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Graphical abstract Highlights • We propose a regression-tree-based method for modeling electricity price formation. • The explanatory variables are extracted from publicly accessible energy related data. • The energy-related data are free and published by the TSO in a graphical interface. • The model shows good accuracy in predicting the price formation. • It also allows for a non-linear analysis of the dependence of price on predictors. Abstract Until recently, detailed information on the power system state to estimate future spot prices by regression analysis was generally restricted to qualified parties. However, to ensure transparency in operation, the Spanish Transmission System Operator has launched an informative web in which a sizable amount of real-time energy-related data can be consulted through a graphical interface. Undoubtedly, this provides the opportunity for non-qualified parties to develop applications and algorithms in which price forecast and maybe knowledge about how price is determined are required. This paper approaches the use of data extracted from that interface with two aims: the prediction of the day-ahead price in a simple way, and the exploration of the influence that the underlying energy drivers have on it. For the prediction we specified a quantile regression model based on Gradient Boosted Regression Trees. It improves the accuracy over multiple linear regression models at the cost of more complexity, and still it has simpler specification and tuning compared to other machine learning approaches. The calculated metrics show that our model produces remarkably low prediction errors when using the median as point prediction method (RMSE = 2.78 €/MWh, MAE = 1.94 €/MWh, and MAPE = 0.059). Interestingly, the quantile regression model also allows to inherently define prediction intervals, with a different interpretation of accuracy. Our results show that on average 90% of times the prediction error will not exceed 6.8 €/MWh. We also implemented a partial dependence analysis on that model. This implementation—as far as we know the first time employed to analyze the formation of electricity prices—has shown to be of significant usefulness in detecting highly non-linear relationships. [ABSTRACT FROM AUTHOR]

Highlights • We analyze the economic value of participating in multi-stage electricity markets. • The party submitting bids is a wind power producer with generic energy storage system. • The market analyzed as a case analysis has eight sub-markets. We condensate them into one. • We consider uncertainty in power production and prices. We propose a common modeling basis. • We analyze a number of scenarios comprising power and price differences. Abstract This paper provides a methodology to compute the optimal bidding by a wind power producer in a multi-stage market. The methodology is not restricted to the two-stage markets often reported in the literature—a day-ahead bid submission followed by an adjustment in the imbalance market. Instead, it allows studying any number of markets operating on the same dispatch hour. Particularly, this paper analyzes part of the Spanish market, covering the day-ahead, the six intraday, and the imbalance market. They are markets with different schedules, but this paper shows that by simply rearranging the market prices into a single equivalent market and employing the increments of power as bids, the calculations are visibly simplified; despite the different scope and gate closures. The methodology also includes a dynamic programming approach that relies on the equivalent market data to provide an optimal bidding sequence and its economic value when (i) uncertain prices and wind power production are considered, and (ii) energy storage is employed. As an application, the proposed methodology is employed to analyze the revenues derived by a wind power producer using ESS in the Spanish market. [ABSTRACT FROM AUTHOR]

This article presents the results of the monitoring the political conjuncture that led to the election of the first woman president of the Costa Rican republic. Starting from an analytical recuperation of the main events that establish the total set of the 2010 electoral process, the aim is to integrally order an interactive sequence of actors and phenomenon that would allow understanding from a sociopolitical perspective of lattice, scenarios, the actors and librettos that in the political electoral drama we recently experienced, and nowadays live in its consequences the restricted Costa Rican democracy. [ABSTRACT FROM AUTHOR]

Axial spondyloarthritis (axSpA) comprises a spectrum of chronic inflammatory manifestations affecting the axial skeleton and represents a challenge for diagnosis and treatment. Our objective was to generate a set of evidence-based recommendations for the management of axSpA for physicians, health professionals, rheumatologists and policy decision makers in Pan American League of Associations for Rheumatology (PANLAR) countries. Grading of Recommendations, Assessment, Development and Evaluation-ADOLOPMENT methodology was used to adapt existing recommendations after performing an independent systematic search and synthesis of the literature to update the evidence. A working group consisting of rheumatologists, epidemiologists and patient representatives from countries within the Americas prioritized 13 topics relevant to the context of these countries for the management of axSpA. This Evidence-Based Guideline article reports 13 recommendations addressing therapeutic targets, the use of NSAIDs and glucocorticoids, treatment with DMARDs (including conventional synthetic, biologic and targeted synthetic DMARDs), therapeutic failure, optimization of the use of biologic DMARDs, the use of drugs for extra-musculoskeletal manifestations of axSpA, non-pharmacological interventions and the follow-up of patients with axSpA. This Evidence-Based Guideline presents the first Pan American League of Associations for Rheumatology recommendations for the management of axial spondyloarthritis, addressing therapeutic targets, the use of pharmacological and non-pharmacological interventions and monitoring of patients. [ABSTRACT FROM AUTHOR]

