Data-Driven Demand-Side Flexibility Quantification: Prediction and Approximation of Flexibility Envelopes

Real-time quantification of residential building energy flexibility is needed to enable a cost-efficient operation of active distribution grids. A promising means is to use the so-called flexibility envelope concept to represent the time-dependent and inter-temporally coupled flexibility potential. However, existing optimization-based quantification entails high computational burdens limiting flexibility utilization in real-time applications, and a more computationally efficient quantification approach is desired. Additionally, the communication of a flexibility envelope to system operators in its original form is data-intensive. In order to address the computational burdens, this paper first trains several machine learning models based on historical quantification results for online use. Subsequently, probability distribution functions are proposed to approximate the flexibility envelopes with significantly fewer parameters, which can be communicated to system operators instead of the original flexibility envelope. The results show that the most promising prediction and approximation approaches allow for a minimum reduction of the computational burden by a factor of 9 and of the communication load by a factor of 6.6, respectively

Data Analytics and Machine Learning for the Operation and Planning of Distribution Grids

The recent aspirations for a more sustainable energy system and a reduction of energy-related CO2 emissions have triggered a change of paradigm in power distribution grids, often encouraged by national and supranational policies. Traditionally considered as a passive black-box component of power systems, the distribution grid currently undergoes a rapid transformation and sees the emergence of new types of loads (e.g., electric vehicles, electric heating systems, electric water heaters) as well as distributed energy resources (e.g., small wind turbines, solar photovoltaic systems, battery energy storage systems). Their integration requires increased reliability, efficiency, and adaptability of distribution systems, which inevitably relies on more visibility. Consequently, advanced electricity sensor elements are massively rolled out in distribution grids down to the end-users. The gains in transparency and controllability offered by the advanced metering infrastructure open up an extensive range of new opportunities discussed extensively in the literature. Nevertheless, the research community is usually not granted access to real-world data due to understandable privacy concerns. They must depend on simplifications and synthetic data that often do not reflect the more complex reality and might lead to biased conclusions. On the sole basis of real-world data, this thesis intends to highlight which are the assumptions that can realistically be taken in the development and validation of data-based studies and applications. It also suggests various processes and methods to effectively leverage the actual potential of the advanced metering infrastructure and address some of the current challenges in grid operation and planning. This work primarily focuses on the low-voltage level, which is still rarely considered in the state-of-the-art literature. Data preparation, big data visualization, pseudo-measurement synthesis, distribution system state estimation, load disaggregation, and short-term forecasting are among the investigated topics. In that respect, the thesis hopes to bridge some of the gaps between the relatively conservative practices in the power industry and the various advanced data-based applications proposed in the literature

Impact of data availability and pseudo‐measurement synthesis on distribution system state estimation

The influence of the advanced metering infrastructure (AMI) set‐up and of the related modelling of pseudo‐measurements on the accuracy of state estimation (SE) in distribution grids, especially at the low‐voltage level, is studied herein. A comprehensive sensitivity analysis is carried out that accounts for the type, the penetration level, and the placement of the metering devices that compose state‐of‐the‐art AMIs. Special care is given to the synthesis of active and reactive power pseudo‐measurements, which substantially impacts the estimation of peak values. Although crucial for system operators, peak values are very often neglected in the SE literature. For that purpose, this study promotes realistic approaches to generate synthetic load profiles and relies on evaluation metrics that are not purely based on the point‐wise precision but consider the statistical properties of the SE outcomes. This is tested on an actual 971‐bus distribution grid with the corresponding smart meter data and is evaluated with respect to the accuracy of active and reactive power injections, bus voltages, and line loadings

Studying the Impact of Smart Meter Placement on Low-Voltage Grid State Estimation

This paper comprehensively investigates the influence of smart meter allocation methods, considering sensors at grid buses and branches, on the performance of distribution system state estimation algorithms. Three algorithms are used, namely the Weighted Least Squares, the Extended Kalman Filter, and the Schweppe-type GM-estimator with the Huber psi-function. These are tested on a real low-voltage distribution grid with radial structure for multiple scenarios characterized by different penetration levels and types of measurements. Based on Monte Carlo simulations, different locations of sensors at buses and branches are considered for each scenario. An empirical study is carried out to assess the correlation of the placement of meters with the state estimation error. The results suggest that bus meters are most profitable at customers with the highest energy consumption. In addition, well distributed sensors at the grid branches based on a newly proposed `path search' method appear to be the most effective

Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty

While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method

Ionic liquid-coated immobilized lipase for the synthesis of methylglucose fatty acid esters

A homologous series of biosurfactants has been synthesized by a novel sustainable biotransformation technique and compared with three other enzymatic processes. 6-O-Alkanoyl-methyl-α-D-glucopyranosides were obtained by lipase mediated esterification of methyl-α-D-glucopyranoside with capric acid C10:0, lauric acid C12:0, myristic acid C14:0, palmitic acid C16:0, and oleic acid C18:1. Solvent free transformations were compared with the use of ionic liquids and organic solvents. The lipase from Candida antarctica B, immobilized on macroporous acrylic acid beads (Novozyme 435), was employed either untreated or coated with small amounts of ionic liquids. This resulted in superior efficiencies (80%) with 1-butyl-4-methylpyridine hexafluorophosphate [4bmpy][PF6] and broader substrate tolerance in comparison to solvent free transformation. The results show a positive correlation with increasing polarity of the ionic liquids used as liquid film-coating, which was in opposition to the use of the same ionic liquid as solvent. The analysis of the ionic liquid film coated catalyst carriers was performed by optical and scanning electron microscopy (SEM)