Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

: Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resilienc

The internet of ontological things: On symmetries between ubiquitous problems and their computational solutions in the age of smart objects

This dissertation is about an abstract form of computer network that has recently earned a new physical incarnation called “the Internet of Things.” It surveys the ontological transformations that have occurred over recent decades to the computational components of this network, objects—initially designed as abstract algorithmic agents in a source code of computer programming but now transplanted into real-world objects. Embodying the ideal of modularity, objects have provided computer programmers with more intuitive means to construct a software application with lots of simple and reusable functional building blocks. Their capability of being reassembled into many different networks for a variety of applications has also embodied another ideal of computing machines, namely general-purposiveness. In the algorithmic cultures of the past century, these objects existed as mere abstractions to help humans to understand electromagnetic signals that had infiltrated every corner of automatized spaces from private to public. As an instrumental means to domesticate these elusive signals into programmable architectures according to the goals imposed by professional programmers and amateur end-users, objects promised a universal language for any computable human activities. This utopian vision for the object-oriented domestication of the digital has had enough traction for the growth of the software industry as it has provided an alibi to hide another process of colonization occurring on the flipside of their interfacing between humans and machines: making programmable the highest number of online and offline human activities possible. A more recent media age, which this dissertation calls the age of the Internet of Things, refers to the second phase of this colonization of human cultures by the algorithmic objects, no longer trapped in the hard-wired circuit boards of personal computer, but now residing in real-life objects with new wireless communicability. Chapters of this dissertation examine each different computer application—a navigation system in a smart car, smart home, open-world video games, and neuro-prosthetics—as each particular case of this object-oriented redefinition of human cultures

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems