MAS energy management of a microgrid based on fuzzy logic control

Serraji, Maria; Boumhidi, Jaouad; Nfaoui, El Habib

doi:10.1109/isacv.2015.7106187

Cited by 5 publications

(2 citation statements)

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“…Alternatively to these analytically-based control methods, Fuzzy Logic Control (FLC) allows the implementation of the human's heuristic knowledge about how to control a system [37,38] and has also been applied to the EMS design. For instance, in [39] a FLC-based EMS is designed to prioritize selling the additional electricity generated by RES and to maintain the battery State of Charge (SOC) above the 50% to extend the ESS lifetime, whereas in [40,41] a rule-based controller (i.e., FLC) is used in combination with different optimization techniques to achieve an optimum energy cost and thermal comfort in grid connected microgrids.…”

Section: Introductionmentioning

confidence: 99%

Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting

et al. 2017

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This paper presents the design of an energy management strategy based on a low complexity Fuzzy Logic Control (FLC) for grid power profile smoothing of a residential grid-connected microgrid including Renewable Energy Sources (RES) and battery Energy Storage System (ESS). The proposed energy management strategy uses generation and demand forecasting to anticipate the future behavior of the microgrid. Accordingly to the microgrid power forecast error and the Battery State-of-Charge (SOC) the proposed strategy performs the suitable control of the grid power. A simulation comparison with previous energy management strategies highlights the advantages of the proposed work minimizing fluctuations and power peaks in the power profile exchanged with the grid while keeping the energy stored in the battery between secure limits. Finally, the experimental validation in a real residential microgrid implemented at Public University of Navarre (UPNa, Spain) demonstrates the proper operation of the proposed strategy achieving a smooth grid power profile and a battery SOC center close to the 75% of the rated battery capacity.Peer ReviewedPostprint (author's final draft

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Section: Introductionmentioning

confidence: 99%

Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting

et al. 2017

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“…This heuristic knowledge suggests the use of FLC for the design of the EMS strategy in a residential grid-connected scenario. In this regard, fuzzy logic provides a formal methodology for representing, manipulating, computing, and implementing a human's heuristic knowledge about how to control a system [58], [59]. In addition, it is a powerful control technique capable of dealing with the imprecisions and nonlinearity of complex systems, that can be based on experience of the user about the system behavior rather than the mathematical model of the system as in the traditional control theory [60], [61].…”

Section: Figure Indexmentioning

confidence: 99%

Energy management strategies based on fuzzy logic control for grid-tied domestic electro-thermal microgrid

Arcos Avilés

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The environmental and economic benefits related to the reduction of both carbon dioxide emission and transmission losses have made distributed renewable generation systems became a competitive solution for future power systems. In this context, Microgrids (MG) are considered as the key building blocks of smart grids and have aroused great attention in the last decade for their potential and the impact they may have in the coming future. The MG concept has captured great attention in the last years since it can be considered one of the most suitable alternatives for integration of distributed generation units in the utility grid. However, this integration involves some challenges to deal with especially when penetration of Renewable Energy Sources (RES) into the distribution network is increased. Therefore, an effective Energy Management System (EMS) is required to ensure optimal energy utilization within the MG, consequently, facilitating both the grid integration and operator control. In this regard, the EMS strategy design depends on the application, MG power architecture, and the power management capability of the MG elements. This dissertation research focuses on the design of different EMS strategies based on Fuzzy Logic Control (FLC) for a residential grid-connected electro-thermal MG including renewable power generation (i.e. photovoltaic and wind turbine generators) and storage capability (i.e. battery bank and water storage tank). The main goal of the FLC-based EMS strategies is to minimize the grid power fluctuations while keeping the battery State-of-Charge (SOC) within secure limits. In order to accomplish this goal, the controller design parameters, such as membership functions and rule-base, of the FLC-based EMS strategies, are adjusted to optimize a pre-defined set of quality criteria of the MG behavior. The analysis and design of the FLC-based EMS strategies for electrical and electro-thermal MG power architectures are developed considering two different scenarios. A first scenario where the MG power forecasting is not provided and a second scenario where the forecast of generation power and load demand are considered. A comparison with the different EMS strategies is presented in simulation level, whereas the features of the enhanced FLC-based EMS strategies are experimentally tested on a real residential microgrid implemented at the Public University of Navarre (UPNa) Este estudio presenta el diseño de diferentes estrategias de gestión energética basadas en un controlador difuso para una microrred electro-térmica residencial conectada a la red eléctrica compuesta por generadores de energía renovable (solar y eólico) y elementos de almacenamiento de energía (banco de baterías y tanque de almacenamiento de agua). El objetivo principal de las estrategias de gestión es reducir los picos y fluctuaciones de potencia en el perfil de potencia intercambiado con la red eléctrica y preservar la vida útil del sistema de almacenamiento. Se presenta una revisión del estado del arte de estudios anteriores que buscan este objetivo. Se muestra el análisis de dos arquitecturas de microrred. La primera arquitectura consiste en una microrred eléctrica compuesta fuentes de energía renovables, sistema de almacenamiento de energía y el consumo eléctrico de una vivienda. La segunda arquitectura consiste en una microrred electro-térmica que contiene los elementos de la microrred eléctrica e incluye adicionalmente generadores térmicos y el consumo térmico de la vivienda. Con el objetivo de medir la eficiencia de las diferentes estrategias de gestión, se presenta un conjunto de criterios de evaluación que analizan la calidad del perfil de potencia intercambiado con la red eléctrica obtenido mediante las diferentes estrategias de gestión energética. Estos criterios de calidad son utilizados adicionalmente para la optimización de parámetros de los controladores difusos, lo cual se realiza mediante un proceso de aprendizaje fuera de línea que considera los datos históricos del comportamiento de la microrred. La comparación entre las diferentes estrategias de gestión energética se realiza mediante simulación, utilizando los datos reales de generación y consumo adquiridos en la Universidad Pública de Navarra durante el período comprendido entre Julio 2013 y Julio 2014. El diseño de las estrategias de gestión energética para la arquitectura de microrred eléctrica supone dos posibles escenarios, el primer escenario no considera la previsión de consumo y generación de la microrred, y el segundo escenario si considera esta previsión. Las prestaciones de las estrategias basadas en control difuso para cada uno de estos escenarios son validadas experimentalmente en condiciones reales en la microrred de la Universidad Pública de Navarra. Finalmente, se presenta el análisis de las estrategias de gestión basadas en control difuso empleadas a la arquitectura de microrred electro-térmica. La comparación, mediante simulación, con otras estrategias de gestión aplicadas a la misma arquitectura ha demostrado el correcto desempeño de las estrategias desarrolladas basadas en control difuso.

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