Dynamic constrained economic/emission dispatch scheduling using neural network

Abstract

In this paper, a Dynamic Economic/Emission Dispatch (DEED) problem is obtained by considering both the economy and emission objectives with required constraints dynamically. This paper presents an optimization algorithm for solving constrained combined economic emission dispatch (EED) problem and DEED, through the application of neural network, which is a flexible Hopfield neural network (FHNN). The constrained DEED must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone, are also considered in practical generator operation. The feasibility of the proposed FHNN using to solve DEED is demonstrated using three power systems, and it is compared with the other methods in terms of solution quality and computation efficiency. The simulation results showed that the proposed FHNN method was indeed capable of obtaining higher quality solutions efficiently in constrained DEED and EED problems with a much shorter computation time compared to other methods.