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International audience; Recently, direct current (DC) microgrids have gained more attention over alternating current (AC) microgrids due to the increasing use of DC power sources, energy storage systems and DC loads. However, efficient management of these microgrids and their seamless integration within smart and energy efficient buildings are required. This paper introduces an energy management strategy for a DC microgrid, which is composed of a photovoltaic module as the main source, an energy storage system (battery) and a critical DC load. The designed MG includes a DC-DC boost converter to allow the PV module to operate in MPPT (Maximum Power Point Tracking) mode or in LPM (Limited Power Mode). Furthermore, the system uses a DC-DC bidirectional converter in order to interface the battery with the DC bus. The proposed control strategy manages the power flow among different components of the microgrid. It takes the battery lifetime into consideration by applying constraints to its charging/discharging currents and state-of-charge (SoC). The proposed system is simple and efficient in supplying DC loads, since as it's not using complex algorithms either for MPPT or for energy management. The studied DC microgrid is designed and modeled using Matlab/Simulink software. The load demand is satisfied while ensuring good performance and stability of the system. The controller design, analysis, and simulation validation results are presented and discussed under various operating modes.

Despite its advantages over its AC counterparts, DC microgrids present a lot of challenges. One of these challenges is the instability issues caused by constant power loads (CPLs). CPLs deteriorate the system’s performance due to their incremental negative impedance characteristics. In this paper, a DC microgrid composed of a PV/battery system feeding a pure CPL was considered. A continuous-time model predictive control combined with a disturbance observer was applied to the DC–DC bidirectional converter. The purpose of the composite controller is to address the nonlinearity of the CPL and to maintain the stability of the system in a large operating region under load and PV generation variations. To show the performance of the system, several tests were performed under PV power and CPL power variations. Simulation results show good performance in terms of transient response, optimal tracking, and stability in a large operating region.

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

The resent infrastructure of the buildings is becoming socio-technical systems that integrate different heterogeneous entities (e.g., sensors, actuators, lighting, HVAC (Heating, Ventilation, and Air Conditioning), occupants, renewable energy, storage systems), which could interact dynamically and in a collective manner to balance between energy efficiency, occupants’ comfort, sustainability, and adaptability. This new infrastructure is known as the “Micro-grid” (MG) system concept. However, the main challenge for this infrastructure is real-time monitoring and data processing, which requires the use of new information and communication technologies. In addition, incorporating mechanisms and techniques are required in order to have buildings more energy-efficient while ensuring occupants’ comfort by allowing entities interaction for suitable actions (e.g., turning On/Off HVAC and lighting, balancing the fluctuation between power production and consumption). In this work, a new holistic architecture of smart buildings is presented by improving the main layers of MG systems. This architecture is proposed in order to integrate all buildings’ aspects with the main trade-off is to efficiently manage the building while maintaining a suitable occupants’ comfort. In fact, an MG system is structured into three layers following the proposed holistic architecture. More precisely, we shed more light on the MG system’s layer by integrating recent IoT/Big-Data technologies for data gathering, processing, and control. A set of sensors are installed for power measurement in the MG system while the measured data is gathered and transmitted to an IoT/Big-Data platform for analysis, processing, and storing. The main aim is to store a historical of data (e.g., power production/consumption, weather conditions) from the installed MG system with open-access tools.

Buildings are responsible for over 40% of greenhouse gas emissions and represent almost 40% of the total energy consumption. This high percentage of both greenhouse gas emissions and energy consumption is envisaged to increasingly rise in the forthcoming years because of the excessive usage of household equipment and devies, which are required for improving standards of living and satisfying comfort demands. Therefore, it is imperative to minimize the buildings’ energy consumption and reduce their greenhouse gas emissions while maintaining a suitable occupants’ comfort. Thus, a trade-off between the energy consumption's reduction, the occupant's comfort, and the systems’ costs needs to be established. In this chapter, we introduce a holistic approach that takes into consideration the three major aspects of the building, mainly passive building systems, active building systems, and the integration of renewable energy sources and storage systems. A simple case study was established and detailed to show the effectiveness of the proposed holistic approach.

