Control de eficiencia eléctrica aplicado al confort de un Smart Home utilizando teoría de grafos

El presente artículo aborda la planeación y despliegue de una red de sensores inalámbricos (RSI) dentro de un hogar inteligente o smart home bajo una infraestructura heterogénea de comunicación (IHC). Esto nos permitirá capturar información (temperatura, humedad relativa., etc.) en tiempo real del área en el que el sensor será emplazado. Además, permitirá tomar acciones de control proporcional integral para garantizar un uso eficiente del recurso energético. Una vez que se disponga de datos, estos serán enviados a un punto de acceso de información (PAI) mismo que recolecta datos de al menos un sensor inalámbrico (SI) para redireccionarlos hacia un centro de gestión y control (CGC). Con ello se pretende proporcionar una vida asistida dentro de un hogar inteligente, maximizando el confort del usuario. RSI se relacionan de forma directa con el despliegue, topología y la energía de consuno. El hardware y software será implementado utilizando teoría de grafos.This article studied the planning and deployment of a wireless sensor network (WSN) within a smart home under a heterogeneous communication infrastructure (HCI). This will allow us to capture information (temperature, relative humidity, etc.) in real time of the area in which the sensor will be located. Moreover, it will allow taking integral proportional control actions to guarantee efficient use of the energy resource. Once data is available, it will be sent to an access point (AP) that collects information from at least one wireless sensor (SI) to redirect it to a management and control center (MCC). This is intended to provide assisted living within a smart home, maximizing user comfort. RSI are directly related to deployment, topology, and energy consumption. The hardware and software will be implemented using graph theory

Sustainable and Resilient Smart House Using the Internal Combustion Engine of Plug-in Hybrid Electric Vehicles

Nowadays, due to the increasing number of disasters, improving distribution system resiliency is a new challenging issue for researchers. One of the main methods for improving the resiliency in distribution systems is to supply critical loads after disasters during the power outage and before system restorations. In this paper, a “Sustainable and resilient smart house” is introduced for the first time by using plug-in hybrid electric vehicles (PHEVs). PHEVs have the ability to use their fuel for generating electricity in emergency situations as the Vehicle to Grid (V2G) scheme. This ability, besides smart house control management, provides an opportunity for distribution system operators to use their extra energy for supplying a critical load in the system. The proposed control strategy in this paper is dedicated to a short duration power outage, which includes a large percent of the events. Then, improvement of the resiliency of distribution systems is investigated through supplying smart residential customers and injecting extra power to the main grid. A novel formulation is proposed for increasing the injected power of the smart house to the main grid using PHEVs. The effectiveness of the proposed method in increasing power injection during power outages is shown in simulation results.©2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, http://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

An exact relaxation of AC-OPF problem for battery-integrated power grids

Renewable energy resources and power electronics-interfaced loads introduce fast dynamics in distribution networks. These dynamics cannot be regulated by slow conventional solutions and require fast controllable energy resources such as Battery Energy Storage Systems (BESSs). To compensate for the high costs associated to BESSs, their energy and power management should be optimized. In this paper, a convex iterative optimization approach is developed to find the optimal active and reactive power setpoints of BESSs in active distribution networks. The objective is to minimize the total cost of energy purchase from the grid. Round-trip and life-time characteristics of BESSs are modelled accurately and integrated into a relaxed and exact formulation of the AC power flow, resulting into a Modified Augmented Relaxed Optimal Power Flow (MAROP) problem. The feasibility and optimality of the solution under the grid security limits and technical constraints of BESSs is proven analytically. A 32-bus IEEE test benchmark is used to illustrate the performance of the developed approach in comparison to the alternative approaches existing in the literature

An IoT-based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills

An IoT-based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills

Deep Learning Techniques for Power System Operation: Modeling and Implementation

The fast development of the deep learning (DL) techniques in the most recent years has drawn attention from both academia and industry. And there have been increasing applications of the DL techniques in many complex real-world situations, including computer vision, medical diagnosis, and natural language processing. The great power and flexibility of DL can be attributed to its hierarchical learning structure that automatically extract features from mass amounts of data. In addition, DL applies an end-to-end solving mechanism, and directly generates the output from the input, where the traditional machine learning methods usually break down the problem and combine the results. The end-to-end mechanism considerably improve the computational efficiency of the DL.The power system is one of the most complex artificial infrastructures, and many power system control and operation problems share the same features as the above mentioned real-world applications, such as time variability and uncertainty, partial observability, which impedes the performance of the conventional model-based methods. On the other hand, with the wide spread implementation of Advanced Metering Infrastructures (AMI), the SCADA, the Wide Area Monitoring Systems (WAMS), and many other measuring system providing massive data from the field, the data-driven deep learning technique is becoming an intriguing alternative method to enable the future development and success of the smart grid. This dissertation aims to explore the potential of utilizing the deep-learning-based approaches to solve a broad range of power system modeling and operation problems. First, a comprehensive literature review is conducted to summarize the existing applications of deep learning techniques in power system area. Second, the prospective application of deep learning techniques in several scenarios in power systems, including contingency screening, cascading outage search, multi-microgrid energy management, residential HVAC system control, and electricity market bidding are discussed in detail in the following 2-6 chapters. The problem formulation, the specific deep learning approaches in use, and the simulation results are all presented, and also compared with the currently used model-based method as a verification of the advantage of deep learning. Finally, the conclusions are provided in the last chapter, as well as the directions for future researches. It’s hoped that this dissertation can work as a single spark of fire to enlighten more innovative ideas and original studies, widening and deepening the application of deep learning technique in the field of power system, and eventually bring some positive impacts to the real-world bulk grid resilient and economic control and operation