This paper analyzes and tests a proposed neural network (NN) to solve the Dynamic
Economic Dispatch (DED) as part of the unit commitment problem. The proposed NN is a
fast and direct computation solver using a Hopfield model for solving the dynamic economic
dispatch problem of thermal generators which is a dynamic optimization problem taking into
account the constraints imposed on system operation by generator ramping rate limits.
Formulations for solving the ED and DED problems are explored. Through the application of
these formulations, direct computation instead of iterations for solving the problem becomes
possible. Not like the usual Hopfield network, which select the weighting factors of the
energy function by trials, the proposed network determines the corresponding factors by
calculations and employs a linear input-output model for the neurons. The effectiveness of the
developed neural network is identified through its application to the New England test system.
Computational results manifest that the model has a lot of excellent performances.