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St. Louis post-dispatch --- St. Louis post-dispatch.

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Contenu : Les infirmier(e)s des unités de soins ont souvent des difficultés à reconnaître facilement et traiter de manière optimale un patient qui se dégrade. Dans cette situation, elles ne savent pas toujours qui appeler et il leur est parfois difficile de mobiliser un médecin, surtout en période de garde, pour venir examiner ces patients critiques. Pourtant, tout retard dans la reconnaissance et dans la prise en charge de ces urgences peut être la cause d'événements indésirables graves (arrêts cardiaques, admissions non­ programmées aux soins intensifs et décès inopinés).L'European Resuscitation Council conseille aux hôpitaux de proposer une procédure de détection et de prise en charge rapide des patients qui se détériorent, comme par exemple, des Rapid Respons System. Ces derniers sont composés de 2 branches que sont la détection rapide, grâce par exemple à des scores d'alerte précoce, et des soins optimaux en fonction de l'état clinique du patient. Le but de ce travail est d'implémenter ce type de procédure au sein du CHR Haute Senne de Soignies. Le National Early Warning Score a été utilisé comme outil de détection précoce, qui était à ce jour inexistant au sein de l'institution, et la réponse médicale a été structurée en fonction du score et des ressources disponibles au niveau local. Méthodes : La procédure a été créée et testée auprès d'une cohorte de 246 patients admis au sein d'une unité pilote entre le 6 mars 2017 et le 5 juin 2017.Leur séjour a été étudié afin d'évaluer si l'usage du NEWS était aisé pour les soignants, influençait l'appel médicalisé aigu, pouvait prédire la survenue d'un événement indésirable grave dès l'admission du patient, mais également si ceux admis de manière non programmée aux soins intensifs avaient un score élevé. Enfin, au terme de cette expérience pilote, des propositions de recommandations seront émises pour une éventuelle diffusion plus large dans l'institution. Résultats : Certains utilisateurs ont retiré des avantages de cette procédure, comme une impression de meilleure surveillance du patient, une aide à la décision pour appeler une aide médicale et une meilleure visibilité des paramètres. Cependant, plusieurs facteurs ont été identifiés comme ayant une influence péjorative sur le fonctionnement optimal du score, engendrant un sentiment de charge de travail accrue. Dans ce cadre, la procédure n'a pas correctement été suivie, tant dans la fréquence de suivi des paramètres, que dans l'appel à l'aide médicale aigüe. De plus, dans le cadre de cette expérience pilote, le NEWS ne s'est pas avéré aussi discriminant qu'espéré. Cependant, moyennant probablement certaines adaptations et un meilleur encadrement des utilisateurs, ce score pourrait être un bon outil d'aide à la décision en complément du sens clinique des soignants.

Emergency Medical Dispatch --- Problem-Based Learning --- Emergency Medical Services

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Abstract. The growing problem of climate change has risen global concerns about the way of using natural resources and has brought several initiatives such as the EU 2030 targets towards the cleaner energy production and the emissions reduction. The high penetration of Renewable energy Sources (RES) is considered as a remedy to this issue. However the higher integration of RES to the system is a rather challenging task for it's different components. Therefore there is a growing need for the introduction of higher flexibility to the system as a countermeasure for the unpredictable and unstable RES generation. 

In this report the flexibility potential coming from a portfolio of smart Domestic Hot Water Tanks (DHWT) is investigated. Their ability to provide Active Demand Response (ADR) in order to contribute to the more cost efficient electric system's operation is studied. These systems could allow to modify their electrical load pattern without affecting the final, thermal energy service they deliver, thanks to the thermal inertia of the system. The creation of a general model able to simulate the operation of DHWT is developed. This model is intended to be used in a unit commitment and power dispatch model in order to evaluate the contribution of Demand Side Management (DSM) into the higher RES integration. To accomplish this task first two detailed modeling approaches were followed. The first was was a Rule-Based Control (RBC) strategy able to simulate several thousands individual DHWT. This model was computationally efficient and allowed the simulation of 20000 tanks for 5 days with a sub-hourly operating time scale. The second analytical approach was a linear program (LP) that performed the same computation but for a decreased amount of tanks due to the high computational burden. In both models the ability of a centrally controlled (by an aggregator) portfolio to provide capacity and activation reserves are explored. The two approaches are compared in order to identify benefits and limitations. These models were the main guide towards the modeling of a Virtual Storage Plant (VSP) model. This model is able to follow and simulate the behavior of an increased number of DHWT without much detail. The assumptions made in the VSP model formulation are further explained. The ability of this model to perform in standard conditions and to participate in Demand Response operation are validated through the detailed models. This process lead to the creation of a VSP model that is able to simulate the behavior of a fleet of DHWT. The flexibility that can be offered along with the rate of RES penetration can be investigated by the integration of this model to Dispa-SET, a unit commitment and power dispatch model able to simulate the electric system.

