Coalition of distributed generation units to Virtual Power Players - a game theory approach

Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

Predicting trabecular bone microdamage initiation and accumulation using a non-linear perfect damage model

Studies evaluating the mechanical behavior of the trabecular microstructure play an important role in our understanding of pathologies such as osteoporosis, and in increasing our understanding of bone fracture and bone adaptation. Understanding of such behavior in bone is important for predicting and providing early treatment of fractures. The objective of this study is to present a numerical model for studying the initiation and accumulation of trabecular bone microdamage in both the pre- and post-yield regions. A sub-region of human vertebral trabecular bone was analyzed using a uniformly loaded anatomically accurate microstructural three-dimensional finite element model. The evolution of trabecular bone microdamage was governed using a non-linear, modulus reduction, perfect damage approach derived from a generalized plasticity stress-strain law. The model introduced in this paper establishes a history of microdamage evolution in both the pre- and post-yield regions

Mitochondrial reactive oxygen species generation triggers inflammatory response and tissue injury associated with hepatic ischemia-reperfusion: Therapeutic potential of mitochondrially targeted antioxidants.

Mitochondrial reactive oxygen species generation has been implicated in the pathophysiology of ischemia-reperfusion (I/R) injury; however, its exact role and its spatial-temporal relationship with inflammation are elusive. Herein we explore the spatial-temporal relationship of oxidative/nitrative stress and inflammatory response during the course of hepatic I/R and the possible therapeutic potential of mitochondrial-targeted antioxidants, using a mouse model of segmental hepatic ischemia-reperfusion injury. Hepatic I/R was characterized by early (at 2h of reperfusion) mitochondrial injury, decreased complex I activity, increased oxidant generation in the liver or liver mitochondria, and profound hepatocellular injury/dysfunction with acute proinflammatory response (TNF-α, MIP-1α/CCL3, MIP-2/CXCL2) without inflammatory cell infiltration, followed by marked neutrophil infiltration and a more pronounced secondary wave of oxidative/nitrative stress in the liver (starting from 6h of reperfusion and peaking at 24h). Mitochondrially targeted antioxidants, MitoQ or Mito-CP, dose-dependently attenuated I/R-induced liver dysfunction, the early and delayed oxidative and nitrative stress response (HNE/carbonyl adducts, malondialdehyde, 8-OHdG, and 3-nitrotyrosine formation), and mitochondrial and histopathological injury/dysfunction, as well as delayed inflammatory cell infiltration and cell death. Mitochondrially generated oxidants play a central role in triggering the deleterious cascade of events associated with hepatic I/R, which may be targeted by novel antioxidants for therapeutic advantage.

Direct-on-line axial flux permanent magnet synchronous generator static and dynamic performance

In distributed energy production, permanent magnet synchronous generators (PMSG) are often connected to the grid via frequency converters, such as voltage source line converters. The price of the converter may constitute a large part of the costs of a generating set. Some of the permanent magnet synchronous generators with converters and traditional separately excited synchronous generators couldbe replaced by direct-on-line (DOL) non-controlled PMSGs. Small directly networkconnected generators are likely to have large markets in the area of distributed electric energy generation. Typical prime movers could be windmills, watermills and internal combustion engines. DOL PMSGs could also be applied in island networks, such as ships and oil platforms. Also various back-up power generating systems could be carried out with DOL PMSGs. The benefits would be a lower priceof the generating set and the robustness and easy use of the system. The performance of DOL PMSGs is analyzed. The electricity distribution companies have regulations that constrain the design of the generators being connected to the grid. The general guidelines and recommendations are applied in the analysis. By analyzing the results produced by the simulation model for the permanent magnet machine, the guidelines for efficient damper winding parameters for DOL PMSGs are presented. The simulation model is used to simulate grid connections and load transients. The damper winding parameters are calculated by the finite element method (FEM) and determined from experimental measurements. Three-dimensional finite element analysis (3D FEA) is carried out. The results from the simulation model and 3D FEA are compared with practical measurements from two prototype axial flux permanent magnet generators provided with damper windings. The dimensioning of the damper winding parameters is case specific. The damper winding should be dimensioned based on the moment of inertia of the generating set. It is shown that the damper winding has optimal values to reach synchronous operation in the shortest period of time after transient operation. With optimal dimensioning, interferenceon the grid is minimized.

The use of future-oriented knowledge in regional innovation processes: Research on knowledge generation, transfer and conversion

The importance of the regional level in research has risen in the last few decades and a vast literature in the fields of, for instance, evolutionary and institutional economics, network theories, innovations and learning systems, as well as sociology, has focused on regional level questions. Recently the policy makers and regional actors have also began to pay increasing attention to the knowledge economy and its needs, in general, and the connectivity and support structures of regional clusters in particular. Nowadays knowledge is generally considered as the most important source of competitive advantage, but even the most specialised forms of knowledge are becoming a short-lived resource for example due to the accelerating pace of technological change. This emphasizes the need of foresight activities in national, regional and organizational levels and the integration of foresight and innovation activities. In regional setting this development sets great challenges especially in those regions having no university and thus usually very limited resources for research activities. Also the research problem of this dissertation is related to the need to better incorporate the information produced by foresight process to facilitate and to be used in regional practice-based innovation processes. This dissertation is a constructive case study the case being Lahti region and a network facilitating innovation policy adopted in that region. Dissertation consists of a summary and five articles and during the research process a construct or a conceptual model for solving this real life problem has been developed. It is also being implemented as part of the network facilitating innovation policy in the Lahti region.

