Data-driven modelling, forecasting and uncertainty analysis of disaggregated demands and wind farm power outputs

Correct analysis of modern power supply systems requires to evaluate much wider ranges of uncertainties introduced by the implementation of new technologies on both supply and demand sides. On the supply side, these uncertainties are due to the increased contributions of renewable generation sources (e.g., wind and PV), whose stochastic output variations are difficult to predict and control, as well as due to the significant changes in system operating conditions, coming from the implementation of various control and balancing actions, increased automation and switching functionalities, and frequent network reconfiguration. On the demand side, these uncertainties are due to the installation of new types of loads, featuring strong spatio-temporal variations of demands (e.g., EV charging), as well as due to the deployment of different demand-side management schemes. Modern power supply systems are also characterised by much higher availability of measurements and recordings, coming from a number of recently deployed advanced monitoring, data acquisition and control systems, and providing valuable information on system operating and loading conditions, state and status of network components and details on various system events, transients and disturbances. Although the processing of large amounts of measured data brings its own challenges (e.g., data quality, performance, and incorporation of domain knowledge), these data open new opportunities for a more accurate and comprehensive evaluation of the overall system performance, which, however, require new data-driven analytical approaches and modelling tools. This PhD research is aimed at developing and evaluating novel and improved data-driven methodologies for modelling renewable generation and demand, in general, and for assessing the corresponding uncertainties and forecasting, in particular. The research and methods developed in this thesis use actual field measurements of several onshore and offshore wind farms, as well as measured active and reactive power demands at several low voltage (LV) individual household levels, up to the demands at medium voltage (MV) substation level. The models are specifically built to be implemented for power system analysis and are actually used by a number of researchers and PhD students in Edinburgh and elsewhere (e.g., collaborations with colleagues from Italy and Croatia), which is discussed and illustrated in the thesis through the selected study cases taken from this joint research efforts. After literature review and discussion of basic concepts and definitions, the first part of the thesis presents data-driven analysis, modelling, uncertainty evaluation and forecasting of (predominantly residential) demands and load profiles at LV and MV levels. The analysis includes both aggregation and disaggregation of measured demands, where the latter is considered in the context of identifying demand-manageable loads (e.g., heating). For that purpose, periodical changes in demands, e.g., half-daily, daily, weekly, seasonal and annual, are represented with Fourier/frequency components and correlated with the corresponding exploratory meteorological variables (e.g., temperature, solar irradiance), allowing to select the combination of components maximising the positive or negative correlations as an additional predictor variable. Convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) are then used to represent dependencies among multiple dimensions and to output the estimated disaggregated time series of specific load types (with Bayesian optimisation applied to select appropriate CNN-BiLSTM hyperparameters). In terms of load forecasting, both tree-based and neural network-based models are analysed and compared for the day-ahead and week-ahead forecasting of demands at MV substation level, which are also correlated with meteorological data. Importantly, the presented load forecasting methodologies allow, for the first time, to forecast both total/aggregate demands and corresponding disaggregated demands of specific load types. In terms of the supply side analysis, the thesis presents data-driven evaluation, modelling, uncertainty evaluation and forecasting of wind-based electricity generation systems. The available measurements from both the individual wind turbines (WTs) and the whole wind farms (WFs) are used to formulate simple yet accurate operational models of WTs and WFs. First, available measurements are preprocessed, to remove outliers, as otherwise obtained WT/WF models may be biased, or even inaccurate. A novel simulation-based approach that builds on a procedure recommended in a standard is presented for processing all outliers due to applied averaging window (typically 10 minutes) and WT hysteresis effects (around the cut-in and cut-out wind speeds). Afterwards, the importance of distinguishing between WT-level and WF-level analysis is discussed and a new six-parameter power curve model is introduced for accurate modelling of both cut-in and cut-out regions and for taking into account operating regimes of a WF (WTs in normal/curtailed operation, or outage/fault). The modelling framework in the thesis starts with deterministic models (e.g., CNN-BiLSTM and power curve models) and is then extended to include probabilistic models, building on the Bayesian inference and Copula theory. In that context, the thesis presents a set of innovative data-driven WT and WF probabilistic models, which can accurately model cross-correlations between the WT/WF power output (Pout), wind speed (WS), air density (AD) and wind direction (WD). Vine Copula and Gaussian mixture Copula model (GMCM) are combined, for the first time, to evaluate the uncertainty of Pout values, conditioning on other explanatory variables (which may be either deterministic, or also uncertain). In terms of probabilistic wind energy forecasting, Bayesian CNN-BiLSTM model is used to analyse and efficiently handle high dimensionality of both input meteorological variables (WS, AD and WD) and additional uncertainties due to WF operating regimes. The presented results demonstrate that the developed Vine-GMCM and operational WF model can accurately integrate and effectively correlate all propagated uncertainties, ultimately resulting in much higher confidence levels of the forecasted WF power outputs than in the existing literature

Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere

Solar Ring (SOR) is a proposed space science mission to monitor and study the Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit. The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second, S2, 90{\deg} downstream, and the third, S3, completes the configuration. This design with necessary science instruments, e.g., the Doppler-velocity and vector magnetic field imager, wide-angle coronagraph, and in-situ instruments, will allow us to establish many unprecedented capabilities: (1) provide simultaneous Doppler-velocity observations of the whole solar surface to understand the deep interior, (2) provide vector magnetograms of the whole photosphere - the inner boundary of the solar atmosphere and heliosphere, (3) provide the information of the whole lifetime evolution of solar featured structures, and (4) provide the whole view of solar transients and space weather in the inner heliosphere. With these capabilities, Solar Ring mission aims to address outstanding questions about the origin of solar cycle, the origin of solar eruptions and the origin of extreme space weather events. The successful accomplishment of the mission will construct a panorama of the Sun and inner-heliosphere, and therefore advance our understanding of the star and the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space Researc

A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Due to the significant increase of the number of wind-based electricity generation systems, it is important to have accurate information on their operational characteristics, which are typically obtained by processing large amounts of measurements from the individual wind turbines (WTs) and from the whole wind farms (WFs). For further processing of these measurements, it is important to identify and remove bad quality or abnormal data, as otherwise obtained WT and WF models may be biased, or even inaccurate. There are wide ranges of both causes and manifestations of these bad/abnormal data, which are often denoted with the common general term “outlier”. This paper reviews approaches for the detection and treatment of outliers in processing WT and WF measurements, starting from the discussion of the commonly measured parameters, variables and resolutions, as well as the corresponding requirements and recommendations in related standards. Afterwards, characteristics and causes of outliers reported in existing literature are discussed and illustrated, as well as the requirements for the data rejection in related standard. Next, outlier identification methods are reviewed, followed by a review of approaches for testing the success of outlier removal procedures, with a discussion of their potential negative effects and impact on the WT and WF models. Finally, the paper indicates some issues and concerns that could be of interests for the further research on the detection and treatment of outliers in processing WT and WF measurements

P2-type Na2/3Ni1/3Mn2/3O2as a cathode materialwith high-rate and long-life for sodium ionstorage

Layered P2-type Na 2/3 Ni 1/3 Mn 2/3 O 2 was successfully synthesized through a facile sol-gel method and subsequent heat treatment. Resulting from different phase transformation and sodium ion diffusion rates, its electrochemical performance is highly related to the cut-off voltage and the electrolyte used. When the cut-off voltage is set up to 4.5 V or lowered to 1.5 V, capacity fade happens due to the occurrence of P2-O2 transformation and electrolyte decomposition or the redox reaction of the Mn 4+ /Mn 3+ ionic pair and P2-P2′ transformation. The electrode maintained 89.0 mA h g -1 with good cycling stability and excellent structural preservation between 4.0 and 2.0 V. The capacity retention is 71.2% even after 1200 cycles at 10C. It can be expected that P2-type Na 2/3 Ni 1/3 Mn 2/3 O 2 is very promising as a cathode material for sodium ion batteries

