Multiagent System Architecture for Short-term Operation of Integrated Microgrids

The forthcoming smart grids are comprised of integrated microgrids operating in grid-connected and isolated mode with local generation, storage and demand response (DR) programs. The proposed model is based on three successive complementary steps for power transaction in the market environment. The first step is characterized as a microgrid’s internal market; the second concerns negotiations between distinct interconnected microgrids; and finally, the third refers to the actual electricity market. The proposed approach is modeled and tested using a MAS framework directed to the study of the smart grids environment, including the simulation of electricity markets. This is achieved through the integration of the proposed approach with the MASGriP (Multi-Agent Smart Grid Platform) system.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Short-term Hydropower Planning System

The Thesis gives a decision support framework that has significant impact on the economic performance and viability of a hydropower company. The studyaddresses the short-term hydropower planning problem in the Nordic deregulated electricity market. The basics of the Nordic electricity market, trading mechanisms, hydropower system characteristics and production planning are presented in the Thesis. The related modelling theory and optimization methods are covered aswell. The Thesis provides a mixed integer linear programming model applied in asuccessive linearization method for optimal bidding and scheduling decisions inthe hydropower system operation within short-term horizon. A scenario based deterministic approach is exploited for modelling uncertainty in market price and inflow. The Thesis proposes a calibration framework to examine the physical accuracy and economic optimality of the decisions suggested by the model. A calibration example is provided with data from a real hydropower system using a commercial modelling application with the mixed integer linear programming solver CPLEX.

Drug-eluting stents compared to bare-metal stents improve short-term survival in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention: a nationwide prospective analysis of the AMIS Plus registry.

BACKGROUND: Recently, it has been suggested that the type of stent used in primary percutaneous coronary interventions (pPCI) might impact upon the outcomes of patients with acute myocardial infarction (AMI). Indeed, drug-eluting stents (DES) reduce neointimal hyperplasia compared to bare-metal stents (BMS). Moreover, the later generation DES, due to its biocompatible polymer coatings and stent design, allows for greater deliverability, improved endothelial healing and therefore less restenosis and thrombus generation. However, data on the safety and performance of DES in large cohorts of AMI is still limited. AIM: To compare the early outcome of DES vs. BMS in AMI patients. METHODS: This was a prospective, multicentre analysis containing patients from 64 hospitals in Switzerland with AMI undergoing pPCI between 2005 and 2013. The primary endpoint was in-hospital all-cause death, whereas the secondary endpoint included a composite measure of major adverse cardiac and cerebrovascular events (MACCE) of death, reinfarction, and cerebrovascular event. RESULTS: Of 20,464 patients with a primary diagnosis of AMI and enrolled to the AMIS Plus registry, 15,026 were referred for pPCI and 13,442 received stent implantation. 10,094 patients were implanted with DES and 2,260 with BMS. The overall in-hospital mortality was significantly lower in patients with DES compared to those with BMS implantation (2.6% vs. 7.1%,p < 0.001). The overall in-hospital MACCE after DES was similarly lower compared to BMS (3.5% vs. 7.6%, p < 0.001). After adjusting for all confounding covariables, DES remained an independent predictor for lower in-hospital mortality (OR 0.51,95% CI 0.40-0.67, p < 0.001). Since groups differed as regards to baseline characteristics and pharmacological treatment, we performed a propensity score matching (PSM) to limit potential biases. Even after the PSM, DES implantation remained independently associated with a reduced risk of in-hospital mortality (adjusted OR 0.54, 95% CI 0.39-0.76, p < 0.001). CONCLUSIONS: In unselected patients from a nationwide, real-world cohort, we found DES, compared to BMS, was associated with lower in-hospital mortality and MACCE. The identification of optimal treatment strategies of patients with AMI needs further randomised evaluation; however, our findings suggest a potential benefit with DES.

Optimal trading of wind power in the short term market

The energy reform, which is happening all over the world, is caused by the common concern of the future of the humankind in our shared planet. In order to keep the effects of the global warming inside of a certain limit, the use of fossil fuels must be reduced. The marginal costs of the renewable sources, RES are quite high, since they are new technology. In order to induce the implementation of RES to the power grid and lower the marginal costs, subsidies were developed in order to make the use of RES more profitable. From the RES perspective the current market is developed to favor conventional generation, which mainly uses fossil fuels. Intermittent generation, like wind power, is penalized in the electricity market since it is intermittent and thus diffi-cult to control. Therefore, the need of regulation and thus the regulation costs to the producer differ, depending on what kind of generation market participant owns. In this thesis it is studied if there is a way for market participant, who has wind power to use the special characteristics of electricity market Nord Pool and thus reach the gap between conventional generation and the intermittent generation only by placing bids to the market. Thus, an optimal bid is introduced, which purpose is to minimize the regulation costs and thus lower the marginal costs of wind power. In order to make real life simulations in Nord Pool, a wind power forecast model was created. The simulations were done in years 2009 and 2010 by using a real wind power data provided by Hyötytuuli, market data from Nord Pool and wind forecast data provided by Finnish Meteorological Institute. The optimal bid needs probability intervals and therefore the methodology to create probability distributions is introduced in this thesis. In the end of the thesis it is shown that the optimal bidding improves the position of wind power producer in the electricity market.

