Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

Abrasive water jet drilling of advanced sustainable bio-fibre-reinforced polymer/hybrid composites : a comprehensive analysis of machining-induced damage responses

This paper aims at investigating the effects of variable traverse speeds on machining-induced damage of fibre-reinforced composites, using the abrasive water jet (AWJ) drilling. Three different types of epoxy-based composites laminates fabricated by vacuum bagging technique containing unidirectional (UD) flax, hybrid carbon-flax and carbon fibre-reinforced composite were used. The drilling parameters used were traverse speeds of 20, 40, 60 and 80 mm/min, constant water jet pressure of 300 MPa and a hole diameter of 10 mm. The results obtained depict that the traverse speed had a significant effect with respect to both surface roughness and delamination drilling-induced damage responses. Evidently, an increase in water jet traverse speed caused an increase in both damage responses of the three samples. Significantly, the CFRP composite sample recorded the lowest surface roughness damage response, followed by C-FFRP, while FFRP exhibited the highest. However, samples of FFRP and hybrid C-FFRP recorded lowest and highest delamination damage responses, respectively. The discrepancy in both damage responses, as further validated with micrographs of colour video microscopy (CVM), scanning electron microscopy (SEM) and X-ray micro-computed tomography (X-ray μCT), is attributed to the different mechanical properties of the reinforced fibres, fibre orientation/ply stacking and hybridisation of the samples.Peer reviewe

Rewriting DNA Methylation Signatures at Will:The Curable Genome Within Reach?

DNA methyltransferases are important enzymes in a broad range of organisms. Dysfunction of DNA methyltransferases in humans leads to many severe diseases, including cancer. This book focuses on the biochemical properties of these enzymes, describing their structures and mechanisms in bacteria, humans and other species, including plants, and also explains the biological processes of reading of DNA methylation and DNA demethylation. It covers many emerging aspects of the biological roles of DNA methylation functioning as an essential epigenetic mark and describes the role of DNA methylation in diseases. Moreover, the book explains modern technologies, like targeted rewriting of DNA methylation by designed DNA methyltransferases, as well as technological applications of DNA methyltransferases in DNA labelling. Finally, the book summarizes recent methods for the analysis of DNA methylation in human DNA. Overall, this book represents a comprehensive state-of-the-art- work and is a must-have for advanced researchers in the field of DNA methylation and epigenetics

Peer-to-Peer Bundled Energy Trading with Game Theoretic Approach

An increasing share of on-site distributed generation systems enabled peer-to-peer (P2P) energy trading in distribution systems, where several entities cooperate to obtain electricity at minimum price and make the generation sector Eco-friendly. In this research avenue, significantly less attention was given to the ancillary services, such as reactive power, trading by prosumers. In this paper, we propose a P2P framework in which prosumers can trade reactive power in addition to the active power. The interactions and decision-making processes are modeled as games, and insights on auction mechanisms and bidding (pricing) strategies are present. The game-theoretic approach with trading the bundled energy trading model provides prosumers with more benefits than centralised entity or active power P2P trading model

Prediction of power demand in residential areas using the load profile clustering technique

The present-day advances in technologies provide the opportunities to pave a road from conventional power systems towards smart grids. As a result, smart grid features enable us to analyze the electricity usage data and identify electricity consumption patterns. This paper provides an analysis of half-hourly electricity consumption in domestic regions of the UK using clustering methods. To decrease the data dimensions and make it convenient to work with, unsupervised clustering methods such as k-means and Self-Organizing Maps are used for load profiling. The households are divided into several types and clusters, depending on the number of bedrooms and their daily electricity consumption patterns. Clustering is performed every day for different seasons providing intra-daily and seasonal variations. Probabilistic Neural Network is implemented to train the labeled dataset based on the clusters which identify load profile classes. The paper provides an investigation of the interconnection between house types and profile classes

PAN precursor fabrication, applications and thermal stabilization process in carbon fiber production: Experimental and mathematical modelling

Polyacrylonitrile (PAN) is a versatile man-made polymer and has been used in a large array of products since its first mass production in the mid 40s. Among all applications of PAN the widely used application is in manufacture of precursor fiber for fabrication of carbon fibers. The process of PAN-based carbon fiber production comprises fiber spinning, thermal stabilization and carbonization stages. Carbon fiber properties are significantly dependent on the quality of PAN precursor fiber and in particular the process parameters involved in thermal stabilization. This paper is the first comprehensive review that provides a general understanding of the links between PAN fiber structure, properties, and its stabilization process along with the use of mathematical modelling as a powerful tool in prediction and optimization of the processes involved. Since the promise of the mathematical modelling is to predict the future behaviour of the system and the value of the variables for the unseen or unmeasured domain of variables; and in the era of industry 4.0 rise, this review will be valuable in further understanding of the intricate processes of carbon fiber manufacture and utilising the advanced mathematical modelling using machine learning techniques to predict and optimize a range of critical factors that control the quality of PAN and resultant carbon fibers