A hierarchical decomposition approach for multi-level building design optimization
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Cited by 4 publications
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Intelligent secondary control for energy management in a micro-grid by using multi-agent system
(English) Nowadays, distributed energy resources are widely used to supply demand in micro grids especially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers. The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This thesis presents two bottomup smart control approaches to manage energy in DC microgrids (MG) that split the demand among several generators. Firstly, an energy management system (EMS) based on multi-agent system (MAS) controllers is developed to manage energy, control the '.<lltage and create balance between supply and demand in the system with the aim of supporting the reliability charactenstic. In the proposed approach, a reconfigured hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has the ability to control system, even if a failure appears into decision unit The second research approach presents an energy management system based on multi-agent system under the supervision of a smart contract with a bottom-up approach for a grid connected DC micro-grid that is equipped with solar photovoltaic panels (PV), wind turbine (WT) and micro-turbine (MT) and energy storage (BES). In the presented approach, each unit controls and manages through a distributed decision structure. The BES agent is managed by an intelligent structure based on a reinforcement learning model. Since charging and discharging the battery is a stepwise process, in this research, a Markov decision process is trained by using a Q-learning algorithm. The rest of the agents are controlled and managed by heuristic algorithms. To create interaction and coordination among agents, a tendering process is used wherein each agent under its supervised control structure presents its offer to the tendered item at each time period The tendering organization allocates the requested power through first price sealed-bid algorithm between bidders to optimize energy cost in the MG. The two proposed approaches present online intelligent systems that can guarantee fault-tolerance, stability and reliability in the MG especially in green houses and smart cities, respectively. Various scientific papers have been published or are currently under review that can validate the achieved objectives and guarantee the quality of the thesis. (Español) Hoy en día, los recursos energéticos distribuidos se utilizan ampliamente para satisfacer la demanda en microrredes, especialmente en edificios ecológicos. Estos recursos suelen conectarse mediante convertidores electrónicos de potencia, que actuan como actuadores, al sistema, y permiten inyectar la potencia activa y reactiva deseada, según lo determinen los controladores inteligentes. La operación y el rendimiento general de un convertidor en dicho sistema depende de la estabilidad y robustez de las técnicas de control. Esta tesis presenta dos enfoques de control inteligente de abajo hacia arriba para gestionar la energía en microrredes de CC que dividen la demanda entre varios generadores. En primer lugar, se desarrolla un sistema de gestión de energla (EMS) basado en controladores de sistema multiagente (MAS) para gestionar la energía, controlar el voltaje y crear un equilibrio entre la oferta y la demanda en el sistema con el objetivo de respaldar la característica de confiabilidad. En el enfoque propuesto, se implementa un algoritmo jerárquico reconfigurado para controlar la interacción de los agentes, donde se utiliza un bus CAN para establecer la comunicación entre ellos. Este marco de operación tiene la capacidad de controlar el sistema, incluso si aparece una falla en la unidad de decisión. El segundo enfoque de inwstigación presenta un sistema de gestión de energía basado en un sistema multiagente bajo la supervisión de un contrato inteligente con un enfoque de abajo hacia arriba para una microrred de CC conectada a la red que está equipada con paneles solares fotovoltaicos (PV), turbina eólica ( WT) y micro-turbina (MT) y almacenamiento de energía (BES). En el enfoque presentado, cada unidad controla y gestiona su operación a través de una estructura de decisión distribuida. El agente BES esta gestionado por una estructura inteligente basada en un modelo de aprendizaje por refuerzo. Dado que cargar y descargar la batería es un proceso gradual, en esta inwstigación se entrena un proceso de decisión de Markov mediante el uso de un algoritrno Q-leaming. El resto de agentes son controlados y gestionados por algoritrnos heurísticos. Para crear interacción y coordinación entre los agentes, se utiliza un proceso de licitación en el que cada agente bajo la estructura de control supervisado presenta su oferta al artículo licitado en cada período de tiempo. La organización licitadora asigna la potencia solicitada a través del algoritmo de oferta en sobre cerrado de primer precio entre los licitadores para optimizar el costo de la energía en la microrred. Los dos enfoques propuestos presentan sistemas inteligentes en Iínea que pueden garantizar tolerancia a fallas, estabilidad y confiabilidad en la microrred, especialmente para edificios sostenibles y ciudades inteligentes, respectivamente. Se han publicado o se encuentran en proceso de revisión diversos trabajos cientlficos que demuestran y validan los obietivos alcanzados v garantizan la calidad de la tesis.
Intelligent secondary control for energy management in a micro-grid by using multi-agent system
(English) Nowadays, distributed energy resources are widely used to supply demand in micro grids especially in green buildings. These resources are usually connected by using power electronic converters, which act as actuators, to the system and make it possible to inject desired active and reactive power, as determined by smart controllers. The overall performance of a converter in such system depends on the stability and robustness of the control techniques. This thesis presents two bottomup smart control approaches to manage energy in DC microgrids (MG) that split the demand among several generators. Firstly, an energy management system (EMS) based on multi-agent system (MAS) controllers is developed to manage energy, control the '.<lltage and create balance between supply and demand in the system with the aim of supporting the reliability charactenstic. In the proposed approach, a reconfigured hierarchical algorithm is implemented to control interaction of agents, where a CAN bus is used to provide communication among them. This framework has the ability to control system, even if a failure appears into decision unit The second research approach presents an energy management system based on multi-agent system under the supervision of a smart contract with a bottom-up approach for a grid connected DC micro-grid that is equipped with solar photovoltaic panels (PV), wind turbine (WT) and micro-turbine (MT) and energy storage (BES). In the presented approach, each unit controls and manages through a distributed decision structure. The BES agent is managed by an intelligent structure based on a reinforcement learning model. Since charging and discharging the battery is a stepwise process, in this research, a Markov decision process is trained by using a Q-learning algorithm. The rest of the agents are controlled and managed by heuristic algorithms. To create interaction and coordination among agents, a tendering process is used wherein each agent under its supervised control structure presents its offer to the tendered item at each time period The tendering organization allocates the requested power through first price sealed-bid algorithm between bidders to optimize energy cost in the MG. The two proposed approaches present online intelligent systems that can guarantee fault-tolerance, stability and reliability in the MG especially in green houses and smart cities, respectively. Various scientific papers have been published or are currently under review that can validate the achieved objectives and guarantee the quality of the thesis. (Español) Hoy en día, los recursos energéticos distribuidos se utilizan ampliamente para satisfacer la demanda en microrredes, especialmente en edificios ecológicos. Estos recursos suelen conectarse mediante convertidores electrónicos de potencia, que actuan como actuadores, al sistema, y permiten inyectar la potencia activa y reactiva deseada, según lo determinen los controladores inteligentes. La operación y el rendimiento general de un convertidor en dicho sistema depende de la estabilidad y robustez de las técnicas de control. Esta tesis presenta dos enfoques de control inteligente de abajo hacia arriba para gestionar la energía en microrredes de CC que dividen la demanda entre varios generadores. En primer lugar, se desarrolla un sistema de gestión de energla (EMS) basado en controladores de sistema multiagente (MAS) para gestionar la energía, controlar el voltaje y crear un equilibrio entre la oferta y la demanda en el sistema con el objetivo de respaldar la característica de confiabilidad. En el enfoque propuesto, se implementa un algoritmo jerárquico reconfigurado para controlar la interacción de los agentes, donde se utiliza un bus CAN para establecer la comunicación entre ellos. Este marco de operación tiene la capacidad de controlar el sistema, incluso si aparece una falla en la unidad de decisión. El segundo enfoque de inwstigación presenta un sistema de gestión de energía basado en un sistema multiagente bajo la supervisión de un contrato inteligente con un enfoque de abajo hacia arriba para una microrred de CC conectada a la red que está equipada con paneles solares fotovoltaicos (PV), turbina eólica ( WT) y micro-turbina (MT) y almacenamiento de energía (BES). En el enfoque presentado, cada unidad controla y gestiona su operación a través de una estructura de decisión distribuida. El agente BES esta gestionado por una estructura inteligente basada en un modelo de aprendizaje por refuerzo. Dado que cargar y descargar la batería es un proceso gradual, en esta inwstigación se entrena un proceso de decisión de Markov mediante el uso de un algoritrno Q-leaming. El resto de agentes son controlados y gestionados por algoritrnos heurísticos. Para crear interacción y coordinación entre los agentes, se utiliza un proceso de licitación en el que cada agente bajo la estructura de control supervisado presenta su oferta al artículo licitado en cada período de tiempo. La organización licitadora asigna la potencia solicitada a través del algoritmo de oferta en sobre cerrado de primer precio entre los licitadores para optimizar el costo de la energía en la microrred. Los dos enfoques propuestos presentan sistemas inteligentes en Iínea que pueden garantizar tolerancia a fallas, estabilidad y confiabilidad en la microrred, especialmente para edificios sostenibles y ciudades inteligentes, respectivamente. Se han publicado o se encuentran en proceso de revisión diversos trabajos cientlficos que demuestran y validan los obietivos alcanzados v garantizan la calidad de la tesis.
This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters such as the depth and width of different rooms and their window-to-wall ratios etc., to establish a climate responsive optimization design process with indoor lighting environment comfort, with heating and cooling demand as the objective functions. Meanwhile, based on Monte Carlo simulation data, ANN (Artificial Neural Network) is used to establish a prediction model to analyze the sensitivity of interior design parameters under different typical cities’ climatic conditions. The study results show that the recommended values for the total width and total depth of indoor units under the climatic conditions of each city are both approximately 14.97 m and 7.88 m. Among them, under the climatic conditions of Harbin and Shenzhen, the design parameters of residential interiors can take the recommended value of UDI optimal or nZEB optimal. While the recommended values of window-to-wall ratios for the north bedroom, master bedroom and living room in Shanghai residential interiors are 0.26, 0.32 and 0.33, respectively. The recommended value of the window-to-wall ratio of the master bedroom in Kunming residences is 0.36, and that of the remaining rooms is between 0.15 and 0.18. The recommended values of window-to-wall ratios for the master bedroom and living room in Beijing residences are 0.41 and 0.59, respectively, and that for the remaining rooms are 0.15. The multi-objective optimization process based on parametric performance simulation used in the study can effectively assist architects in making energy-saving design decisions in the preliminary stage, allowing architects to have a case to follow in the actual design operation process.
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