Finally a part of this thesis is related to the improvement of the Dispa-SET simulations and the overall computed system's cost. This was performed by collecting yearly generation data available at Entso-e's website from thousands of generation units in Europe. Afterwards several methods were developed in order to acquire important real-time values for the simulation parameters. These methods are tailor made and are explained in detail. To our knowledge this is the first attempt to collect and process real time generation data for European units.

Demand side management --- Virtual storage plant --- Renewable energy sources --- Unit commitment and economic dispatch --- Power system --- Domestic hot water tank --- Ingénierie, informatique & technologie > Energie

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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

localization --- reactive power optimization --- model predictive control --- CNN --- long short term memory (LSTM) --- meter allocation --- particle update mode --- combined economic emission/environmental dispatch --- glass insulator --- emission dispatch --- genetic algorithm --- grid observability --- defect detection --- feature extraction --- parameter estimation --- incipient cable failure --- active distribution system --- boiler load constraints --- multivariate time series --- particle swarm optimization --- inertia weight --- VMD --- NOx emissions constraints --- spatial features --- penalty factor approach --- self-shattering --- differential evolution algorithm --- short term load forecasting (STLF) --- genetic algorithm (GA) --- economic load dispatch --- least square support vector machine --- Combustion efficiency --- electricity load forecasting

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Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue.

Technology: general issues --- Benders’ decomposition --- distributed generation planning (DGP) --- two-stage stochastic mixed-integer linear programming (MILP) --- renewable energy sources (RES) --- micro-grid --- energy management system --- IEC 61850 --- 5G --- LoRa --- multi-agent system --- isolated AC/DC hybrid microgrid --- two-layer consensus method --- dynamic economic dispatch --- distributed hierarchical strategy --- MILP optimization --- allocation --- renewable energy sources --- system modeling --- energy storages --- energy curtailment --- mathematical programming --- small scale distributed generation --- distribution networks --- active power management --- battery degradation --- electric vehicles --- electric vehicles aggregator --- electricity markets --- stochastic programming --- combined economic emission dispatch --- environment-based demand response --- emission constraints --- penalty factor --- weighting update artificial bee colony --- distribution network planning --- energy storage system --- multi-objective optimization --- optimal location --- risk assessment --- flexibility --- distributed energy resources --- distribution system operators --- local services --- system services --- arbitrage --- frequency control --- Benders’ decomposition --- distributed generation planning (DGP) --- two-stage stochastic mixed-integer linear programming (MILP) --- renewable energy sources (RES) --- micro-grid --- energy management system --- IEC 61850 --- 5G --- LoRa --- multi-agent system --- isolated AC/DC hybrid microgrid --- two-layer consensus method --- dynamic economic dispatch --- distributed hierarchical strategy --- MILP optimization --- allocation --- renewable energy sources --- system modeling --- energy storages --- energy curtailment --- mathematical programming --- small scale distributed generation --- distribution networks --- active power management --- battery degradation --- electric vehicles --- electric vehicles aggregator --- electricity markets --- stochastic programming --- combined economic emission dispatch --- environment-based demand response --- emission constraints --- penalty factor --- weighting update artificial bee colony --- distribution network planning --- energy storage system --- multi-objective optimization --- optimal location --- risk assessment --- flexibility --- distributed energy resources --- distribution system operators --- local services --- system services --- arbitrage --- frequency control