Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid

Shadmand, Mohammad B.; Balog, Robert S.

doi:10.1109/tsg.2014.2315043

Cited by 225 publications

(93 citation statements)

References 25 publications

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“…In this research, NSGA2 is one of the multi-objective optimization techniques based on searching the Pareto solution. NSGA2 is adopted in many research works, and it is possible to perform an efficient search of the solutions [16][17][18][19][20]. NSGA2 performs the elitism strategy where the superior solution is left to the next generation, non-dominated sorting where the superior solution and the other solution are ranked and sorted and crowding-distance computation to choose the solution that is far in distance between the adjacent solution [21].…”

Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

et al. 2017

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Abstract:Recently, the off-grid smart house has been attracting attention in Japan for considering global warming. Moreover, the selling price of surplus power from the renewable energy system by Feed-In Tariff (FIT) has declined. Therefore, this paper proposes an off-grid smart house with the introduced Photovoltaic (PV) system, Solar Collector (SC) system, Hot Water Heat Pump (HWHP), fixed battery and Electric Vehicle (EV). In this research, a multi-objective optimization problem is considered to minimize the introduced capacity and shortage of the power supply in the smart house. It can perform the electric power procurement from the EV charging station for the compensation of a shortage of power supply. From the simulation results, it is shown that the shortage of the power supply can be reduced by the compensation of the EV power. Furthermore, considering the uncertainty for PV output power, reliable simulation results can be obtained.

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Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

et al. 2017

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“…Many literature works about the multi-objective optimization method have been published by using NSGA2 [27][28][29].…”

Section: Nsga2mentioning

confidence: 99%

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

et al. 2016

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Abstract:A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

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“…En [7] se expone una metodología para determinar la disponibilidad y el costo del recurso energético entregado por una microrred con generación eólica y solar, y conexión a la red eléctrica principal. La metodología propuesta pretende un suministro fiable de energía con la menor inversión económica, para lo cual hace uso del Algoritmo MOGA como método de optimización.…”

Section: Introductionunclassified

Gestión Óptima de la Potencia Eléctrica en una Microgrid Conectada, basada en el Algoritmo Genético para Optimización Multiobjetivo MOGA

Cortes¹,

Bravo²,

Santiago³

2017

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Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 (https://creativecommons.org/licenses/by-nd/4.0/) F.A. Zúñiga-Cortés, E.F Caicedo-Bravo y D.M López-Santiago. "Gestión óptima de la potencia eléctrica en una microrred conectada, basada en el algoritmo genético para optimización multiobjetivo MOGA", UIS Ingenierias, vol. RESUMENLas microrredes son un concepto revolucionario que enfrenta algunos problemas de los grandes sistemas eléctricos centralizados. Aunque este concepto promete numerosos beneficios, también presenta nuevos desafíos en materia de control y operación. Uno de los desafíos más importantes es gestionar, de manera óptima, la energía producida por las microrredes. Este artículo presenta una estrategia de gestión de potencia en una microrred conectada, basada en el algoritmo MOGA. La estrategia se formula con base en dos objetivos conflictivos entre sí: la reducción de los costos operacionales de la microrred y la reducción de la cantidad de emisiones de gases contaminantes. Los resultados de simulación muestran los beneficios de usar la estrategia de gestión basada en MOGA, en comparación con una estrategia de importación total de la red principal, lo cual hace a MOGA una técnica competitiva para integrar el núcleo optimizador de un sistema de gestión de energía en microrredes.Palabras clave: algoritmos evolutivos de optimización multiobjetivo (MOEA), algoritmo genético multiobjetivo (MOGA), gestión óptima de potencia, microrred, optimización multiobjetivo, sistema de gestión de energía (EMS). ABSTRACTMicrogrids are a revolutionary concept to face some problems of the big and centralized power systems. Although emerging concept promises to offer numerous benefits, also presents new challenges in control and operation. One of the most important challenges is to make optimal power management into the microgrid. This power management must ensure maximum use of available resources; at same time it must reduce environmental contamination. Based on Multi-Objective Genetic Algorithm MOGA, this paper presents one strategy to make optimal power management in a grid-connected microgrid. The strategy runs over two conflictive objectives: to reduce microgrid operational cost and greenhouse emissions. Simulation results show benefits using the MOGA strategy, in comparison with a strategy of full importation from main grid. In this manner, the proposed strategy is a good option to build the optimizer kernel of energy management system EMS in microgrids. Keywords: energy management system (EMS), microgrid, multi-objective optimization, multi-objective genetic algorithm (MOGA), multi-objective optimization evolutionary algorithms (MOEA), optimal power management.

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Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid

Cited by 225 publications

References 25 publications

Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

Multi-Objective Optimization for Equipment Capacity in Off-Grid Smart House

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

Gestión Óptima de la Potencia Eléctrica en una Microgrid Conectada, basada en el Algoritmo Genético para Optimización Multiobjetivo MOGA

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