ED) problem with piecewise quadratic cost functions. The EALHN is an augmented Lagrange Hopfield neural network (ALHN), which is a combination of continuous Hopfield neural network and augmented Lagrangian relaxation function as its energy function, enhanced by a heuristic search algorithm for determination of fuel type. The proposed EALHN solve the problem in two phases. In the first phase, a heuristic algorithm based on average production cost of generating units is used to determine the most suitable fuel type of units satisfying load demand. In the last phase, the ALHN is applied to solve economic dispatch to find optimal solution with the selected fuel types. The proposed method is tested on two test systems with various load demands and compared to many other methods in the literature. The results have shown that the proposed method is efficient and fast for the ED problems with multiple fuel types represented by quadratic cost functions.