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Improved salp-swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems

  • Datos identificativos

    Identificador: imarina:6112298
    Autores:
    Mahmoud KAbdel-Nasser MMustafa EAli ZM
    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.
  • Otros:

    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 Ren 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: 2024-07-27
    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: https://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
  • Palabras clave:

    Computer Networks and Communications,Education,Energy Engineering and Power Technology,Environmental Science (Miscellaneous),Environmental Sciences,Environmental Studies,Geography, Planning and Development,Green & Sustainable Science & Technology,Hardware and Architecture,Management, Monitoring, Policy and Law,Ren,Renewable Energy, Sustain
    Sustainable power systems
    Regression
    Network
    Improved salp-swarm optimizer
    Generation
    Fossil-fuel
    Forecasting
    Dynamic economic dispatch
    Deep learning
    Algorithm
    Zootecnia / recursos pesqueiros
    Renewable energy, sustainability and the environment
    Ren
    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
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