This paper describes an approach to compute the maximum value of energy storage systems (ESS) in grid applications under uncertain energy prices. The value obtained is based on an optimal operation (consisting of charge/discharge sequences) of the ESS. In other words, it is the maximum value that may be obtained when the ESS charge/discharge sequence is adapted to the expected operational conditions. To obtain that optimal value, this paper describes a dynamic program approach, with the particularity that the switching decisions are optimized considering an uncertain price evolution and a dynamic calculation of the aging cost. A practical implementation of this approach is proposed, in which the problem is conveniently sliced into matrices corresponding to single decisions. It is shown that such an arrangement, combined with shift and re-indexing operators, provides a fast solution to the optimization problem consisting of a huge number of decision evaluations. The algorithm is then applied to a number of European electricity markets, with a particular focus on arbitrage. The particularities of the algorithm solutions are analyzed, and it is shown that not considering the imperfect foresight and the aging impacts leads to considerable errors in valuing an ESS. [ABSTRACT FROM AUTHOR]

This paper proposes the use of state space models to generate scenarios for the analysis of wind power plant (WPP) generation capabilities. The proposal is rooted on the advantages that state space models present for dealing with stochastic processes; mainly their structural definition and the use of Kalman filter to naturally tackle some involved operations. The specification proposed in this paper comprises a structured representation of individual Box–Jenkins models, with indications about further improvements that can be easily performed. These marginal models are combined to form a joint model in which the dependence structure is easily handled. Indications about the procedure to calibrate and check the model, as well as a validation of its statistical appropriateness, are provided. Application of the proposed state space models provides insight on the need to properly specify the structural dependence between wind speeds. In this paper the joint and marginal models are smoothly integrated into a backward–forward sweep algorithm to determine the performance indicators (voltages and powers) of a WPP through simulation. As a result, visibly heavy tails emerge in the generated power probability distribution through the use of the joint model—incorporating a detailed description of the dependence structure—in contrast with the normally distributed power yielded by the margin-based model. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative computation method of the levelized cost of energy of distributed wind power generators. Unlike in the conventional procedures, it includes time of commencement as an optimization variable. For that purpose, a methodology from Longstaff and Schwartz’s dynamic program for pricing financial American options is derived, which provides the ability to find the optimum time and value while coping with uncertainty revenues from energy sales and variable capital costs. The results obtained from the analysis of wind records of 50 sites entail that the conventional levelized cost of energy can be broken down into an optimum, minimum (time-dependent) value and a penalty for early exercising, which can be employed to define investment strategies and support policies. [ABSTRACT FROM AUTHOR]

In the context of decaying capital cost and uncertain revenues, prospective valuation of a wind power distributed generation (DG) project is difficult. The conventional net present value (NPV) presents a static picture that does not account for the value of waiting for better market conditions to proceed with a DG investment. On the contrary, real options (RO) analysis does account for the managerial flexibility to switch between options over the investment horizon. In this paper we argue that the value of a DG wind-based project can be revisited by means of Longstaff–Schwartz method, originally intended for the evaluation of American financial options. The adaption of this method to the wind power DG setting provides a means for (i) efficiently dealing with the several stochastic processes involved (spot electricity prices and possibly various wind speed processes) avoiding the curse of dimensionality, (ii) accounting for the decaying capital cost of DG, and (iii) solving the perfect foresight problem presented by Monte Carlo conventional simulations. We present in this paper the procedure to follow when applying the method to the wind power DG setting. Particularly, we discuss the standardization of the wind speed and spot price processes, and the advantages of building a state space model that includes all the correlated processes by adequately transforming Box–Jenkins and Ornstein–Uhlenbeck models. Also we discuss the representation of the capital cost forecast by means of learning curves. On the whole, we put together this multivariate setting and show how it is translated to the Longstaff–Schwartz framework. The developed methodological approach allows us analyzing the sensitivity of the RO value to the site and DG capacity factor, the risk-free rate, the current forward projections of global installed capacity, and the aggregation of DGs under a common owner. On the whole, we conclude that the RO analysis is best employed to analyze DG settings in which the NPV yields a close to negative assessment: low power production, for instance. In such cases, RO analysis shows advantages in producing delayed optimal investment times. [ABSTRACT FROM AUTHOR]

