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Modelo hiperheurístico y simulación para la optimización de la inyección de potencia desde micro generación en sistemas eléctricos de distribución de baja tensión
Copyright policy )Modelo hiperheurístico y simulación para la optimización de la inyección de potencia desde micro generación en sistemas eléctricos de distribución de baja tensión
El presente trabajo aborda los desarrollos requeridos para resolver el problema de optimizar la inyección de potencia proveniente de micro generadores en sistemas eléctricos de distribución de energía (SEDE) de baja tensión (BT), focalizándose en los paneles solares fotovoltaicos (MG FV). Para tal propósito, se toman como referencia los conceptos y desarrollos generales presentados en artículos previos del autor, y se detallan los mismos, orientándolos a dos hiperheurísticas Basadas en Razonamiento, con dominio en metaheurísticas variantes de PSO, Formas (X-FPSO) y Cardumen de Peces Artificiales (FAFS) multiobjetivo. Estas hiperheurísticas fueron referidas como HY CBR X-FPSO y HY CBR (X-FPSO + FAFS) MPI, siendo la segunda la que mejora los resultados en comparación con la primera, resolviendo las dos limitaciones observadas en esta. Se describe también la paralelización del algoritmo hiperheurístico HY CBR (X-FPSO + FAFS) MPI, en las dos variantes descriptas, empleando la plataforma MPI. Finalmente, se presenta una simulación sobre un SEDE con MG FV BT real, mediante ambos algoritmos hiperheurísticos, comparando los resultados. This paper addresses the processes required to solve the problem of optimizing the injection of power from micro generators in electric power distribution system (SEDE) of low voltage (BT), focusing on photovoltaic solar panels (MG FV). For this purpose, the concepts and general developments presented in previous articles by the author are taken as a reference, and they are detailed, leading to two hyper-heuristics based on reasoning, with mastery in variant meta-heuristics of PSO, Forms (X-FPSO) and Fast Artificial Fish Swarm (FAFS) multi-objective. These hyper-heuristics were referred to as HY CBR X-FPSO and HY CBR (X-FPSO + FAFS) MPI, the latter being the one that improves the results compared to the first one, solving the two limitations observed at this point. The parallelization of the hyper-heuristic algorithm HY CBR (X-FPSO + FAFS) MPI is also described, in the two afore-mentioned variants, using the MPI platform. Finally, a simulation is presented on a SEDE with real MG FV BT, through both hyper-heuristic algorithms, comparing the results. Fil: Schweickardt, Gustavo Alejandro. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
- National Technological University Argentina
- National Scientific and Technical Research Council Argentina
Microsoft Academic Graph classification: Computer science Parallel computing Point (geometry) Electric power distribution business.industry Photovoltaic system Swarm behaviour Power (physics) Fish <Actinopterygii> business Low voltage
Technology, metaheurísticas, sistema de distribución de energía eléctrica, baja tensión, paneles solares., //purl.org/becyt/ford/2.2 [https], //purl.org/becyt/ford/1 [https], SIMULACIÓN PARA LA OPTIMIZACIÓN, MICROGENERACIÓN DISTRIBUIDA, SISTEMA DE DISTRIBUCIÓN DE ENERGÍA ELÉCTRICA, METAHEURÍSTICAS, //purl.org/becyt/ford/5.2 [https], hiperheurísticas, HIPERHEURÍSTICAS, BAJA TENSIÓN, //purl.org/becyt/ford/5 [https], T, POTENCIA, microgeneración distribuida, simulación para la optimización, //purl.org/becyt/ford/1.2 [https], Engineering (General). Civil engineering (General), computación paralela, //purl.org/becyt/ford/2 [https], COMPUTACIÓN PARALELA, micro generación, potencia, TA1-2040, PANELES SOLARES
Technology, metaheurísticas, sistema de distribución de energía eléctrica, baja tensión, paneles solares., //purl.org/becyt/ford/2.2 [https], //purl.org/becyt/ford/1 [https], SIMULACIÓN PARA LA OPTIMIZACIÓN, MICROGENERACIÓN DISTRIBUIDA, SISTEMA DE DISTRIBUCIÓN DE ENERGÍA ELÉCTRICA, METAHEURÍSTICAS, //purl.org/becyt/ford/5.2 [https], hiperheurísticas, HIPERHEURÍSTICAS, BAJA TENSIÓN, //purl.org/becyt/ford/5 [https], T, POTENCIA, microgeneración distribuida, simulación para la optimización, //purl.org/becyt/ford/1.2 [https], Engineering (General). Civil engineering (General), computación paralela, //purl.org/becyt/ford/2 [https], COMPUTACIÓN PARALELA, micro generación, potencia, TA1-2040, PANELES SOLARES
Microsoft Academic Graph classification: Computer science Parallel computing Point (geometry) Electric power distribution business.industry Photovoltaic system Swarm behaviour Power (physics) Fish <Actinopterygii> business Low voltage
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- National Technological University Argentina
- National Scientific and Technical Research Council Argentina
El presente trabajo aborda los desarrollos requeridos para resolver el problema de optimizar la inyección de potencia proveniente de micro generadores en sistemas eléctricos de distribución de energía (SEDE) de baja tensión (BT), focalizándose en los paneles solares fotovoltaicos (MG FV). Para tal propósito, se toman como referencia los conceptos y desarrollos generales presentados en artículos previos del autor, y se detallan los mismos, orientándolos a dos hiperheurísticas Basadas en Razonamiento, con dominio en metaheurísticas variantes de PSO, Formas (X-FPSO) y Cardumen de Peces Artificiales (FAFS) multiobjetivo. Estas hiperheurísticas fueron referidas como HY CBR X-FPSO y HY CBR (X-FPSO + FAFS) MPI, siendo la segunda la que mejora los resultados en comparación con la primera, resolviendo las dos limitaciones observadas en esta. Se describe también la paralelización del algoritmo hiperheurístico HY CBR (X-FPSO + FAFS) MPI, en las dos variantes descriptas, empleando la plataforma MPI. Finalmente, se presenta una simulación sobre un SEDE con MG FV BT real, mediante ambos algoritmos hiperheurísticos, comparando los resultados. This paper addresses the processes required to solve the problem of optimizing the injection of power from micro generators in electric power distribution system (SEDE) of low voltage (BT), focusing on photovoltaic solar panels (MG FV). For this purpose, the concepts and general developments presented in previous articles by the author are taken as a reference, and they are detailed, leading to two hyper-heuristics based on reasoning, with mastery in variant meta-heuristics of PSO, Forms (X-FPSO) and Fast Artificial Fish Swarm (FAFS) multi-objective. These hyper-heuristics were referred to as HY CBR X-FPSO and HY CBR (X-FPSO + FAFS) MPI, the latter being the one that improves the results compared to the first one, solving the two limitations observed at this point. The parallelization of the hyper-heuristic algorithm HY CBR (X-FPSO + FAFS) MPI is also described, in the two afore-mentioned variants, using the MPI platform. Finally, a simulation is presented on a SEDE with real MG FV BT, through both hyper-heuristic algorithms, comparing the results. Fil: Schweickardt, Gustavo Alejandro. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina