Modelling and Optimization Based Control for Demand Response in Active Distribution Networks

We explore how Demand Response (DR) can effectively provide electricity system services such as for the management of bi-directional power flows and the control of voltage deviations in active distribution networks, without compromising consumer comfort or adversely affecting grid operation in the transmission network. By translating intricate power system physics into straightforward control objectives, we design DR control algorithms that can operate within realistic computational time frames at scale. We conduct simulation-based experiments and find that minimizing the Euclidean-Norm of the total residual load at transformer sub-stations is an effective objective for harnessing DR for the dispatch of renewable electricity grids. We show that this control objective can be efficiently pursued in sequential order, without optimal power flow calculations or information about the topology of a grid. Additionally, we find that pursuing this objective reduces the sum of peak power flows along all lines in active distribution networks, and can therefore enable both lower transmission losses and lower voltage deviations

Active machine learning for spatio-temporal predictions using feature embedding

Active learning (AL) could contribute to solving critical environmental problems through improved spatio-temporal predictions. Yet such predictions involve high-dimensional feature spaces with mixed data types and missing data, which existing methods have difficulties dealing with. Here, we propose a novel batch AL method that fills this gap. We encode and cluster features of candidate data points, and query the best data based on the distance of embedded features to their cluster centers. We introduce a new metric of informativeness that we call embedding entropy and a general class of neural networks that we call embedding networks for using it. Empirical tests on forecasting electricity demand show a simultaneous reduction in prediction error by up to 63-88% and data usage by up to 50-69% compared to passive learning (PL) benchmarks

Unified machine learning tasks and datasets for enhancing renewable energy

Multi-tasking machine learning (ML) models exhibit prediction abilities in domains with little to no training data available (few-shot and zero-shot learning). Over-parameterized ML models are further capable of zero-loss training and near-optimal generalization performance. An open research question is, how these novel paradigms contribute to solving tasks related to enhancing the renewable energy transition and mitigating climate change. A collection of unified ML tasks and datasets from this domain can largely facilitate the development and empirical testing of such models, but is currently missing. Here, we introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets from six different application domains related to enhancing renewable energy, including out-of-distribution validation and testing data. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model. We further analyse the dimensions of each dataset; investigate what they require for designing over-parameterized models; introduce a set of dataset scores that describe important properties of each task and dataset; and provide performance benchmarks

City-scale car traffic and parking density maps from Uber Movement travel time data

We generate car parking density maps from Uber travel time data for 33 cities worldwide. The resolution of the generated maps in time is one hour. The time periods for which we generate the results depend on the availability of the underlying Uber travel time measurements. At the time of writing this, Uber provides travel time data for the years 2015 - 2018 and distinguishes these by weekdays, weekends and the quarter of a year. In addition to the datasets that are distinguished by day type, Uber also provides travel time statistics that are collected regardless of the day type; these datasets include more measurements than the separated datasets and have therefore lower sparsity. The resolution of the generated maps in space varies and depends on how Uber divides cities into different zones. The parking maps can be used for an analysis of the entire suburban area of a town or the center of a town; the accuracy is also adequate for an analysis of parking densities within the city center of a town. The generated data is available in the folder "data". The generated car parking density maps are available for one arbitrary dataset of each city in folder "images". The images are further provided as Graphical Interchange Format (GIF) files in folder "gif"

Enhanced spatio-temporal electric load forecasts using less data with active deep learning

An effective way to mitigate climate change is to electrify most of our energy demand and supply the necessary electricity from renewable wind and solar power plants. Spatio-temporal predictions of electric load become increasingly important for planning this transition, while deep learning prediction models provide increasingly accurate predictions for it. The data that are used for training deep learning models, however, are usually collected at random using a passive learning approach. This naturally results in a large demand for data and associated costs for sensors such as smart meters, posing a large barrier for electric utilities when decarbonizing their grids. Here we investigate whether electric utilities can use active learning to collect a more informative subset of data by leveraging additional computation for better distributing smart meters. We predict ground-truth electric load profiles for single buildings using only remotely sensed data from aerial imagery of these buildings and meteorological conditions in the area of these buildings at different times. We find that active learning can enable 26–81% more accurate predictions using 29–46% less data at the price of 4–11 times more computation compared with passive learning.ISSN:2522-583

City-scale car traffic and parking density maps from Uber Movement travel time data

Car parking is of central importance to congestion on roads and the urban planning process of optimizing road networks, pricing parking lots and planning land use. The efficient placement, sizing and grid connection of charging stations for electric cars makes it even more important to know the spatio-temporal distribution of car parking densities on the scale of entire cities. Here, we generate car parking density maps using travel time measurements only. We formulate a Hidden Markov Model that contains non-linear functional relationships between the changing average travel times among the zones of a city and both the traffic activity and flow direction probabilities of cars. We then sample the traffic flow for 1,000 cars per city zone for each city from these probability distributions and normalize the resulting spatial parking distribution of cars in each time step. Our results cover the years 2015–2018 for 34 cities worldwide. We validate the model for Melbourne and reach about 90% accuracy for parking densities and over 93% for circadian rhythms of traffic activity.ISSN:2052-446