Une contribution Vers une Startegie de Contrôle Prédictif pour la Gestion de l'Energie dans les Systèmes Micro-réseaux : Application dans des Bâtiments Intelligents et Economes en Energie

The general context of this thesis concerns the integration of RESs (Renewable Energy Sources) into smart MGs (micro-grids) for buildings in order to support the continuous growth of buildings’ electricity demands. However, their intermittent nature and unpredictable variability represent the main challenge of their efficient and seamless integration into buildings. Energy storage systems are considered among the most promising technologies that could balance RESs production with buildings’ energy consumption. Electrochemical (batteries) storage systems are the most deployed in buildings. This is due to their multiple advantages, mainly modularity, cleanliness, and high efficiency. However, the unpredictable and discontinuous nature of the power production and consumption make the power management in MG systems a difficult task. Therefore, intelligent control strategies are required for efficient energy management in MG systems.This thesis focuses on the development and deployment of an intelligent and predictive control strategy for energy balance in MG systems. A predictive control approach, named MAPCASTE (Measure, Analyze, Plan, ForeCAST, and Execute), is developed and deployed in real-sitting scenarios. Mainly, MPC (Model Predictive Control) and GPC (Generalized Predictive Control) have been investigated in order to carry out the proposed MAPSASTE. This later was deployed and evaluated by assessing its effectiveness for energy management in MG systems. In particular, modeling, simulation, experimentation, and performance assessment of the deployed MG system, together with the developed control approach, have been performed. Experimentations have been conducted using a real MG platform, which was deployed in the frame of two research & development projects.Le contexte général de cette thèse concerne l’intégration des sources d’énergie renouvelables (RES) dans les micro-réseaux (MG) afin de faire face à la croissance continue de la demande en électricité des bâtiments. Cependant, le caractère intermittent et la variabilité imprévisible des RESs représentent le principal défi à relever afin de permettre leur intégration efficace dans les bâtiments. Les systèmes de stockage d’énergie sont considérés parmi les technologies les plus prometteuses qui pourraient équilibrer la production des RES avec la consommation d’énergie. Les systèmes de stockage électrochimiques (batteries) sont les plus déployés dans les bâtiments. Cela est dû à leurs multiples avantages, principalement la modularité, la propreté et le rendement élevé. Cependant, la nature imprévisible et discontinue de la production et de la consommation d'énergie rend la gestion de l'énergie dans les systèmes MG une tâche difficile. Par conséquent, des stratégies de contrôle intelligentes sont nécessaires pour une gestion efficace de l'énergie dans ces systèmes. Dans la présente thèse, une approche de contrôle intelligent et prédictif est proposée afin de permettre une gestion efficace d’énergie dans les systèmes MG. Cette approche, nommée MAPCASTE (Mesurer, Analyser, Planifier, ForeCAST et Exécuter), est développée et déployée dans des scénarios réels. Principalement, les techniques MPC (Model Predictive Control) et GPC (Generalized Predictive Control) ont été étudiés afin de réaliser l’approche MAPSASTE. Cette dernière a été déployée et son efficacité pour la gestion de l'énergie dans les systèmes MG est évaluée. En particulier, la modélisation, la simulation, l'expérimentation et l'évaluation des performances du système MG déployé, ainsi que l'approche de contrôle développée, ont été effectuées. Des expérimentations ont été menées sur une plateforme MG, qui nous avons déployée dans le cadre de deux projets de recherche

Une contribution Vers une Startegie de Contrôle Prédictif pour la Gestion de l'Energie dans les Systèmes Micro-réseaux : Application dans des Bâtiments Intelligents et Economes en Energie

