Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Ciudad inteligente y sostenible: Singapore, caso de éxito

The following paper focuses on the qualitative description of the nation city of Singapore and its quantitative evaluation, which are part of an exploratory research for the ORCA and SciBas research group of the Francisco José de Caldas District University. Therefore, during the first semester of 2018, a conceptual review was made on the subject, proposing an action methodology whose snuyources are selected with the category of smart city in the context of sustainability. The search for information, both national and international, was focused on the databases: IEEE Xplore, ScienceDirect, Springer Link, among others; and in the search for the evaluation model.El siguiente trabajo se centra en la descripción cualitativa de la ciudad nación de Singapur y su evaluación cuantitativa, que hacen parte de una investigación exploratoria para el grupo de investigación ORCA y SciBas de la Universidad Distrital Francisco José de Caldas. Por lo tanto, durante el primer semestre de 2018, se realizó una revisión conceptual sobre el tema, proponiendo una metodología de acción cuyas fuentes son seleccionadas con la categoría de ciudad inteligente en el contexto de la sostenibilidad. La búsqueda de información, tanto nacional como internacional, se centró en las bases de datos: IEEE Xplore, ScienceDirect, Springer Link, entre otras; y en la búsqueda del modelo de evaluación