Recent studies showed that energy consumption in buildings could be efficiently reduced by including recent IoT (Internet of Things) and Big-Data technologies into microgrid systems. In fact, three major aspects could be further considered for reducing energy consumption while maintaining a suitable occupants’ comfort, (i) integrating renewable energy sources and storage devices, (ii) integrating programmable and less-energy-consuming equipment, and (iii) deploying innovative information and communication technologies. These aspects might contribute substantially to the improvement of winning and saving energy toward smart and energy-efficient buildings. In this chapter, a microgrid system infrastructure is developed together with a platform for data gathering, monitoring, and processing. We put more emphasis on microgrid systems as crucial infrastructures for leveraging energy-efficient and smart buildings by developing and deploying a holistic IoT/Big-Data platform in which sensing and actuation tasks are performed according to the actual contextual changes. Scenarios are presented in order to show the usefulness of this holistic platform for monitoring, data processing, and control in energy-efficient buildings.

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.

The intermittent nature of renewable energy sources has attracted much interest for integrating storage devices towards the deployment of micro-grid systems. In fact, these devices could be deployed into the system by decoupling energy production from consumption mainly by storing the extra produced energy from RES during the day for eventual usage at night. However, for standalone systems, computing the optimum size of energy production systems and storage devices is required for continuous electricity supply. In this paper, a micro-grid system is modeled, sized, and experimented according to a given consumption profile using a deployed prototype at our EEBLab.

The Wind energy conversion systems (WECS) have been developed in the past few years as renewable energy sources for minimizing greenhouse emissions while reducing power consumption. Despite the importance of modeling and simulation, experimental studies of WECS are required to figure out the behavior of these systems according to the environmental and climatic circumstances (e.g., wind availability). This paper presents the implementation and experimentation of a wind energy conversion model using the permanent magnet synchronous generator (PMSG) according to different wind speeds. Experiments have been conducted with the main goal is to show the accuracy of the Wind turbine model.

Balancing production and demand of energy is the main challenge to integrate renewable energy sources (RES) in micro-grid (MG) systems. However, the variability and the uncertainty nature of the production and the consumption make the system more difficult to control. In fact, weather conditions influence on the production of renewable energy sources while occupancy influences on the power consumption. Therefore, the development of accurate short term forecasts are needed for a seamless integration of RES (e.g. photovoltaic system, wind turbine) together with the traditional electrical grid in MG systems. This paper presents a forecasting model for predicting the power production and consumption in MG systems together with the battery state of charge (SoC). A control strategy is then implemented to balance the Demand/Response by taking into account the forecasted and real-time values. Based on the data collected from a real MG system, simulation results are presented to show the effectiveness of power forecasting for MG control.

Building's active equipment, such as heating/air-conditioning and ventilation, are considered among the main energy-hungry consumers. Control and automation systems have been designed in the past decades for improving the overall building energy efficiency while satisfying the occupants' comfort (e.g., thermal and indoor air quality). For instance, advance control approaches for standalone ventilation systems have been proposed in order to improve the indoor air quality while enhancing the energy cost saving. In this paper, we investigate the use of model predictive control strategy for ventilation systems. Simulations have been conducted and obtained results show the effectiveness of the proposed predictive control approach in reducing energy consumption while maintaining a good indoor air quality.

The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO2 increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building’s loads. It will efficiently manage the usage of energy storage and the utility grid while maximizing RESs power generation. Simulations have been conducted, using real-sitting scenarios, and results are presented to validate the proposed predictive control approach by showing its effectiveness for MG systems control.