In clusters of wind generators spread over small geographic areas, the spatial correlation of wind power production is strong. Simulation of joint power production in such cases—such as for instance for determining the available power in a microgrid—is flawed if the correlation is not properly defined. Several methods have been proposed in the literature for producing scenarios of correlated samples; mostly focused on wind speed. In this paper we analyze three popular choices: classical Monte Carlo (with correlation induced by Cholesky factorization), Latin Hypercube Sampling (with correlation induced by rank sorting), and the recent copula theory. We put together a variety of statistical tools to transform an uncorrelated multivariate sample into a correlated one; and supplement other works by introducing a detailed definition of the wind power distribution and by expanding the Archimedean copula analysis to dimensions beyond the bivariate case analyzed in some related works. We analyze a year of wind production of 210 wind site from NREL data base. We cluster them to give a view of prospective microgrids, and employ several statistical techniques to measure the adequacy of the simulated samples to the original measured data. Our results show that, for generation in small geographic areas, the higher the number of generators, the better the wind power dependence structure is described by LHS. On the contrary, copulas—Gumbel or Gaussian for two- and three-dimensional problems, and Gaussian for higher dimensions—are better suited for representing correlated wind speed. The results are different when the generators are spread over large geographic areas. Compared with LHS endowed with rank sorting for inducing correlation, copula theory is in some sense cumbersome to apply for modeling and simulating wind power data. However, simulations can be performed in prospective microgrids in small geographical areas with larger accuracy by means of LHS if wind power is analyzed rather than wind speed. This advantage is lost for large distances or when small number of generators is considered. [ABSTRACT FROM AUTHOR]

In this paper we address the problem of aggregating wind power. The purpose of the methodology presented here is to avoid the assumption of extreme values of correlation, meaning perfect dependence or perfect independence of the production. That is, we accept intermediate values of correlation, which we argue is of special interest for small-scale siting analysis, where the fluctuations of wind power production affect the capacity value or the size of energy storage. We provide a formulation that is based on the integration of the joint probability density function (PDF) of the wind power. We formulate this PDF by means of copula theory in order to cope with the involved representation of the marginal PDFs. As a result, we characterize the PDF of the aggregated wind power and the associated duration curve. We also present a simple formulation of the joint forced outage rate. These serves us for verifying, through a case analysis based on NREL datasets, that in some cases the assumption of extreme dependence in small-scale sites does not hold. [ABSTRACT FROM AUTHOR]

Nailfold videocapillaroscopy (VCP) allows non-invasive assessment of the microcirculation. Adequate training in this field is relevant for rheumatologists. There is increasing evidence of the reliability of VCP findings among different readers. Objective: To evaluate inter- and intra-reader agreement of rheumatologists to identify normal images and systemic sclerosis (SSc) patterns on VCP ("early," "active," and "late" proposed by Cutolo et al.). Thirteen rheumatologists with different experience in nailfold VCP received training to standardize reading criteria. They rated 60 VCP images from healthy and SSc patients at baseline and 4 weeks later, using an electronic platform. The reading of an expert was considered the gold standard. Data were analyzed using Cohen's kappa for concordance and Student's t test and ANOVA to compare kappa means for inter-reader, intra-reader, and inter-pattern readings. Mean inter-reader and intra-reader kappa were 0.45 and 0.49, respectively, (moderate agreement). Kappa scores were higher among experienced vs inexperienced readers (inter-reader kappa 0.58 vs 0.34, p = 0.001, intra-reader kappa 0.65 vs 0.37, p = 0.01). Agreement was substantial (kappa = 0.61) for the identification of normal vs abnormal images and higher for the identification of active (0.48, p = 0.009) and late SSc patterns (0.56, p = 0.008) than for the early SSc pattern (0.35, p = 0.003). There is moderate agreement among rheumatologists for the identification of SSc videocapillaroscopy patterns (higher among experienced rheumatologists) and substantial agreement, regardless of previous experience in VCP, in the identification of normal and abnormal images. Agreement for the identification of active and late patterns is higher than for the early pattern. [ABSTRACT FROM AUTHOR]

An introduction is presented in the editors discusses the various issues within the topic including information exchange, sharing of regulatory experience and joint cooperation between National Regulatory Authorities.