The general context of this thesis concerns the integration of RESs (Renewable Energy Sources) into smart MGs (micro-grids) for buildings in order to support the continuous growth of buildings’ electricity demands. However, their intermittent nature and unpredictable variability represent the main challenge of their efficient and seamless integration into buildings. Energy storage systems are considered among the most promising technologies that could balance RESs production with buildings’ energy consumption. Electrochemical (batteries) storage systems are the most deployed in buildings. This is due to their multiple advantages, mainly modularity, cleanliness, and high efficiency. However, the unpredictable and discontinuous nature of the power production and consumption make the power management in MG systems a difficult task. Therefore, intelligent control strategies are required for efficient energy management in MG systems.This thesis focuses on the development and deployment of an intelligent and predictive control strategy for energy balance in MG systems. A predictive control approach, named MAPCASTE (Measure, Analyze, Plan, ForeCAST, and Execute), is developed and deployed in real-sitting scenarios. Mainly, MPC (Model Predictive Control) and GPC (Generalized Predictive Control) have been investigated in order to carry out the proposed MAPSASTE. This later was deployed and evaluated by assessing its effectiveness for energy management in MG systems. In particular, modeling, simulation, experimentation, and performance assessment of the deployed MG system, together with the developed control approach, have been performed. Experimentations have been conducted using a real MG platform, which was deployed in the frame of two research & development projects.Le contexte général de cette thèse concerne l’intégration des sources d’énergie renouvelables (RES) dans les micro-réseaux (MG) afin de faire face à la croissance continue de la demande en électricité des bâtiments. Cependant, le caractère intermittent et la variabilité imprévisible des RESs représentent le principal défi à relever afin de permettre leur intégration efficace dans les bâtiments. Les systèmes de stockage d’énergie sont considérés parmi les technologies les plus prometteuses qui pourraient équilibrer la production des RES avec la consommation d’énergie. Les systèmes de stockage électrochimiques (batteries) sont les plus déployés dans les bâtiments. Cela est dû à leurs multiples avantages, principalement la modularité, la propreté et le rendement élevé. Cependant, la nature imprévisible et discontinue de la production et de la consommation d'énergie rend la gestion de l'énergie dans les systèmes MG une tâche difficile. Par conséquent, des stratégies de contrôle intelligentes sont nécessaires pour une gestion efficace de l'énergie dans ces systèmes. Dans la présente thèse, une approche de contrôle intelligent et prédictif est proposée afin de permettre une gestion efficace d’énergie dans les systèmes MG. Cette approche, nommée MAPCASTE (Mesurer, Analyser, Planifier, ForeCAST et Exécuter), est développée et déployée dans des scénarios réels. Principalement, les techniques MPC (Model Predictive Control) et GPC (Generalized Predictive Control) ont été étudiés afin de réaliser l’approche MAPSASTE. Cette dernière a été déployée et son efficacité pour la gestion de l'énergie dans les systèmes MG est évaluée. En particulier, la modélisation, la simulation, l'expérimentation et l'évaluation des performances du système MG déployé, ainsi que l'approche de contrôle développée, ont été effectuées. Des expérimentations ont été menées sur une plateforme MG, qui nous avons déployée dans le cadre de deux projets de recherche

Wireless Sensor Networks for Smart Micro-Grids

International audienceSmart micro-grid has emerged as a new infrastructure to further enhance existing power grid through the integration of renewable energy sources and communication technologies. In fact, smart micro-grid infrastructure is characterized by renewable energy integration, automatic metering infrastructure, distribution automation and complete monitoring and control of the entire power grid. The monitoring is based on a set of sensors, which are used for collecting data production, data consumption and weather conditions. These data should be transmitted by a wireless network to a main location or sink for visualization and analysis. For this reasons, it is important to develop a self-configured wireless sensor network able to handle efficiently the massive amount of data. This paper investigates the ZigBee network in the context of micro grid by comparing two routing topologies

Magnetic Signature-Based Model Using Machine Learning for Electrical and Mechanical Faults Classification of Wind Turbine Drive Trains