Batteries have shown great potential for being integrated in Micro-Grid (MG) systems. However, their integration is not a trivial task and, therefore, requires extensive modeling and simulations in order to efficiently estimating their State-of-Charge (SoC) within MG systems. The work presented in this article focuses mainly on battery modeling for SoC estimation. We put more emphasize on battery system's characterization, modeling, SoC estimation and its integration into MG systems in order to study its performance. In fact, an instrumentation platform, composed of recent and low cost sensing and actuating equipment, is developed in order to identify the battery's parameters and then build the battery model. The simulation results show good agreement with the experimental results, and thus the battery model is validated in both charging and discharging processes. Then, the battery's SoC estimation in both charging and discharging processes is investigated by means of sophisticated methods, such as, Artificial Neural Network, Luenberger observer and Kalman Filter combined with Coulomb counting. The results of these methods are reported and compared to the SoC estimated using the Coulomb counting method in order to end up with the precise method. The modeled battery is afterwards integrated into an MG system, which is deployed into our EEBLab (Energy Efficient Building Laboratory) test-site, for simulation and experimental investigations. Finally, the four algorithms for SoC estimation are included into the instrumentation platform, which is connected to a Lead-acid battery already integrated into the MG system, in order to show and compare their performances and accuracy for online battery's SoC estimation.

The search for new fuels to supersede fossil fuels has been intensified these recent decades. Among these fuels, hydrogen has attracted much interest due to its advantages, mainly cleanliness and availability. It can be produced from various raw materials (e.g., water, biomass) using many resources, mainly water electrolysis and natural gas reforming. However, water electrolysis combined with renewable energy sources is the cleanest way to produce hydrogen while reducing greenhouse gases. Besides, hydrogen can be used by fuel cells for producing both electrical and thermal energy. The aim of this work was towards efficient integration of this system into energy efficient buildings. The system is comprised of a photovoltaic system, hydrogen electrolyzer, and proton exchange membrane fuel cell operating as a cogeneration system to provide the building with both electricity and thermal energy. The system&rsquo, s modeling, simulations, and experimentations were first conducted over a short-run period to assess the system&rsquo, s performance. Reported results show the models&rsquo, accuracy in analyzing the system&rsquo, s performance. We then used the developed models for long-run testing of the hybrid system. Accordingly, the system&rsquo, s electrical efficiency was almost 32%. Its overall efficiency reached 64.5% when taking into account both produced electricity and thermal energy.

M G systems integrate renewable energy sources (RES) together with storage devices for being connected to the traditional electrical grid in order to supply the power to the building's loads. However, management and control approaches are required for seamless integration of these systems in energy efficient buildings. In this paper, a model predictive control approach is introduced for efficient MG operation. The proposed approach allows minimizing the usage of the traditional electric grid for supplying the power to the building's loads by using as much as possible the power generated by RES while optimizing the storage devices operations. A model predictive control (MPC) strategy is proposed in order to balance the power flow in MG system. The deployed strategy controls the charge/discharge current of the battery depending on RES production, and load consumption variability. Simulations have been conducted using real dataset generated from our deployed MG system, and preliminary results show the usefulness of predictive control principles for the efficient operation of MG systems.

Tracking photovoltaic (TPV) systems are used to get the maximum production during the entire day. In fact, the TPV is more efficient by changing the module’s position in respect to the sun’s position. The production takes its maximum values when the PV panels are perpendicular to the direct radiation. This paper investigates the performance of TPV against fixed PV systems. Mainly, we analyzed the performance of two identical PV panels, one is fixed and the another one follows the sun during the day. The fixed panel was positioned with a specific fixed angle of tilt while the second panel would track the sun’s movement from sunrise to sunset with two axis tracking system. Simulations and experiments have been conducted and results showed that TPV is more efficient in terms of power production.

Renewable energy sources (RES) have been promoted to overcome environmental constraints as well as the scarcity of fossil energy sources. In fact, many initiatives have been deployed by integrating these sources (e.g. Photovoltaic panel, Wind, Fuel cell...) in order to increase energy efficiency (i.e., electricity production) in buildings while limiting CO 2 emissions. In parallel to this development, micro-grids have emerged as decentralized systems to allow connecting different decentralized sources in which buildings become active electricity producers in addition to the main traditional electric grid. However, a smarter demand response management system is required to efficiently balancing the energy production, storage, and the electricity consumption. In this paper, a demand/response control approach is introduced by integrating into a holistic platform different RES together with storage devices for supplying electricity to buildings equipment (e.g. ventilation system, lighting…). A prototype was developed and deployed in our EEBLab for experiments and real-testing. Preliminary experimental results are reported and show the usefulness of the proposed holistic approach.

