Autor según el artículo: Mahmoud K; Abdel-Nasser M; Mustafa E; Ali ZM

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed

Palabras clave: Sustainable power systems Regression Network Improved salp-swarm optimizer Generation Fossil-fuel Forecasting Dynamic economic dispatch Deep learning Algorithm

Resumen: © 2020 by the authors. Worldwide, the penetrations of photovoltaic (PV) and energy storage systems are increased in power systems. Due to the intermittent nature of PVs, these sustainable power systems require efficient managing and prediction techniques to ensure economic and secure operations. In this paper, a comprehensive dynamic economic dispatch (DED) framework is proposed that includes fuel-based generators, PV, and energy storage devices in sustainable power systems, considering various profiles of PV (clear and cloudy). The DED model aims at minimizing the total fuel cost of power generation stations while considering various constraints of generation stations, the power system, PV, and energy storage systems. An improved optimization algorithm is proposed to solve the DED optimization problem for a sustainable power system. In particular, a mutation mechanism is combined with a salp-swarm algorithm (SSA) to enhance the exploitation of the search space so that it provides a better population to get the optimal global solution. In addition, we propose a DED handling strategy that involves the use of PV power and load forecasting models based on deep learning techniques. The improved SSA algorithm is validated by ten benchmark problems and applied to the DED optimization problem for a hybrid power system that includes 40 thermal generators and PV and energy storage systems. The experimental results demonstrate the efficiency of the proposed framework with different penetrations of PV.

Áreas temáticas: Zootecnia / recursos pesqueiros Renewable energy, sustainability and the environment Medicina i Management, monitoring, policy and law Interdisciplinar Historia Hardware and architecture Green & sustainable science & technology Geography, planning and development Geografía Geociências Environmental studies Environmental sciences Environmental science (miscellaneous) Ensino Engenharias iii Engenharias ii Engenharias i Enfermagem Energy engineering and power technology Education Computer science (miscellaneous) Computer networks and communications Ciências agrárias i Building and construction Biotecnología Biodiversidade Arquitetura, urbanismo e design Arquitetura e urbanismo

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 1937-0709

Direcció de correo del autor: mohamed.abdelnasser@urv.cat

Identificador del autor: 0000-0002-1074-2441

Fecha de alta del registro: 2023-07-31

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.mdpi.com/2071-1050/12/2/576

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Sustainability. 12 (2):

Referencia de l'ítem segons les normes APA: Mahmoud K; Abdel-Nasser M; Mustafa E; Ali ZM (2020). Improved salp-swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems. Sustainability, 12(2), -. DOI: 10.3390/su12020576

DOI del artículo: 10.3390/su12020576

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications