Advanced forecasting methods for renewable generation and loads in modern power systems

The PhD Thesis deals with the problem of forecasting in power systems, i.e., a wide topic that today covers many and many needs, and that is universally acknowledged to require further deep research efforts. After a brief discussion on the classification of forecasting systems and on the methods that are currently available in literature for forecasting electrical variables, stressing pros and cons of each approach, the PhD Thesis provides four contributes to the state of the art on forecasting in power systems where literature is somehow weak. The first provided contribute is a Bayesian-based probabilistic method to forecast photovoltaic (PV) power in short-term scenarios. Parameters of the predictive distributions are estimated by means of an exogenous linear regression model and through the Bayesian inference of past observations. The second provided contribute is a probabilistic competitive ensemble method once again to forecast PV power in short-term scenarios. The idea is to improve the quality of forecasts obtained through some individual probabilistic predictors, by combining them in a probabilistic competitive approach based on a linear pooling of predictive cumulative density functions. A multi-objective optimization method is proposed in order to guarantee elevate sharpness and reliability characteristics of the predictive distribution. The third contribute is aimed to the development of a deterministic industrial load forecasting method suitable in short-term scenarios, at both aggregated and single-load levels, and for both active and reactive powers. The deterministic industrial load forecasting method is based on multiple linear regression and support vector regression models, selected by means of 10-fold cross-validation or lasso analysis. The fourth contribute provides advanced PDFs for the statistical characterization of Extreme Wind Speeds (EWS). In particular, the PDFs proposed in the PhD Thesis are an Inverse Burr distribution and a mixture Inverse Burr – Inverse Weibull distribution. The mixture of an Inverse Burr and an Inverse Weibull distribution allows to increase the versatility of the tool, although increasing the number of parameters to be estimated. This complicates the parameter estimation process, since traditional techniques such as the maximum likelihood estimation suffer from convergence problems. Therefore, an expectation-maximization procedure is specifically developed for the parameter estimation. All of the contributes presented in the PhD Thesis are tested on actual data, and compared to the state-of-the-art benchmarks to assess the suitability of each proposal

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

The high penetration of photovoltaic (PV) systems led to their growing impact on the planning and operation of actual distribution systems. However, the uncertainties due to the intermittent nature of solar energy complicate these tasks. Therefore, high-quality methods for forecasting the PV power are now essential, and many tools have been developed in order to provide useful and consistent forecasts. This chapter deals with probabilistic forecasting methods of PV system power, since they have recently drawn the attention of researchers as appropriate tools to cope with the unavoidable uncertainties of solar source. A new multi-model probabilistic ensemble is proposed; it properly combines a Bayesian-based and a quantile regression-based probabilistic method as individual predictors. Numerical applications based on actual irradiance data give evidence of the probabilistic performances of the proposed method in terms of both sharpness and calibration

Scalable Recovery-based Adaptation on Quadtree Meshes for Advection-Diffusion-Reaction Problems

We propose a mesh adaptation procedure for Cartesian quadtree meshes, to discretize scalar advection-diffusion-reaction problems. The adaptation process is driven by a recovery-based a posteriori estimator for the $L^2(\Omega)$-norm of the discretization error, based on suitable higher order approximations of both the solution and the associated gradient. In particular, a metric-based approach exploits the information furnished by the estimator to iteratively predict the new adapted mesh. The new mesh adaptation algorithm is successfully assessed on different configurations, and turns out to perform well also when dealing with discontinuities in the data as well as in the presence of internal layers not aligned with the Cartesian directions. A cross-comparison with a standard estimate--mark--refine approach and with other adaptive strategies available in the literature shows the remarkable accuracy and parallel scalability of the proposed approach

Challenges and new trends in power electronic devices reliability

Power electronic devices are expected to play an ever more fundamental role in unlocking the potentialities of smart power systems and in developing more electric ground and air transportation systems. The reliability of power electronic devices at different hierarchical levels (single component, single device, installation and system) becomes a crucial point in this framework, as failures may determine technical, economical and safety issues that should be carefully addressed at the design and maintenance stages. Power electronic devices are subject to thermal, electrical and mechanical stresses, which can be assessed through consolidated, traditional techniques [1,2,3,4]. However, today these devices are expected to operate under challenging environmental conditions (e.g., high altitudes in more electric aircrafts or high temperatures on photovoltaic (PV) installations), undermining the effectiveness of traditional approaches that are typically based on historical failure data, fault rates or past observed scenarios. In fact, the rapid evolution of power electronic technologies and the ever more challenging operating frameworks pose severe limitations on the trustworthiness of available reliability data, as they are typically related to incoherent operating conditions [1,2,3,4]

Evidence of glacial melt water input in the Western Ross Sea (Antarctica) water masses

Ice shelves are believed to control the glacial stability and the Antarctic Ice Sheet balance, thus their melting is a critical issue. One of the recognized drivers of the ice shelf melting is the intrusion of the warm Circumpolar Deep Water (CDW) onto the Antarctic continental shelves. In the Ross Sea: CDW is a primary source of heat, salt, iron (Fe) and nutrients and plays a major role on the shelf biogeochemical processes; CDW intrudes onto the shelf preferably in the western sector, where the local glaciers are potentially exposed to this warm intrusion; CDW, besides contributing directly to the Fe input, may also have an indirect, but relevant role on the Fe bulk by enhancing the ice shelves melting and iceberg calving. The CELEBeR (CDW Effects on glaciaL mElting and on Bulk of Fe in the Western Ross Sea) project aims to investigate the role of the CDW in supplying Fe to the Ross Sea biological system both directly, as one of the main Fe sources, and indirectly by inducing the Fe-rich glacial melt water inputs in the western Ross Sea. Preliminary data on evidence of glacial melt water input in selected areas are here presented

Simultaneous Extraction of Density of States Width, Carrier Mobility and Injection Barriers in Organic Semiconductors

The predictive accuracy of state–of–the–art continuum models for charge transport in organic semiconductors is highly dependent on the accurate tuning of a set of parameters whose values cannot be effectively estimated either by direct measurements or by first principles. Fitting the complete set of model parameters at once to experimental data requires to set up extremely complex multi–objective optimization problems whose solution is, on the one hand, overwhelmingly computationally expensive and, on the other, it provides no guarantee of the physical soundness of the value obtained for each individual parameter. In the present study we present a step–by–step procedure that enables to determine the most relevant model parameters, namely the density of states width, the carrier mobility and the injection barrier height, by fitting experimental data from a sequence of relatively simple and inexpensive measurements to suitably devised numerical simulations. At each step of the proposed procedure only one parameter value is sought for, thus highly simplifying the numerical fitting and enhancing its robustness, reliability and accuracy. As a case study we consider a prototypical n-type organic polymer. A very satisfactory fitting of experimental measurements is obtained, and physically meaningful values for the aforementioned parameters are extracted

Prognostic value of diabetes and metformin use in a real-life population of head and neck cancer patients

IntroductionHead and neck carcinoma (HNC) is a disease with a poor prognosis despite currently available treatments. The management of patients with this tumor is often complicated by several comorbidities. Among these, diabetes is the second most frequent and its influence on the prognosis is not known.MethodsIn this work, we collected data on progression free survival (PFS) and overall survival (OS) of one hundred twenty-three patients with HNC who received biweekly cetuximab maintenance treatment after first-line chemotherapy. We then compared the survival of nondiabetic patients versus diabetics’ one.ResultsSurprisingly, both PFS (4 vs. 5 months, HR 2.297, p < 0.0001) and OS (7 vs. 10 months, HR 3.138, p < 0.0001) were in favor of diabetic patients, even after excluding other clinical confounding factors. In addition, we also studied survivals in patients taking metformin, a widely used oral antidiabetic drug that has demonstrated antitumor efficacy in some cancers. Indeed, diabetic patients taking metformin had better PFS and OS than those not taking it, 7 vs. 5 months (HR 0.56, p = 0.0187) and 11 vs. 8.5 months (HR 0.53, p = 0.017), respectively.DiscussionIn conclusion, real-world outcomes of biweekly cetuximab maintenance remain comparable to clinical trials. The prognostic role of diabetes and metformin was confirmed to be significant in our series, but further prospective studies are needed for a definitive evaluation