International audienceSignal processing and fault indicators analysis are essential for efficient fault detection, classification, and diagnosis of wind turbines. Accordingly, existing works proposed the installation of multiple intrusive sensors (e.g., current, voltage, frequency) for data collection in order to detect and classify the faults in wind turbine drive train (WTDT). However, these sensors are scattered on the drive train and have a limited local reach on its components making it technically difficult to install. Therefore, signals from these sensors are not able to detect multi parameter phenomena such as coupling of the mechanical and electrical components of the drive train which contains essential fault information. This work proposes the use of magnetic signatures as fault condition indicators of the complete drive train due to the ability of contactless measurement of this signal without opening the main components of the drive train. This is achieved by performing non-destructive magnetic modeling and analysis of the entire drive train. The air gap magnetic flux density of the wind generator is demonstrated as a good fault condition indicator for different common faults occurring on the gearbox, bearings, and the generator. The proposed model is validated using a supervised machine learning classification algorithm in a way to distinguish between electrical and mechanical faults

An Energy Management Strategy for DC Microgrids with PV/Battery Systems

International audienceRecently, 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

Photovoltaic Power Forecasting Using Neural Networks for Short and Medium-Term Dependencies

International audienceRenewable energy sources are pivotal in evolving microgrid systems. Users can generate, utilize, and store electrical power in a local setting by incorporating sustainable energy sources and storage systems. The inherent unpredictability of these sources necessitates innovative technologies like machine learning and the Internet of Things for effective control, upkeep, and integration with the existing electrical grid. A crucial hurdle in this integration, particularly for photovoltaic (PV) systems, is the creation of a precise power forecasting tool. This work introduces a long short-term memory (LSTM) algorithm tailored for multistep-ahead forecasting of PV power. It explores both LSTM and convolutional LSTM (Conv-LSTM) models for short to medium-term PV power prediction. The aim is to enhance PV power forecasting accuracy, extending the time horizon of forecasts while maintaining reasonable error margins and processing efficiency. Various criteria are assessed to demonstrate the effectiveness of the proposed algorithms. Factors like processing duration and machine resource requirements for training and testing phases are key in choosing optimal neural network parameters. Results from this study highlight the efficacy of the LSTM model in short and medium-term PV power forecasting, showcasing its potential in renewable energy integration into the power grid

Constant Power Load Stabilization in DC Microgrids Using Continuous-Time Model Predictive Control

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

Performance Evaluation of Fixed and Sun Tracking Photovoltaic Systems

International audienceTracking photovoltaic (TPV) systems have been proposed to get the maximum production during the whole day. In fact, the TPV is more efficient by varying the modules position, the production takes its maximum value 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 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

Constant Power Load Stabilization in DC Microgrids Using Continuous-Time Model Predictive Control

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

Economic feasibility study of standalone community microgrid in 37 cities of the USA

International audienceCommunity microgrids are applicable universally because they can decentralise grid-connected areas and electrify remote areas. Moreover, they can provide energy security by improving the reliability and resiliency of electric distribution while diminishing the infrastructure costs of preserving or constructing electric power generation, transmission, and distribution assets. Using renewable energy sources (RESs) maybe incentivised through the government and utilities, which can help make such projects economically viable. A model has been developed to calculate the costs and benefits of building a standalone community microgrid in various cities in the United States, powered by wind turbines and photovoltaic (PV) panels where the battery is the backup. Using the System Advisor Model, the sizing of the microgrid system was optimised. This microgrid community's capital, operation, and maintenance costs were calculated using various per unit costs obtained from previous research. Real-estate data were collected from websites to calculate the average price of land and houses. An automated tool was created to calculate the developer's profit and house price subsidy for building such communities. The result demonstrated that several states in the USA could be profitable for developers to build and homebuyers to be a member of this microgrid community. Additionally, this microgrid community has the objective of real/near power supply during disaster and emergency events (e.g., coastal/riverine flooding, tsunamis, earthquakes, wildfires, possible damage, and terrorist attacks). The benefit is not only the direct power generation cost but also the advantage of keeping some critical services operational during catastrophic events that cause electricity blackout