Search

In glass furnace boosting, particularly in the tin bath and annealing lehr heating systems, an ac–ac power converter is normally employed by the industry. This Triac-based converter driven by integral cycle control (ICC) is employed to provide highly accurate power to the resistive heating elements distributed into the furnace. The advantages of ICC include low inrush current and increased reliability; although its impact on power quality (PQ), due to the subharmonic distortion produced, is significant. While different strategies for energy demand management have been implemented to date, they have not adequately addressed this PQ issue. Thus, the goal of this research is to establish an optimal electrical load scheduling strategy considering criteria about PQ and thermal behavior through a mixed-binary quadratic programming problem which exploits the ICC advantages avoiding PQ degradation.

Microgrids are considered as a key technology for the introduction of renewable energy systems in the electrical market. Nevertheless, microgrids are subject to random conditions such as changes in the energy forecast or component failures which will force microgrids to incur in the penalty costs applied in real time markets. In order to minimize the aforementioned costs, an optimal Energy Management System (EMS) for the economic re-schedule of a network of interconnected microgrids with hybrid Energy Storage System (ESS) under failure conditions is developed and validated using Model Predictive Control (MPC). The algorithm is specifically designed to achieve lower economic losses under failure conditions through the establishment of a local energy market in the network of microgrids than participating in the intraday or real-time markets.

This paper proposes a method for estimating the harmonic content of the current demanded by the loads present in a microgrid (MG) through the use of the current injected by an active power filter (APF). The main goal of this approach is to complement the benefits of adding IoT measurements in an MG by speeding up the estimation of the amplitudes of the harmonic distortion caused by the loads. For this purpose, a fast method for the online calculation of the APF reference current is proposed as well as a procedure for monitoring its spectral content. With the aim of demonstrating the validity of the proposed approach, simulation results obtained from a model developed in MATLAB Simulink are shown. As a result, the estimation of the harmonic content is achieved in less than one cycle and its use as an early detection system is demonstrated.

Ensuring the good health of industry operations is crucial as they are pushing themselves to have fully automated production. The mass adoption and rapid advancement of mobile devices has shifted the way in which businesses and consumers interact. Data visualization is an important method for big-data analytics, particularly to help human to comprehend and find pattern or trends. An Internet of Things (IoT) solution together with an adequate mobile application for proper data visualization would be extremely beneficial for the management of the facility and thus ensure continuous business operations. This paper describes an application for voltage sag visualization based on data monitored by an IoT PQ sensor. The sensor uses the ESP32 microcontroller to send data to the IoT platform monitored by an energy metering integrated circuit ADE9000. Through the new developed Android smartphone application, PQ data can be remotely accessed from remote smart phones, tablets or similar. The application uses Unity software. The paper shows measurements from voltage dips.

The deployment of smart appliances and the capabilities offered to the user are increasing. These devices become important investments as the user is not willing to suffer external damage that alters the appropriate operation of the system. For this reason, the disturbances in power quality (PQ) and energy efficiency are not less important than the user-programmable functions or current operating status available on these devices. All these involved in the development of the Internet of Things (IoT) paradigm and communication protocols (wired and wireless) applied to smart appliances will enable the implementation of a smart energy consumption device. This paper develops the first approach of a novel embedded power quality sensor (EPQS) to provide information related to the PQ and energy consumption about the smart appliance within an installation. The sensor and the monitoring software solution proposed allow integrating the system within the cloud. This will lead to a proper analysis of operational parameter patterns as well as knowledge of the power supply and load conditions of the multiple appliances. These data will be useful for the product maintenance and support and, ultimately, for customer service.

Adoption of distributed energy resources such as wind and solar can exacerbate energy inequality. A new study shows that inequality in opportunity to adopt renewable energy resources may already be baked into the grid infrastructure design.

In electric power systems, any deviation with respect to the theoretical sinusoidal wave form is considered to be a disturbance in the power quality of the electrical grid. The deviation can alter any of the parameters of the waveform: frequency, amplitude, and symmetry among phases. Microgrid, as a part of the electric power system, has to contribute providing an adequate current waveform in grid connected-mode, as well as to guarantee similar voltage features than the standard requirement given for public distribution grids under normal exploitation conditions in islanded mode. Adequate power quality supply is necessary for the correct compatibility between all the devices connected to the same grid. In this paper, the power quality of microgrids is managed using a Model Predictive Control (MPC) methodology which regulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the following microgrids working modes: grid-connected, islanded, and interconnected. The simulation results demonstrate that the proposed methodology improves the transient response in comparison with classical methods in all the working modes, minimizing the harmonic content in the current and the voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems. European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) SUDOESOE3/P3/E0901

