Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

Situational awareness in low-observable distribution grid - exploiting sparsity and multi-timescale data

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe power distribution grid is typically unobservable due to a lack of real-time measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Limited real-time measurements hinders the distribution system state estimation (DSSE). DSSE involves estimation of the system states (i.e., voltage magnitude and voltage angle) based on available measurements and system model information. To cope with the unobservability issue, sparsity-based DSSE approaches allow us to recover system state information from a small number of measurements, provided the states of the distribution system exhibit sparsity. However, these approaches perform poorly in the presence of outliers in measurements and errors in system model information. In this dissertation, we first develop robust formulations of sparsity-based DSSE to deal with uncertainties in the system model and measurement data in a low-observable distribution grid. We also combine the advantages of two sparsity-based DSSE approaches to estimate grid states with high fidelity in low observability regions. In practical distribution systems, information from field sensors and meters are unevenly sampled at different time scales and could be lost during the transmission process. It is critical to effectively aggregate these information sources for DSSE as well as other tasks related to situational awareness. To address this challenge, the second part of this dissertation proposes a Bayesian framework for multi-timescale data aggregation and matrix completion-based state estimation. Specifically, the multi-scale time-series data aggregated from heterogeneous sources are reconciled using a multitask Gaussian process. The resulting consistent time-series alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints for accurate state estimation low-observable distribution system. We also develop a computationally efficient recursive Gaussian process approach that is capable of handling batch-wise or real-time measurements while leveraging the network connectivity information of the grid. To further enhance the scalability and accuracy, we develop neural network-based approaches (latent neural ordinary differential equation approach and stochastic neural differential equation with recurrent neural network approach) to aggregate irregular time-series data in the distribution grid. The stochastic neural differential equation and recurrent neural network also allows us to quantify the uncertainty in a holistic manner. Simulation results on the different IEEE unbalanced test systems illustrate the high fidelity of the Bayesian and neural network-based methods in aggregating multi-timescale measurements. Lastly, we develop phase, and outage awareness approaches for power distribution grid. In this regard, we first design a graph signal processing approach that identifies the phase labels in the presence of limited measurements and incorrect phase labeling. The second approach proposes a novel outage detector for identifying all outages in a reconfigurable distribution network. Simulation results on standard IEEE test systems reveal the potential of these methods to improve situational awareness

Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961https://orcid.org/0000-0003-1166-198

Big data analytics in smart grids: a review

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COMPRESSIVE SENSING-BASED METHODOLOGIES FOR SMART GRID MONITORING

Modern distribution networks, commonly known as Smart Grids, will be characterized by strictly requirements in terms of reliability and efficiency of the power supply. This will require a high empowerment in the management of the distribution, and transmission, networks by the system operators. Problems such as the identification of the prevailing harmonic sources and the fault location are characterized by criticality which must be appropriately taken into account, in order to fully exploit the capabilities of the Smart Grids. The analysis of both phenomena requires an appropriate monitoring of the networks, which are currently characterized by the availability of a limited number of measurements. This increase the complexity of the analysis of distribution networks, and the necessity of developing ad-hoc algorithms and solutions aimed at supporting the system operators while managing the networks. In this thesis, Compressive Sensing-based algorithms for detecting the main harmonic polluting sources, and for identifying the location of faults occurring in distribution systems have been presented. With reference to the identification of the main harmonic sources, two algorithms have been proposed: one for detailed analysis, with reference to a specific harmonic order, and one for more general analysis, which allows to investigate multiple harmonic orders simultaneously. The performed tests have proved how both methodologies are robust with respect to the measurement uncertainties, underlying the different capabilities of the two methods. Contrarily, the performance of the fault location algorithms are more influenced by the higher uncertainties in measuring the dynamic signals involved during the fault. The analysis performed considering the proper uncertainty scenarios have underlined how the use of modern devices for branch current measurements allow to increase the performance of the fault location algorithms; providing additional information which are useful for locating the fault

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen