Structure de contrôle hiérarchique pour l'optimisation de l'autoconsommation des micro-réseaux de bâtiments

International audienceRenewable energy sources are increasingly deployed as distributed generators, restructuring the traditional electrical grid toward smart grids. Their intermittent power generation makes difficult the development of a complete carbon-free MicroGrid. Hence, aiming to keep the safe operation of a building MicroGrid (BMG) under stochastic variations in the power imbalance while respecting the requirements imposed by grid regulation to maximise self-consumption, a three-level energy management system was designed. The BMG main grid interaction aspects are assured by the two upper control level throughout a hierarchical model predictive control, whereas the power sharing among all electric vehicles is ensured via a deterministic state machine. The entire hierarchical control structure was tested through simulation in MATLAB under different scenarios. Results prove that the proposed control allows the BMG to keep its self-consumption index within expected boundaries despite environmental disturbances

A review of hierarchical control for building microgrids

Building microgrids have emerged as an advantageous alternative for tackling environmental issues while enhancing the electricity distribution system. However, uncertainties in power generation, electricity prices and power consumption, along with stringent requirements concerning power quality restrain the wider development of building microgrids. This is due to the complexity of designing a reliable and robust energy management system. Within this context, hierarchical control has proved suitable for handling different requirements simultaneously so that it can satisfactorily adapt to building environments. In this paper, a comprehensive literature review of the main hierarchical control algorithms for building microgrids is discussed and compared, emphasising their most important strengths and weaknesses. Accordingly, a detailed explanation of the primary, secondary and tertiary levels is presented, highlighting the role of each control layer in adapting building microgrids to current and future electrical grid structures. Finally, some insights for forthcoming building prosumers are outlined, identifying certain barriers when dealing with building microgrid communities

Real-time Parameters Identification of Lithium-ion Batteries Model to Improve the Hierarchical Model Predictive Control of Building MicroGrids

Energy storage systems are key elements for enabling the design of MicroGrids in buildings, specially to deal with stochastic renewable energy resources and to promote peak shifting. However, inaccuracies in the batteries' mathematical models due to temperature and ageing effects can reduce the performance of a MicroGrid system. To tackle these uncertainties, this article presents a two-level hierarchical model predictive controller empowered with a data-driven algorithm for real-time model identification of Lithium-ion batteries. The objective is to enhance their state of charge estimation and to make their maximum use without damaging them. The results demonstrate that it improves up to three times the accuracy of state-of-charge estimation and increases about 3% the annual building MicroGrid selfconsumption rate. Furthermore, the division of the building MicroGrid energy management system into two hierarchical levels soften the drawbacks arise from the inaccuracies of day-ahead data prediction while reducing the computational cost. The proposed architecture guarantees higher energetic autonomy indexes than a conventional rule-based controller in all scenarios under study

Identification en temps réel des paramètres des batteries pour améliorer le contrôle par modèle prédictif des micro-réseaux dédiés aux bâtiments

Les systèmes de stockage sont des éléments clefs pour permettre la conception de micro-réseaux dédiés aux bâtiments. Cependant, les imprécisions des modèles mathématiques des batteries, dû aux effets de la température et de leur vieillissement peuvent réduire les performances d’un système micro-réseaux. Cet article présente un contrôleur par modèle prédictif doté d'un algorithme d'identification en temps réel des modèles des batteries pour mieux estimer leur état de charge afin d’exploiter au maximum les batteries sans les endommager. Les résultats démontrent que l'algorithme proposé associé au nouveau modèle pour l’estimation de l’état de charge des batteries est capable d’améliorer jusqu’à trois fois la précision des modèles de l’estimation de l’état de charge des batteries Li-ion, et d’augmenter jusqu’à 3% l’indice d’autoconsommation annuel d’un micro-réseau dédié aux bâtiments

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

Aiming to take full advantage of Electric Vehicles' (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles

Autonomous observer of hydrogen storage to enhance a model predictive control structure for building microgrids