Due to the myriad of devices and loads that are collected under the Commercial Building Energy Management Systems (EMS) focused on the Industry 4.0 paradigm, it is important to ensure their proper electrical operation. The power quality here requires a granular monitoring approach, reaching a point where each device connected to the microgrid can diagnose that its power supply is optimal. Otherwise, it can participate cooperatively in decision-making to avoid faults or blackouts in the microgrid. In this work, we present a novel Controller to make smart appliances responsive to the grid, either autonomously or managed under Demand Response policies. Apart from a TRIAC acting as an AC switch, the main advantage lies in its embedded Power quality internet of things (IoT) sensor. It measures a wide spectrum of electrical disturbances, far exceeding the capabilities of other solutions such as the Grid Friendly Appliance Controller, so it is possible to customize a battery of alarms at will, in e.g. according to IEEE-1547. Moreover, although it can act autonomously, its main mission will be to act in coordination, either cooperatively or under the supervision of the EMS. The IoT platform where the controller would be incorporated is also presented. Finally, two case studies are presented to show their capabilities. With the integration in the microgrid of these distributed submetering systems, with wireless connectivity and under standard communication protocols, a further step will be taken in the development of the Digital Energy Platform, and the improvement of the quality of consumption by the user, as well as information support systems for smart meters.

Electricity demand and its typical load pattern are usually affected by many endogenous and exogenous factors to which the generation system must accordingly respond through utility operators. Lockdown measures to prevent the spread of COVID-19 imposed by many countries have led to sudden changes in socioeconomic habits which have had direct effects on the electricity systems. Therefore, a detailed analysis of how confinement measures have modified the electricity consumption in Spain, one of the countries most affected by this pandemic, has been performed in this work. Its electricity consumption has decreased by 13.49% from March 14 to April 30, compared to the average value of five previous years. Daily power demand profiles, especially morning and evening peaks, have been modified at homes, hospitals, and in the total power demand. These changes generate a greater uncertainty for the System Operator when making demand forecasts, but production deviations have increased by only 0.1%, thanks to the presence of a diversified generation mix, which has been modified during this period, increasing the proportion of renewable sources and decreasing CO2 emissions., Highlights • Power demand in Spain has decreased by 13.49% due to lockdown measures by COVID-19. • Power demand profiles have changed its usual form on weekdays and weekends. • Average daily deviations in power predictions have only increased by 0.1%. • Renewable power generation has increased during the lockdown. • In March and April 2020, CO2 emissions have decreased by 32.61% compared to 2019.

Energy efficiency and consumers&rsquo, role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices, secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill, finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers&rsquo, power converters track the power setpoints obtained from the proposed strategy.

espanolEstudios demuestran que la tasa de exito de las asignaturas se fomenta si los alumnos aprecian la interdisciplinariedad entre asignaturas. Por otro lado, el uso de las nuevas tecnologias como el aprendizaje multimedia resulta esencial hoy dia, mas aun si se realiza en un segundo idioma. En el articulo se describen una serie de recursos multimedia bilingues que pretenden servir para mejorar el aprendizaje en asignaturas de ingenieria, permitiendo desarrollar competencias transversales y especificas de las titulaciones de grado de Ingenieria Industrial. El uso de esta experiencia puede suponer importantes mejoras a nivel de titulo, ya que poner de manifiesto el nexo de union entre varias asignaturas ayuda al aprendizaje, ademas de ofrecer una prospeccion de futuro en las diversas asignaturas del grado, lo que motivara y alentara al alumnado en promocionar a los siguientes cursos. Ver una aplicacion practica concreta en 1o curso (o incluso ver en los videos) y saber que seran capaces de realizar el dispositivo con el que se ha realizado dicho ensayo en asignaturas posteriores, animara al alumnado y les pondra imagen a practicas concretas de cursos siguientes, hecho que siempre es demandado por los alumnos durante los primeros cursos. EnglishStudies show that the success rate of the subjects is encouraged if students appreciate the interdisciplinarity between subjects. On the other hand, the use of new technologies such as multimedia learning is essential today, even more if it is done in a second language. The article describes a series of bilingual multimedia resources that aim to serve to improve learning in engineering subjects, allowing to develop transversal and specific competences of Industrial Engineering degree programs. Important improvements can also be achieved at degree’s level, since highlighting the link between various subjects helps learning, as well as offering future prospects within the subjects of the degree, which will motivate and encourage students to be promoted. Seeing a specific practical application in 1st year course and know (or even watch videos) that they will be able to make the device that will be used in later subjects, will therefore encourage the students and will put image to concrete practical lessons of following courses, which is always demanded by the students during the first courses.

There is an outstanding concern from the international standard-setting community about harmonics distortion in the frequency range 2–150 kHz, referred to as supraharmonics. Power electronics has established as a ubiquitous technology, which plays a linchpin role in almost any electrical systems. The classical deterministic pulse-width modulation strategy, entailing at reducing low-frequency harmonics emission (

In power electronics, deterministic pulse width modulation strategies, aimed at reducing the emission of low-frequency harmonics (

Smart grids aim to dramatically change residential area energy systems by creating active grid interaction. Specifically, renewable energies will play a key role when it comes to handling energy storage systems (ESS) at houses within a Smart Community. This paper presents a novel global control strategy for distributed micro-storage energy system. Each home receives the active and reactive power set- points from the Smart Community Energy Management System (SCEMS), providing energy resources to the community in function of the expected demand and production. Once SCEMS requirements are fulfilled, harmonic current components demanded by the household circuitry are injected according to the ESS available capacity. The proposed approach not only causes a local improvement in the power quality of the demanded current, but also contributes to the global power quality consumption of the community. Experimental tests are conducted using a prototype of the proposed ESS charger, validating the control strategies.

Stochastic models for predicting human behavior have become an essential part of the development of demand planning strategies, as well as a high-resolution base information for building simulation software. Due to the close relationship between human presence and consumption, occupancy patterns allow for the recognition of activity peaks, and subsequently, potential maximum demand hours. This contributes to the improvement of control strategies, which combined with the active participation of consumers will drive to major energy savings. In this paper, a novel behavior model for nine economic sectors in Spain has been developed using a Markov Chain methodology that can easily be extrapolated to other locations. The model can generate daily occupancy profiles with a 10-min resolution for the selected sectors, distinguishing between the type of day and type of working hours. The results, which have been validated and compared with other works showing good accuracy, have highlighted the characteristic patterns and maximum occupancy hours of each studied sector. Furthermore, these simulated profiles have been used as input datasets for the estimation of consumption in some selected sectors, illustrating the potential link that can be established between occupancy profiles and energy usage by means of different modeling techniques.

Heating and cooling consumption is one of the most significant terms in the total supply, which may come to represent half of the total demand in European countries. These appliances are supplied by a wide range of sources, being electrical devices of special interest in the Smart Grid. Current tools allow the assessment of the consumption with a high accuracy, nevertheless, they lack the temporal resolution or low-level details to study advanced control techniques. In this context, bottom-up stochastic models are a main tool to simulate high-resolution demand profiles. This paper presents a 1-min resolution model for electricity demand of heating and cooling appliances. The system is based on the simulation of individual households considering variables such as the number of residents, location, type of day (weekday or weekend) and date. It was used to simulate daily profiles which showed two main demand peaks, one during mornings and another during dinner time, for heating, and a high demand during midday for cooling consumption. Moreover, annual simulations depicted the importance of cooling appliances, which despite having a lower annual demand, can overcharge the grid with their concurrent utilisation, highlighting the usefulness of this tool for studying the impact of these devices.

The current upward trend in large-scale integration of distributed energy resources (DER) in distribution networks has fueled interest in knowing their power quality issues (PQ), even in nearly real-time scale when possible. This trend involves researching new protection strategies that contribute to the reduction of supply interruptions times, which will ultimately result in greater energy efficiency. Based on this scenario, a research and development platform for testing the behavior of the electronic systems involved in supervision and control is presented. The proposed design is based on the experience acquired by our research group in the last ten years. The main objective is to reproduce on a laboratory scale the behavior of a 9 kVA power generation system associated with active loads. This setup allows reproducing all kind of perturbation that might happen in the actual grid and to verify the behavior of the system devoted to their detection. Therefore, the main interest of this system is to test the behavior of the physical devices, as well as the advanced algorithms for real-time detection. As one of its main characteristics, the setup makes use of a high-level synchronization system in order to guarantee the precise estimation of the response times. The most advanced objective is related to the global estimation of the response time considering the communication delays. Thus, the test platform contributes to the PQ, the protection, and the advanced communications, aspects which stand out as highly relevant in the imminent wide expansion of the Smart Grids. A case study, as well as the experimental results, are presented, which show and address the performance of the entire system.

It is usual in literature that power sharing among grid-forming sources of an isolated microgrid obeys their energy rating, instead of economic agreements between stakeholders, and circulating energy among them is usually avoided. However, these energy interchanges make strong sense and classical power sharing methods must be reformulated in the context of prosumer-based microgrids. This paper proposes a secondary control method for a prosumer-based low-voltage nanogrid that allows for energy interchange between prosumers, where storage systems, together with PV generators, are the controllable grid-forming sources. A power flow technique adapted to islanded microgrids is used for secondary control algorithm and the whole hierarchical control strategy for the prosumer converter is simulated and validated. This hierarchical control consists of three stages: tertiary control plans the energy interchange among prosumers, secondary obtains different voltage and power setpoints for each of the grid-forming sources, and, finally, primary control guarantees stable voltage and frequency values within the nanogrid with droop rules. Inner control loops for the power converter are also defined to track setpoints and assure stable performance. Simulation tests are carried out, which prove the stability of the proposed methods and the accuracy of the setpoint tracking.

Although the power electronics converters involved in glass furnace boosting process may be as diverse as require the electric heating system, in tin bath and annealing lehr heating systems a thyristor based AC-AC voltage converter controlled by integral cycle control (ICC) is often employed, distributed over the multiple heating zones. The ICC is widely used due to its multiples benefits such as low transient current, increased reliability and limited harmonic distortion, but noteworthy at low frequency. The goal of this research is to establish an optimal demand response (DR) and power quality (PQ) strategy using a hybrid technique derived from ICC through a straightforward algorithm characterized by a low computational cost that provides successful results for both thermal stability as well as from the PQ and DR point of view.

Smart grids play an important role in the development and modernization of the electric power system. Smart grids deal with the integration of information technology and the electric grid. In this context, a communication system is needed to achieve the main goals of smart grids. Interoperability between the systems has become an important issue. To achieve this, the IEC 61850 standard models the electric power system by means of a hierarchical data model based on semantics definitions. Existing and future applications for the electric power system heavily depend on the performance of the communication system. Several factors can affect the performance of the IEC 61850 communications. Previous studies have already analyzed the influence of network related aspects. In this research, some factors related to data and data modeling of IEC 61850 have been identified that influence the performance of the IEC 61850 devices, through the processing time. The study presented in this paper analyzes them and proposes practical recommendations to optimize the performance of the system. The results obtained reveal the importance of a proper use of data sets because their influence over the communications performance.

More and more human activities are increasingly dependent on the power quality energy they are supplied. In particular, those loads that have an electronic switching mode power supply (SMPS) and microcontrollers or microprocessors are very susceptible to de-energization of the AC line as voltage monitoring takes place on the DC end and may not have enough time to cope with a voltage event involving energy loss. There have been many proposals that analyze power quality or current consumption, even those using limited resources hardware or the classic formulas of discrete root mean square (RMS). In this proposal, an approximation to the problem by means of an RMS value estimator that uses as a base a microcontroller of basic range and low economic cost and algorithms of calculation of very low computational cost that elude complex arithmetic operation to controllers, such as powers or roots, is presented. The results of the experiments to which the proposal was subjected show its ability to provide an estimation of the RMS value of voltage with sufficient precision or an event alarm in less time than the options analyzed. The validation tests and functional comparison of the prototype which were carried out support its viability.

The present article proposes an adaptive neurofuzzy control method that is capable of compensating for model uncertainty and parametric changes of Synchronous Reluctance Machines (SRMs), as well as for lack of measurements for the SRMs state vector elements. First it is proven that the SRM's model is a differentially flat one. This means that all its state variables and its control inputs can be written as differential functions of key state variables which are the so-called flat outputs. Moreover, this implies that the flat output and its derivatives are linearly independent. By exploiting differential flatness properties it is shown that the 4-th order SRM model can be transformed into the linear canonical form. For the latter description, the new control inputs comprise unknown nonlinear functions which can be identified with the use of neurofuzzy approximators. The estimated dynamics of the electric machine is used by a feedback controller thus establishing an indirect adaptive control scheme. Moreover, to improve the robustness of the control loop a supplementary control term is computed using H-infinity control theory. Another problem that has to be dealt with comes from the inability to measure the complete state vector of the SRM. Thus, a state-observer is implemented in the control loop. The stability of the considered observer-based adaptive control approach is proven using Lyapunov analysis.

The increase in the number of facilities embarking on the fully automated production journey, under the Industry 4.0 paradigm, is making critical the guarantee of good health of their operations. Uninterrupted operations & business continuity are key requirements for any plant, for which power quality (PQ) play an extremely important role. Voltage sags are one of the most critical PQ disturbances in the grid, so that early detection and analysis are essential for improving the continuity of supply and enhancing the productivity in any automatized facility. An Internet of Things (IoT) solution that distributed monitors and controls this critical variable would be extremely beneficial for the management of the facility and thus ensures seamless business operations. This paper describes the development of a low-cost embedded device for detecting and analyzing the voltage sags and Voltage Root Mean Square of the AC power supply. The proposed IoT sensor is built around a flexible microcontroller, which manages an energy metering IC through SPI Interface, uprising its original capabilities by including new sophisticated software functionality. Additionally, it wirelessly communicates with a cloud-based platform to allow the storage and supervision of the different events for the entire facility.

With the emergence of Internet of Things (IoT), appears the necessity of a protocol to handle the data flow of all the smart devices. HTTP (Hypertext Transfer Protocol) is the most used protocol. But, in recent years, new protocols are emerging. Nowadays, the Message Queue Telemetry Transport (MQTT) is one of the most used protocol within IoT, due to its low power and bandwidth consumption. This work presents the implementation of an MQTT protocol into LabVIEW platform providing a powerful tool to assess students training into novel protocols and applications under the IoT paradigm.

This paper describes a laboratory setup that allows students to gain knowledge in Electrical Engineering and Electronics. In this paper, the developed lab for power quality (PQ) monitoring is described. The set is implemented using the National Instrument (NI) Educational Laboratory Virtual Instrumentation Suite (ELVIS) and custom-made load boards. The software is based on the NI LabVIEW development environment. The set has been developed and employed for teaching electronic and instrumentation courses within the Master’s degree in Distributed Renewable Energies from the University of Cordoba. The paper shows different practical lessons that are designed to perform typical PQ measurements such as power, frequency, RMS or THD.

Power quality (PQ) has been increasing interest in recent years due to the high integration of sources of distributed generation (DG) in the electricity market. This interest has led a considerable amount of research into early detection of faults and their in processes in operation. This paper presents the design and implementation of an educational platform based on embedded electronic systems to operate in real time in DG framework. We contend that the causal relationship between building model solutions and developing products from these solutions provides a sound basis for industrial strength software engineering. The proposed test bench enables both undergraduate and graduate electrical and electronics engineering students to analyze the modelling techniques developed for event analysis. The architecture has been specially configured to assess the efficiency of algorithms when they are executed in real time. A case study, as well as the experimental results, are presented, which show and address the performance of the educational platform.

Energy efficiency improvement is one of the most important targets to be achieved on every society as a whole and in power system in particular. This target could bring renewable energy resources technologies closer to the ‘Smart Energy Communities’; that would allow the active participation of the so-called ׳prosumers׳ in a genuinely open market. Accordingly, there is a need to develop strategies to motivate and involve citizens in the future electricity system. Information and Communication Technologies (ICT) have an important role to play in reducing the energy intensity and therefore increasing the energy efficiency of the economy, contributing to sustainable growth. The emphasis of the smart grid in social media through mobile apps could give utilities a collection of new instruments to improve their customer engagement. To preserve long-term customer fidelity, utilities should transform themselves from energy suppliers to energy service advisor. Utilities can draw useful lessons from most valuable brands on how to expand their digital customer experience initiatives. Therefore, this paper discusses and offers guidance for utilities in creating a social roadmap for the smart grid, emphasizing prosumer role.

The residential sector represents about 30% of the total energy demand in Europe. Included in this percentage, lighting consumption is one of the basic end uses in all households and it may come to represents 15–20% of the total electricity bill. This figure can be reduced using advanced control techniques or more efficient lighting technologies, requiring previous detailed information about current consumption patterns. In this context, bottom-up stochastic models are established as the main tools to study new energy savings. In this paper, a high-resolution stochastic model for simulating lighting consumption profiles was developed, obtaining both daily active and reactive instantaneous power demand profiles, with a 1-min resolution. The model takes into account the number of household residents and differentiates between weekdays and weekends. Moreover, the monthly and annual amounts of electricity demanded by lighting in Spanish households were simulated. The proposed model was also used to quantify the impact of LED technology's penetration into domestic lighting systems on consumption patterns. Research has revealed the existence of two consumption peaks matching with morning and evening. Although these peaks are hard to shift since they are due to human behavior, they are easy to reduce through the improvement of lighting systems.