J. Chromatogr. A

Hydrophilic organic/salt-containing aqueous two-phase system composing of ethanol, water and ammonium sulfate for separation polar compounds was investigated on multilayer coil associated with J-type HSCCC devices. Compared to the classical polar solvent system based on 1-butanol-water or PEG1000-ammonium sulfate-water, the water content of upper phase in ethanol-ammonium sulfate-water systems was from 53.7% to 32.8% (wt%), closed to PEG1000-ammonium sulfate-water aqueous two-phase systems and higher than 1-butanol-water (22.0%, wt%). Therefore, the polarity of ethanol-ammonium sulfate-water is in the middle of 1-butanol-water and PEG-ammonium sulfate-water system, which is quite good for separating polar compounds like phenols, nucleosides and amino acids with low partition coefficient in 1-octanol-water system. The retention of stationary phase in four elution mode on type-J counter-current chromatography devices with multilayer coil column changed from 26% to 71%. Hydrodynamic trend possess both intermediate and hydrophilic solvent system property, which closely related to the composition of solvent system. The applicability of this system was demonstrated by successful separation of adenosine, uridine guanosine and cytidine. (C) 2014 Elsevier B.V. All rights reserved.Hydrophilic organic/salt-containing aqueous two-phase system composing of ethanol, water and ammonium sulfate for separation polar compounds was investigated on multilayer coil associated with J-type HSCCC devices. Compared to the classical polar solvent system based on 1-butanol-water or PEG1000-ammonium sulfate-water, the water content of upper phase in ethanol-ammonium sulfate-water systems was from 53.7% to 32.8% (wt%), closed to PEG1000-ammonium sulfate-water aqueous two-phase systems and higher than 1-butanol-water (22.0%, wt%). Therefore, the polarity of ethanol-ammonium sulfate-water is in the middle of 1-butanol-water and PEG-ammonium sulfate-water system, which is quite good for separating polar compounds like phenols, nucleosides and amino acids with low partition coefficient in 1-octanol-water system. The retention of stationary phase in four elution mode on type-J counter-current chromatography devices with multilayer coil column changed from 26% to 71%. Hydrodynamic trend possess both intermediate and hydrophilic solvent system property, which closely related to the composition of solvent system. The applicability of this system was demonstrated by successful separation of adenosine, uridine guanosine and cytidine. (C) 2014 Elsevier B.V. All rights reserved

The Cathode Choice for Commercialization of Sodium-Ion Batteries: Layered Transition Metal Oxides versus Prussian Blue Analogs

2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim With the unprecedentedly increasing demand for renewable and clean energy sources, the sodium-ion battery (SIB) is emerging as an alternative or complementary energy storage candidate to the present commercial lithium-ion battery due to the abundance and low cost of sodium resources. Layered transition metal oxides and Prussian blue analogs are reviewed in terms of their commercial potential as cathode materials for SIBs. The recent progress in research on their half cells and full cells for the ultimate application in SIBs are summarized. In addition, their electrochemical performance, suitability for scaling up, cost, and environmental concerns are compared in detail with a brief outlook on future prospects. It is anticipated that this review will inspire further development of layered transition metal oxides and Prussian blue analogs for SIBs, especially for their emerging commercialization

Oracle-free repair synthesis for floating-point programs

The floating-point representation provides widely-used data types (such as “float” and “double”) for modern numerical software. Numerical errors are inherent due to floating-point’s approximate nature, and pose an important, well-known challenge. It is nontrivial to fix/repair numerical code to reduce numerical errors — it requires either numerical expertise (for manual fixing) or high-precision oracles (for automatic repair); both are difficult requirements. To tackle this challenge, this paper introduces a principled dynamic approach that is fully automated and oracle-free for effectively repairing floating-point errors. The key of our approach is the novel notion of micro-structure that characterizes structural patterns of floating-point errors. We leverage micro-structures’ statistical information on floating-point errors to effectively guide repair synthesis and validation. Compared with existing state-of-the-art repair approaches, our work is fully automatic and has the distinctive benefit of not relying on the difficult to obtain high-precision oracles. Evaluation results on 36 commonly-used numerical programs show that our approach is highly efficient and effective: (1) it is able to synthesize repairs instantaneously, and (2) versus the original programs, the repaired programs have orders of magnitude smaller floating-point errors, while having faster runtime performance.ISSN:2475-142

P2-type Na2/3Ni1/3Mn2/3O2 as a cathode material with high-rate and long-life for sodium ion storage

Layered P2-type Na2/3Ni1/3Mn2/3O2 was successfully synthesized through a facile sol-gel method and subsequent heat treatment. Resulting from different phase transformation and sodium ion diffusion rates, its electrochemical performance is highly related to the cut-off voltage and the electrolyte used. When the cut-off voltage is set up to 4.5 V or lowered to 1.5 V, capacity fade happens due to the occurrence of P2-O2 transformation and electrolyte decomposition or the redox reaction of the Mn4+/Mn3+ ionic pair and P2-P2 transformation. The electrode maintained 89.0 mA h g(-1) with good cycling stability and excellent structural preservation between 4.0 and 2.0 V. The capacity retention is 71.2% even after 1200 cycles at 10C. It can be expected that P2-type Na2/3Ni1/3Mn2/3O2 is very promising as a cathode material for sodium ion batteries