Uncertainty in wind energy forecasting

Wind energy is the energy source that contributes most to the renewable energy mix of European countries. While there are good wind resources throughout Europe, the intermittency of the wind represents a major problem for the deployment of wind energy into the electricity networks. To ensure grid security a Transmission System Operator needs today for each kilowatt of wind energy either an equal amount of spinning reserve or a forecasting system that can predict the amount of energy that will be produced from wind over a period of 1 to 48 hours. In the range from 5m/s to 15m/s a wind turbine’s production increases with a power of three. For this reason, a Transmission System Operator requires an accuracy for wind speed forecasts of 1m/s in this wind speed range. Forecasting wind energy with a numerical weather prediction model in this context builds the background of this work. The author’s goal was to present a pragmatic solution to this specific problem in the ”real world”. This work therefore has to be seen in a technical context and hence does not provide nor intends to provide a general overview of the benefits and drawbacks of wind energy as a renewable energy source. In the first part of this work the accuracy requirements of the energy sector for wind speed predictions from numerical weather prediction models are described and analysed. A unique set of numerical experiments has been carried out in collaboration with the Danish Meteorological Institute to investigate the forecast quality of an operational numerical weather prediction model for this purpose. The results of this investigation revealed that the accuracy requirements for wind speed and wind power forecasts from today’s numerical weather prediction models can only be met at certain times. This means that the uncertainty of the forecast quality becomes a parameter that is as important as the wind speed and wind power itself. To quantify the uncertainty of a forecast valid for tomorrow requires an ensemble of forecasts. In the second part of this work such an ensemble of forecasts was designed and verified for its ability to quantify the forecast error. This was accomplished by correlating the measured error and the forecasted uncertainty on area integrated wind speed and wind power in Denmark and Ireland. A correlation of 93% was achieved in these areas. This method cannot solve the accuracy requirements of the energy sector. By knowing the uncertainty of the forecasts, the focus can however be put on the accuracy requirements at times when it is possible to accurately predict the weather. Thus, this result presents a major step forward in making wind energy a compatible energy source in the future.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

The Rpf/DSF system and the regulation of virulence in the plant pathogen Xanthomonas campestris

The full virulence of Xanthomonas campestris pv. campestris (Xcc) to plants depends upon cell-to-cell signalling mediated by the signal molecule DSF (for diffusible signal factor), that has been characterised as cis-11-methyl-2-dodecenoic acid. DSF-mediated signalling regulates motility, biofilm dynamics and the synthesis of particular virulence determinants. The synthesis and perception of the DSF signal molecule involves products of the rpf (regulation of pathogenicity factors) gene cluster. DSF synthesis is fully dependent on RpfF, which encodes a putative enoyl-CoA hydratase. A two-component system, comprising the complex sensor histidine kinase RpfC and the HD-GYP domain regulator RpfG, is implicated in DSF perception. The HD-GYP domain of RpfG is a phosphodiesterase working on cyclic di-GMP; DSF perception is thereby linked to the turnover of this intracellular second messenger. The full range of regulatory influences of the Rpf/DSF system and of cyclic di-GMP in Xcc has yet to be established. In order to further characterise the Rpf/DSF regulatory network in Xcc, a proteomic approach was used to compare protein expression in the wildtype and defined rpf mutants. This work shows that the Rpf/DSF system regulates a range of biological functions that are associated with virulence and biofilm formation but also reveals new functions mediated by DSF regulation. These functions include antibiotic resistance, detoxification and stress tolerance. Mutational analysis showed that several of these regulated protein functions contribute to virulence in Chinese radish. Interestingly, it was demonstrated that different patterns of protein expression are associated with mutations of rpfF, rpfC and rpfG. This suggests that RpfG and RpfC have broader roles in regulation other than perception and transduction of DSF. Taken together, this analysis indicates the broad and complex regulatory role of Rpf/DSF system and identifies a number of new functions under Rpf/DSF control, which were shown to play a role in virulence.

Molecular analysis of inflammatory mechanisms associated with Escherichia coli and inflammatory bowel diseases

Inflammatory bowel diseases (IBD), encompasses a range of chronic, immune-mediated inflammatory disorders that are usually classified under two major relapsing conditions, Crohn’s Disease (CD) and ulcerative colitis (UC). Extensive studies in the last decades have suggested that the etiology of IBD involves environmental and genetic factors that lead to dysfunction of epithelial barrier with consequent deregulation of the mucosal immune system and inadequate responses to gut microbiota.Over the last decade, the microbial species that has attracted the most attention, with respect to CD etiology, is Eschericia coli. In CD tissue, E. coli antigens have also been identified in macrophages within the lamina propria, granulomas, and in the germinal centres of mesenteric lymph nodes of patients. They have been shown to adhere to and invade intestinal epithelial cells whilst also being able to extensively replicate within macrophages. Through the work of genome-wide association studies (GWAS), there is growing evidence to suggest that the microbial imbalance between commensal and pathogenic bacteria in the gut is aided by a defect in the innate immune system. Autophagy represents a recently investigated pathway that is believed to contribute to the pathogenesis of CD, with studies identified a variant of the autophagy gene, ATG16L1, as a susceptibility gene. The aim of my thesis was to study the cellular and molecular mechanism promoted by E.coli strains in epithelial cells and to assess their contribution to IBD pathology. To achieve this we focused on developing both an in vitro and in vivo model of AIEC infection. This allowed us to further our knowledge on possible mechanisms utilised by AIEC that promoted their survival, as well as developing a better understanding of host reactions. We demonstrate a new survival mechanism promoted by E.coli HM605, whereby it induces the expression of the anti-apoptotic proteins Bcl-XL and BCL2, all of which is exacerbated in an autophagy deficient system. We have also demonstrated the presence of AIEC-induced inflammasome responses in epithelial cells which are exacerbated in an autophagy deficient system and expression of NOD-like receptors (NLRs) which might mediate inflammasome responses in vivo. Finally, we used the Citrobacter rodentium model of infectious colitis to identify Pellino3 as an important mediator in the NOD2 pathway and regulator of intestinal inflammation. In summary, we have developed robust and versatile models of AIEC infection as well as provide new insights into AIEC mediated survival pathways. The collected data provides a new perception into why AIEC bacteria are able to prosper in conditions associated with Crohn’s disease patients with a defect in autophagy.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Placental contribution to the origins of sexual dimorphism in health and diseases: sex chromosomes and epigenetics