As the number of facilities adopting a Building Management System under the Industry 4.0 paradigm increases, it is critical to ensure the good health of their operations. Business continuity and uninterrupted operations are key requirements for any building, for which Power Quality and Supply Reliability sophisticated monitoring can play an extremely important role. Submetering, as opposed to bulk-metering, implies measuring power consumption for individual units or appliances in a building complex. An Internet of Things mesh network, which brings ubiquitous power quality submetering inside the entire facility, would be extremely beneficial for the management of the building thus ensuring seamless business operations. This work describes a novel low-cost Internet of Things sensor for measuring and analyzing power quality at the input of any individual Alternating Current (AC) appliance, providing an early detection and analysis system which controls those critical variables inside the facility and leads to anticipate faults with early-stage alerts based on on-time data streams treatment. Moreover, the recorded power quality parameters that are processed in the Cloud system can help to reduce energy consumption, as power quality disturbances can be automatically analyzed and even compared to standard values. The proposed Internet of Things sensor will help users to detect most power quality steady-state and events disturbances, while monitoring the energy consumption. This Internet of Things Power Quality sensor is built around a flexible microcontroller, which manages an energy metering Integrated Circuit (IC) through Serial Peripheral Interface (SPI), increasing its original capabilities by including new sophisticated software functionality. Additionally, it wirelessly communicates with a cloud-based Internet of Things Platform to allow the storage and supervision of the different power quality events for the entire facility. An example of the access to the data is also included.

The international standardization community is currently reviewing the harmonic distortion limits in the frequency range 2 to 150 kHz, referred to as supraharmonics. Power electronics has emerged as a ubiquitous technology, which also plays a critical role in this subject. Broadly speaking, the reduction of emission in the lower-frequency ranges appears to result in an increase in supraharmonic emission by equipment. The paper presents measurements of waveform distortion from several illustrative facilities and loads. Until now, power electronics converter has been an important source of waveform distortion, even in this frequency range, but, as stated in this paper, can also be the key to mitigate distortion, when technologies like multilevel converter and random pulse with modulation are properly employed.

Smart metering devices have become an essential part in the development of the current electrical network toward the paradigm of Smart Grid. These meters present in most of the cases, functionalities whose analysis capabilities go further beyond the basic automated meter readings for billing purposes, integrating home or building area networks (HAN/BAN), alarms and power quality indicators in some cases. All those characteristics make this widely spread equipment a free, accurate and flexible source of information that can replace expensive and dedicated devices. Therefore, this paper presents the integration of a commercial advanced metering infrastructure (AMI) in the context of a smart building with an energy management system (EMS). Furthermore, power quality monitoring based on this AMI is explained. All the details regarding the implementation in a laboratory scale application, as well as the obtained results, are provided.

De-energization is a concerning issue in household or industrial environments where sensitive loads are installed. Block-out services may occur if a microcontrol's fault event takes place after the SMPS output sustain time. Although several mitigating methods have been implemented to minimize its effects, a fast ac mains voltage inlet monitoring procedure could detect such an event before the sustain time ends. Hence, a method for in-advance quick detection is needed. In this paper, that proposed method is briefly spread and some results proving its speed, reliability and accuracy are shown.

One of the main problems with it comes to the integration of distributed energy resources is the estimation and prediction of the energetic demand that those energy sources must be able to supply [1]. This is especially difficult nowadays due to the upward trend observed in all the projections for the energy consumption for major energy end-use sectors (residential, commercial, industrial, and transportation) [2]. In this context, stochastic modelling techniques have been presented as the most suitable ones as they are able to create the diversity needed to take into account the behaviour of the residents while keeping the general observed statistical trends [7]. These methods are based on the individual modelling of each appliance and the consumer behaviour patterns so not can only high-resolution profiles be generated, but also the end-use of the energy can be specifically determined. That is not even possible nowadays with the new advanced metering infrastructure, unless non-intrusive load monitoring programmes are applied, which also makes these models a great tool for the assessment of energy policies and the impact of including new appliances at home [8]. In this chapter, the usage and application of stochastic demand models will be addressed, with a focus on the prediction and estimation of the energy consumption, the assessment of different demand response policies and the integration of distributed energy resources at small scale. For this aim, first, a short overview of the main modelling techniques employed in the residential sector will be given to the reader in order to contextualise this type of models. Subsequently, the methodology used in these models will be exposed, showing the different blocks that usually built up the estimation process. Finally, the usability for assessing energy policies and integrating distributed energy resources will be discussed.