Hydrogen energy storage has emerged as a promising technology to improve the integration of renewable energy sources in building microgrids. However, inaccuracies in the modelling of fuel cells and electrolysers reduce the performance of building microgrids' energy management system. To improve the flexibility of building microgrids, this paper proposes to associate a two-level hierarchical model predictive controller empowered with an Autonomous Observer of Hydrogen Storage (AOHS). This novel observer evaluates the hydrogen production and consumption rates, storing little past data and needing no tuning of the parameters. Relying only on instantaneous data measurement, the algorithm can estimate the tank's level of hydrogen with an average relative error inferior to 2 %, even under measurement noise. A case-study based on a building microgrid currently under construction serves as the basis for all simulations. The performance of the AOHS is evaluated by comparing the self-consumption rates of the case-study when governed by two-level energy management system: one level using a fixed parameters model and the other one equipped with the proposed AOHS algorithm. Results show that the microgrid associated to the AOHS has better self-consumption compared to the microgrid with fixed parameters, as well as a better robustness regarding the measurement noise and modelling error. Furthermore, this algorithm demonstrates a planning function as it facilitates the energy planning from the aggregator's point of view and the external grid management

Change in diaphragmatic morphology in single-lung transplant recipients: a computed tomographic study

Introduction: The influence of lung disease on the diaphragm has been poorly studied. The study aimed to evaluate the diaphragm morphology (height and thickness) in single-lung transplantation (SLTx), using computed tomography (CT), by assessing the evolution of the hemidiaphragm of the transplanted and the native side.Methods: Patients who underwent single lung transplantation in our center (Marseille, France) between January 2009 and January 2022 were retrospectively included. Thoracic or abdominal CT scans performed before and the closest to and at least 3 months after the surgery were used to measure the diaphragm crus thickness and the diaphragm dome height.Results: 31 patients mainly transplanted for emphysema or pulmonary fibrosis were included. We demonstrated a significant increase in diaphragm crus thickness on the side of the transplanted lung, with an estimated difference of + 1.25 mm, p = <0.001, at the level of the celiac artery, and + 0.90 mm, p < 0.001, at the level of the L1 vertebra while no significant difference was observed on the side of the native lung. We showed a significant reduction in the diaphragm height after SLTx on the transplanted side (−1.20 cm, p = 0.05), while no change on the native side (+0.02 cm, p = 0.88).Conclusion: After a SLTx, diaphragmatic morphology significantly changed on the transplanted lung, while remaining altered on the native lung. These results highlights that an impaired lung may have a negative impact on its diaphragm. Replacement with a healthy lung can promote the recovery of the diaphragm to its anatomical morphology, reinforcing the close relationship between these two organs

Follow-up after radiological intervention in oncology: ECIO-ESOI evidence and consensus-based recommendations for clinical practice

Interventional radiology plays an important and increasing role in cancer treatment. Follow-up is important to be able to assess treatment success and detect locoregional and distant recurrence and recommendations for follow-up are needed. At ECIO 2018, a joint ECIO-ESOI session was organized to establish follow-up recommendations for oncologic intervention in liver, renal, and lung cancer. Treatments included thermal ablation, TACE, and TARE. In total five topics were evaluated: ablation in colorectal liver metastases (CRLM), TARE in CRLM, TACE and TARE in HCC, ablation in renal cancer, and ablation in lung cancer. Evaluated modalities were FDG-PET-CT, CT, MRI, and (contrast-enhanced) ultrasound. Prior to the session, five experts were selected and performed a systematic review and presented statements, which were voted on in a telephone conference prior to the meeting by all panelists. These statements were presented and discussed at the ECIO-ESOI session at ECIO 2018. This paper presents the recommendations that followed from these initiatives. Based on expert opinions and the available evidence, follow-up schedules were proposed for liver cancer, renal cancer, and lung cancer. FDG-PET-CT, CT, and MRI are the recommended modalities, but one should beware of false-positive signs of residual tumor or recurrence due to inflammation early after the intervention. There is a need for prospective preferably multicenter studies to validate new techniques and new response criteria. This paper presents recommendations that can be used in clinical practice to perform the follow-up of patients with liver, lung, and renal cancer who were treated with interventional locoregional therapies