Combined Heat and Power Economic Dispatch by Augmented Lagrange Hopfield Network

This paper proposes an augmented Lagrange Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed ALHN, the energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagrangian function to damp out oscillation of the Hopfield network during the convergence process, leading to a fast convergence. The proposed ALHN has been tested on various systems and compared to Lagrangian relaxation (LR), genetic algorithm (GA), improved ant colony search algorithm (IACSA), evolutionary programming (EP), improved genetic algorithm with multiplier updating (IGA-MU) and harmony search algorithm (HSA). Test results indicate that the proposed neural network is better than the other methods due to a lower total cost and faster computational time, especially for largescale CHPED problems.