A fast solver for combined emission/generation allocation using a Hopfield neural network

Autor:	Ramdani Youcef, Belhachem Rachid, Farid Benhamida, Souag Slimane
Rok vydání:	2013
Předmět:	Mathematical optimization Economic emission dispatch Electricity generation General Computer Science Artificial neural network Computer science Pollutant emissions Computation General Engineering Economic dispatch Optimisation algorithm Solver
Zdroj:	International Journal of Reasoning-based Intelligent Systems. 5:82
ISSN:	1755-0564 1755-0556
DOI:	10.1504/ijris.2013.057269
Popis:	The combined economic/emission dispatch (CEED) problem is obtained by considering both the economy and the emission objectives with required constraints. Many optimisation techniques are slow for such complex optimisation tasks and are not suitable for online use. This paper presents an optimisation algorithm for solving constrained CEED, through the application of a flexible Hopfield neural network (HNN). The constrained CEED must satisfy the system load demand and practical operation constraints of generators. The feasibility of the proposed HNN using to solve CEED is demonstrated using a three–unit test system and it is compared with the other methods in terms of solution quality and computation efficiency. The simulation results showed that the proposed HNN method was indeed capable of obtaining higher–quality solutions efficiently in CEED problems with a much shorter computation time compared with other methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c3956ed4cf30fe845a204a2ce59543fa https://doi.org/10.1504/ijris.2013.057269 Zobrazit plný text záznamu