A Model Predictive Control Approach for Energy Management in Micro-Grid Systems

International audienceMG 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

Modeling and Performance Evaluation of Photovoltaic Systems

International audienceThe 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

Towards a Battery Characterization Methodology for Performance Evaluation of Micro-Grid Systems

International audienceStand-alone micro-grid (MG) systems have been widely studied and installed especially in remote and rural regions. These systems are composed of renewable energy sources (RES) and storage devices. The main component in these systems is the storage device (e.g., batteries), which is used in order to store extra-produced energy during the day for eventual usage at night. However, battery's characterization according to their operating conditions is required to better identify its parameters and study their performance and behavior within MG systems. This paper introduces an instrumentation platform for battery characterization and modeling using recent sensing/actuating equipment. The platform was first deployed for gathering important battery's parameters, which are then used for building a simplified battery model. This model is then integrated into the MG system for evaluation and validation. Simulation results compared to experimental results show the accuracy of the developed battery model

Towards a context-driven platform using IoT and big data technologies for energy efficient buildings

International audienceContext-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