Control approaches of HVAC systems in buildings have been proposed in the past years for minimizing energy consumption and maintaining occupants' comfort. However, recent studies have shown that context-driven control approaches using IoT and Big Data technologies could further improve energy consumption of HVAC systems. In this paper, an intelligent control approach using a state-feedback technique is introduced to regulate the HVAC system according to the actual context. The performance of the proposed control was evaluated in a real test site by deploying a control card that links the controller with the HVAC system. A smart application for real feedback control was also developed and deployed to dynamically adapt the controller to context's changes. Experiments results are reported to demonstrate that this approach is effective in term of energy saving while maintaining a comfortable room temperature.

The intermittent nature and overwhelming variability of the produced electricity from renewable energy sources (RES) constitutes the main challenge for their seamless integration into buildings. Recent studies stated that storage devises, despite their actual high cost, could be used to tackle this issue by balancing the intermittent nature of RES production and the unpredictable building's occupancy. In fact, these storage devises could handle this issue, which is very complex without taking into consideration actual occupancy and environmental context. This paper introduces a demand/response control approach by mainly focusing on micro-grid systems, which are composed of PVs, batteries, and buildings equipment (e.g., ventilation system, lighting, BVAC). A prototype was developed and deployed in a real-setting scenario at our EEBLAB. Experimentations have been performed and results show the usefulness of the proposed control approach for micro-grid systems.

This paper focuses on the problem of the systems prognostic. More precisely, it is about the prognostic of systems subject to stochastic inputs based on the expert's knowledge. The proposed approach consists in assessing the system availability during a mission. This mission is supposed to be the user profile (the environment in which the system will evolve). This profile is given through a partial knowledge provided by the expert. The aim of this work is to estimate the trajectory of system damage and analyze the mission success. Finally, to illustrate the relevance of the proposed methodology, the suspension damage trajectory is treated.

Renewable energy sources (RES) have been largely deployed as green energy producers in order to reduce power consumption while minimizing greenhouse emissions. However, the intermittent nature of RES (e.g., Solar Energy) must be addressed in order to allow their large scale integration in buildings. Standalone PV systems are generally installed in remote and rural regions since they are not connected to the electrical grid. They use mainly storage devices (e.g., batteries) to store produced/extra solar energy during the day for eventual usage at night. Recently, there have been enormous deployments of grid-connected photovoltaic systems, which use solar panels connected to the electricity grid for producing and feeding extra electricity back to the grid. They are also being able to supply electricity to the load when there is no production. In this paper, we are talking the research question aiming at selecting the suitable dimensions for energy production (PV system). We have, therefore, developed a model for each component of the PV system. A grid-connected PV system is sized, modeled and analyzed according to a dedicated consumption profile. Simulation results show the effectiveness of the proposed dimensioning model.

The solar or the Photovoltaic (PV) systems have been developed in the past few years as renewable energy sources in order to reduce power consumption while minimizing greenhouse emissions. However, getting the maximum power output in every type of environmental and climatic circumstances is of most importance and requires efficient modeling and extensive experimental studies. In this work, we present a modeling and experimental study of photovoltaic systems. The developed model uses weather conditions in order to produce the current and the voltage that are required to compute the produced power. Simulations have been conducted to first study the effect of irradiance and temperature on the PVs production. A testbed system was deployed to perform experiments in order to study the effectiveness and the accuracy of the proposed modeling approach in real-setting scenarios.