Monte Carlo study of the XY-model on quasi-periodic lattices

Monte Carlo Simulations were carried out using a nearest neighbour ferromagnetic XYmodel, on both 2-D and 3-D quasi-periodic lattices. In the case of 2-D, both the unfrustrated and frustrated XV-model were studied. For the unfrustrated 2-D XV-model, we have examined the magnetization, specific heat, linear susceptibility, helicity modulus and the derivative of the helicity modulus with respect to inverse temperature. The behaviour of all these quatities point to a Kosterlitz-Thouless transition occuring in temperature range Te == (1.0 -1.05) JlkB and with critical exponents that are consistent with previous results (obtained for crystalline lattices) . However, in the frustrated case, analysis of the spin glass susceptibility and EdwardsAnderson order parameter, in addition to the magnetization, specific heat and linear susceptibility, support a spin glass transition. In the case where the 'thin' rhombus is fully frustrated, a freezing transition occurs at Tf == 0.137 JlkB , which contradicts previous work suggesting the critical dimension of spin glasses to be de > 2 . In the 3-D systems, examination of the magnetization, specific heat and linear susceptibility reveal a conventional second order phase transition. Through a cumulant analysis and finite size scaling, a critical temperature of Te == (2.292 ± 0.003) JI kB and critical exponents of 0:' == 0.03 ± 0.03, f3 == 0.30 ± 0.01 and I == 1.31 ± 0.02 have been obtained.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Top-down and Bottom-up influences on ACC activation: Evaluation of a proposed model of the feedback-related negativity

The Feedback-Related Negativity (FRN) is thought to reflect the dopaminergic prediction error signal from the subcortical areas to the ACC (i.e., a bottom-up signal). Two studies were conducted in order to test a new model of FRN generation, which includes direct modulating influences of medial PFC (i.e., top-down signals) on the ACC at the time of the FRN. Study 1 examined the effects of one’s sense of control (top-down) and of informative cues (bottom-up) on the FRN measures. In Study 2, sense of control and instruction-based (top-down) and probability-based expectations (bottom-up) were manipulated to test the proposed model. The results suggest that any influences of medial PFC on the activity of the ACC that occur in the context of incentive tasks are not direct. The FRN was shown to be sensitive to salient stimulus characteristics. The results of this dissertation partially support the reinforcement learning theory, in that the FRN is a marker for prediction error signal from subcortical areas. However, the pattern of results outlined here suggests that prediction errors are based on salient stimulus characteristics and are not reward specific. A second goal of this dissertation was to examine whether ACC activity, measured through the FRN, is altered in individuals at-risk for problem-gambling behaviour (PG). Individuals in this group were more sensitive to the valence of the outcome in a gambling task compared to not at-risk individuals, suggesting that gambling contexts increase the sensitivity of the reward system to valence of the outcome in individuals at risk for PG. Furthermore, at-risk participants showed an increased sensitivity to reward characteristics and a decreased response to loss outcomes. This contrasts with those not at risk whose FRNs were sensitive to losses. As the results did not replicate previous research showing attenuated FRNs in pathological gamblers, it is likely that the size and time of the FRN does not change gradually with increasing risk of maladaptive behaviour. Instead, changes in ACC activity reflected by the FRN in general can be observed only after behaviour becomes clinically maladaptive or through comparison between different types of gain/loss outcomes.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Simulation-Based Finite-Sample Normality Tests in Linear Regressions

In the literature on tests of normality, much concern has been expressed over the problems associated with residual-based procedures. Indeed, the specialized tables of critical points which are needed to perform the tests have been derived for the location-scale model; hence reliance on available significance points in the context of regression models may cause size distortions. We propose a general solution to the problem of controlling the size normality tests for the disturbances of standard linear regression, which is based on using the technique of Monte Carlo tests.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Asymptotic Approximations in the Near-Integrated Model with a Non-Zero Initial Condition

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of the relevant joint moment generating function in this more general framework. We also consider, as alternative approximations, the stochastic expansion of Phillips (1987c) and the continuous time approximation of Perron (1991). We assess how these alternative methods provide or not an adequate approximation to the finite-sample distribution of the least-squares estimator in a first-order autoregressive model. The results show that, when the initial condition is non-zero, Perron's (1991) continuous time approximation performs very well while the others only offer improvements when the initial condition is zero.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.