Sex differences occur in most non-communicable diseases, including metabolic diseases, hypertension, cardiovascular disease, psychiatric and neurological disorders and cancer. In many cases, the susceptibility to these diseases begins early in development. The observed differences between the sexes may result from genetic and hormonal differences and from differences in responses to and interactions with environmental factors, including infection, diet, drugs and stress. The placenta plays a key role in fetal growth and development and, as such, affects the fetal programming underlying subsequent adult health and accounts, in part for the developmental origin of health and disease (DOHaD). There is accumulating evidence to demonstrate the sex-specific relationships between diverse environmental influences on placental functions and the risk of disease later in life. As one of the few tissues easily collectable in humans, this organ may therefore be seen as an ideal system for studying how male and female placenta sense nutritional and other stresses, such as endocrine disruptors. Sex-specific regulatory pathways controlling sexually dimorphic characteristics in the various organs and the consequences of lifelong differences in sex hormone expression largely account for such responses. However, sex-specific changes in epigenetic marks are generated early after fertilization, thus before adrenal and gonad differentiation in the absence of sex hormones and in response to environmental conditions. Given the abundance of X-linked genes involved in placentogenesis, and the early unequal gene expression by the sex chromosomes between males and females, the role of X- and Y-chromosome-linked genes, and especially those involved in the peculiar placenta-specific epigenetics processes, giving rise to the unusual placenta epigenetic landscapes deserve particular attention. However, even with recent developments in this field, we still know little about the mechanisms underlying the early sex-specific epigenetic marks resulting in sex-biased gene expression of pathways and networks. As a critical messenger between the maternal environment and the fetus, the placenta may play a key role not only in buffering environmental effects transmitted by the mother but also in expressing and modulating effects due to preconceptional exposure of both the mother and the father to stressful conditions.

Investigation of factors driving the costs of operating the 2020 Irish power system with large-scale wind generation

The main goal of this work is to determine the true cost incurred by the Republic of Ireland and Northern Ireland in order to meet their EU renewable electricity targets. The primary all-island of Ireland policy goal is that 40% of electricity will come from renewable sources in 2020. From this it is expected that wind generation on the Irish electricity system will be in the region of 32-37% of total generation. This leads to issues resulting from wind energy being a non-synchronous, unpredictable and variable source of energy use on a scale never seen before for a single synchronous system. If changes are not made to traditional operational practices, the efficient running of the electricity system will be directly affected by these issues in the coming years. Using models of the electricity system for the all-island grid of Ireland, the effects of high wind energy penetration expected to be present in 2020 are examined. These models were developed using a unit commitment, economic dispatch tool called PLEXOS which allows for a detailed representation of the electricity system to be achieved down to individual generator level. These models replicate the true running of the electricity system through use of day-ahead scheduling and semi-relaxed use of these schedules that reflects the Transmission System Operator's of real time decision making on dispatch. In addition, it carefully considers other non-wind priority dispatch generation technologies that have an effect on the overall system. In the models developed, three main issues associated with wind energy integration were selected to be examined in detail to determine the sensitivity of assumptions presented in other studies. These three issues include wind energy's non-synchronous nature, its variability and spatial correlation, and its unpredictability. This leads to an examination of the effects in three areas: the need for system operation constraints required for system security; different onshore to offshore ratios of installed wind energy; and the degrees of accuracy in wind energy forecasting. Each of these areas directly impact the way in which the electricity system is run as they address each of the three issues associated with wind energy stated above, respectively. It is shown that assumptions in these three areas have a large effect on the results in terms of total generation costs, wind curtailment and generator technology type dispatch. In particular accounting for these issues has resulted in wind curtailment being predicted in much larger quantities than had been previously reported. This would have a large effect on wind energy companies because it is already a very low profit margin industry. Results from this work have shown that the relaxation of system operation constraints is crucial to the economic running of the electricity system with large improvements shown in the reduction of wind curtailment and system generation costs. There are clear benefits in having a proportion of the wind installed offshore in Ireland which would help to reduce variability of wind energy generation on the system and therefore reduce wind curtailment. With envisaged future improvements in day-ahead wind forecasting from 8% to 4% mean absolute error, there are potential reductions in wind curtailment system costs and open cycle gas turbine usage. This work illustrates the consequences of assumptions in the areas of system operation constraints, onshore/offshore installed wind capacities and accuracy in wind forecasting to better inform the true costs associated with running Ireland's changing electricity system as it continues to decarbonise into the near future. This work also proposes to illustrate, through the use of Ireland as a case study, the effects that will become ever more prevalent in other synchronous systems as they pursue a path of increasing renewable energy generation.