Application of dynamic programming based fast computation Hopfield neural network to unit commitment and economic dispatch

摘要

This paper develops a new dynamic programming based direct computation Hopfield method for solving short term unit commitment (UC) problems of thermal generators. The proposed two step process uses a direct computation Hopfield neural network to generate economic dispatch (ED). Then using dynamic programming (DP) the generator schedule is produced. The method employs a linear input-output model for neurons. Formulations for solving the UC problems are explored. Through the application of these formulations, direct computation instead of iterations for solving the problems becomes possible. However, it has been found that the UC problem cannot be tackled accurately within the framework of the conventional Hopfield network. Unlike the usual Hopfield methods which select the weighting factors of the energy function by trials, the proposed method determines the corresponding factor using formulation calculation. Hence, it is relatively easy to apply the proposed method. The Neyveli thermal power station (NTPS) unit II in India with 3 units having prohibited operating zone has been considered as a case study and extensive studies have also been performed for different power systems consisting of 10, 20, and 26 generating units. Numerical results obtained are compared with conventional methods to reach proper unit commitment.