Heating, ventilating, and air conditioning (HVAC) systems in industrial and commercial buildings constitutes 40% of the world energy consumption. Many recent studies put more emphasize on developing efficient control strategies in order to deliver acceptable occupants' comfort while maintaining optimal energy consumption. This paper introduces a state feedback control approach for heating and air conditioning systems by optimizing set points and operation modes while providing the desired indoor environmen's quality at minimum energy consumption. Simulations have been conducted using the BCVTB framework by developing the interface that allows data exchange between EnergyPlus for building's envelope and MATLAB for integrating the proposed controller. Simulation results are reported to show the potential of the state feedback control approach for saving energy while keeping suitable occupant's comfort.

Autonomous monitoring of electricity consumption requires a metering platform for recording and processing electrical data. Recent advances in IoT tools and Big data technologies allow now the development of holistic platforms for real-time data monitoring and processing. In this paper, we shed further more light on the integration of these technologies into a holistic platform for real-time sensing, processing, and data visualization. A platform prototype was developed and tested for detailed profiling of energy consumption in buildings. It allows measuring and monitoring the electric power consumption of individual appliances. A testbed was deployed in our university building and results are reported to show the usefulness of the proposed platform.

Context-awareness is crucial for leveraging energy-efficient buildings by developing intelligent control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the users' actions and behaviours in up-to-date context taking into account the complex interlinked elements, situations, processes, and their dynamics. In this paper, we introduce a holistic platform that integrates recent sensing/actuating and Big data technologies for monitoring and data processing. The main aim is to develop context-driven control approaches whereby energy consumption, production, and storage could be controlled according to actual situations (e.g., occupancy, occupant behaviour patterns, energy production patterns, and weather data). A platform prototype was deployed in our university test site. Experiments have been conducted and preliminary results show the usefulness of this holistic platform for monitoring and data processing in energy efficient buildings.

Ventilation systems operate at buildings level for enhancing air quality by injecting fresher air from outside into inside buildings. In many situations, occupants have to open doors or windows in order to get fresh air. However, in cold or hot periods, or when there are no windows, ventilation systems automatically act on behalf of occupants by insuring good indoor air quality. More precisely, the ventilation controller performs this task by adjusting fresh air as much as needed based on actual indoor CO 2 concentration. Several approaches have been implemented and deployed in real-setting scenarios, but most of them are either time-triggered or are based on fixed threshold values. In this paper, we propose to use contextual data, mainly indoor/outdoor CO 2 concentration, for developing a Wireless Sensor Network (WSN) based platform for monitoring and ventilation control. A prototype was developed and deployed in a laboratory-based test scenario for ventilation control. Experiments have been conducted and obtained results show the efficiency of the proposed context-driven control approach.

With rapid expansion of wireless ad hoc networks and their interactions with wired networks together with the use of location-based services and the deployment of mobile and wearable sensor devices, it is now possible to develop context-aware applications such as those in healthcare and transportation. These applications could react to the environment changes and users' preference with the main aim is to make their life more comfortable according to their locations, current time and situations. However, handling context changes according to current data and events is a difficult task without supporting tools. Therefore, a platform that includes real-time event/data acquisition and processing is required. In this paper, we first survey existing work related to event processing and big data technologies by highlighting current challenges related to realtime data processing. We then introduce a context-aware platform that merges different technologies such as service-oriented architecture, complex-event processing (CEP) tools, and big data technologies. A sample context-aware application for energy efficient buildings control is presented to highlight the different components of the platform and their interactions.

The easy deployment of wireless sensors allows the development of context-aware applications that could react to the environment changes and users' preferences. For example, information extracted from data gathered using mobile phones and embedded computers in buses and taxis could be used to understand city dynamics in real-time and therefore take mitigation actions. However, gathering and real-time processing of relevant information is still a challenging task. Complex-event processing (CEP) techniques and predictive analytics have been recently proposed for analyzing streaming data in real-time in order to generate fast insights and then take suitable actions according to the environment changes. The work presented in this paper focuses mainly on the performance evaluation of three CEP engines widely used by researchers for semantic and physical streaming data processing. Experiments have been conducted using existing benchmark tools and results are reported to shed more light on the performance these engines for stream data processing.