Short-term stromal alterations in the rat ventral prostate following alloxan-induced diabetes and the influence of insulin replacement

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis of ensemble models in the medium term hydropower scheduling

The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed. © 2012 IEEE.

Short-term CO2-C emissions from soil prior to sugarcane (Saccharum spp.) replanting in southern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Short-term expansion planning of radial electrical distribution systems using mixed-integer linear programming

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Drug-eluting stents compared to bare-metal stents improve short-term survival in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention: a nationwide prospective analysis of the AMIS Plus registry

BACKGROUND Recently, it has been suggested that the type of stent used in primary percutaneous coronary interventions (pPCI) might impact upon the outcomes of patients with acute myocardial infarction (AMI). Indeed, drug-eluting stents (DES) reduce neointimal hyperplasia compared to bare-metal stents (BMS). Moreover, the later generation DES, due to its biocompatible polymer coatings and stent design, allows for greater deliverability, improved endothelial healing and therefore less restenosis and thrombus generation. However, data on the safety and performance of DES in large cohorts of AMI is still limited. AIM To compare the early outcome of DES vs. BMS in AMI patients. METHODS This was a prospective, multicentre analysis containing patients from 64 hospitals in Switzerland with AMI undergoing pPCI between 2005 and 2013. The primary endpoint was in-hospital all-cause death, whereas the secondary endpoint included a composite measure of major adverse cardiac and cerebrovascular events (MACCE) of death, reinfarction, and cerebrovascular event. RESULTS Of 20,464 patients with a primary diagnosis of AMI and enrolled to the AMIS Plus registry, 15,026 were referred for pPCI and 13,442 received stent implantation. 10,094 patients were implanted with DES and 2,260 with BMS. The overall in-hospital mortality was significantly lower in patients with DES compared to those with BMS implantation (2.6% vs. 7.1%,p < 0.001). The overall in-hospital MACCE after DES was similarly lower compared to BMS (3.5% vs. 7.6%, p < 0.001). After adjusting for all confounding covariables, DES remained an independent predictor for lower in-hospital mortality (OR 0.51,95% CI 0.40-0.67, p < 0.001). Since groups differed as regards to baseline characteristics and pharmacological treatment, we performed a propensity score matching (PSM) to limit potential biases. Even after the PSM, DES implantation remained independently associated with a reduced risk of in-hospital mortality (adjusted OR 0.54, 95% CI 0.39-0.76, p < 0.001). CONCLUSIONS In unselected patients from a nationwide, real-world cohort, we found DES, compared to BMS, was associated with lower in-hospital mortality and MACCE. The identification of optimal treatment strategies of patients with AMI needs further randomised evaluation; however, our findings suggest a potential benefit with DES.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

ARB: Phase II window of opportunity study of short-term preoperative treatment with enzalutamide in ER-positive and triple-negative breast cancer

The androgen receptor (AR) is expressed in 60-80% of breast cancers (BC) across all molecular phenotypes, with a higher incidence in oestrogen receptor positive (ER+) BC compared to ER negative tumours. In ER+ disease, AR-expression has been linked to endocrine resistance which might be reversed with combined treatment targeting ER and AR. In triple negative BCs (TNBC), preclinical and clinical investigations have described a subset of patients that express the AR and are sensitive to androgen blockade, providing a novel therapeutic target. Enzalutamide, a potent 2nd generation anti-androgen, has demonstrated substantial preclinical and clinical anti-tumour activity in AR+ breast cancer. Short-term preoperative window of opportunity studies are a validated strategy for novel treatments to provide proof-of-concept and define the most appropriate patient population by directly assessing treatment effects in tumour tissue before and after treatment. The ARB study aims to assess the anti-tumour effects of enzalutamide in early ER+ breast cancer and TNBC, to identify the optimal target population for further studies and to directly explore the biologic effects of enzalutamide on BC and stromal cells. Methods: ARB is an international, investigator sponsored WOO phase II study in women with newly diagnosed primary ER+ BC or AR+ TNBC of ≥ 1cm. The study has two cohorts. In the ER+ cohort, postmenopausal patients will be randomised 2:1 to receive either enzalutamide (160mg OD) plus exemestane (50mg OD) or exemestane (25mg OD). In the TNBC cohort, AR+ will receive single agent treatment with enzalutamide (160mg OD). Study treatment is planned for 15–29 days, followed by surgery or neo-adjuvant therapy. Tissue and blood samples are collected before treatment and on the last day of study treatment. The primary endpoint is inhibition of tumour-cell proliferation, as measured by change in Ki67 expression, determined centrally by 2 investigators. Secondary endpoints include induction of apoptosis (Caspase3), circulating hormone levels and safety. ARB aims to recruit ≈235 patients from ≈40 sites in the UK, Germany, Spain and USA. The study is open to recruitment.