Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> It now appears that the general nature of the deviations from uniformity in the spectrum of a complicated nucleus is essentially the same in all regions of the spectrum and over the entire Periodic Table. This behavior, moreover, is describable in terms of standard Hamiltonian ensembles which could be generated on the basis of simple information-theory concepts, and which give also a good account of fluctuation phenomena of other kinds and, apparently, in other many-body systems besides nuclei. The main departures from simple behavior are ascribable to the moderation of the level repulsion by effects due to symmetries and collectivities, for the description of which more complicated ensembles are called for. One purpose of this review is to give a self-contained account of the theory, using methods: sometimes approximate: which are consonant with the usual theory of stochastic processes. Another purpose is to give a proper foundation for the use of ensemble theory, to make clear the origin of the simplicities in the observable fluctuations, and to derive other general fluctuation results. In comparing theory and experiment, the authors give an analysis of much of the nuclear-energy-level data, as well as an extended discussion of observable effects in nuclear transitionsmore » and reactions and in the low-temperature thermodynamics of aggregates of small metallic particles.« less <s> BIB001 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> In this paper we assume that time synchronized measurements will be ubiquitously available at all high-voltage substations at very high rates. We examine how this information can be utilized more effectively for real-time operation as well as for subsequent decision making. This new information available in real time is different, both in quality and in quantity, than the real-time measurements available today. The promise of new and improved applications to operate the power system more reliably and efficiently has been recognized but is still in conceptual stages. Also, the present system to handle this real-time data has been recognized to be inadequate but even conceptual designs of such infrastructure needed to store and communicate the data are in their infancy. In this paper, we first suggest the requirements for an information infrastructure to handle ubiquitous phasor measurements recognizing that the quantity and rate of data would make it impossible to store all the data centrally as done today. Then we discuss the new and improved applications, classified into two categories: one is the set of automatic wide-area controls and the other is the set of control center (EMS) functions with special attention to the state estimator. Finally, given that the availability of phasor measurements will grow over time, the path for smooth transition from present-day systems and applications to those discussed here is delineated. <s> BIB002 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> The global upsurge in research of smart grid and demand response (DR) is opening up new opportunities and challenges to China's power industry in recent years. The smart grid emphasize the interaction with user particularly, including the information interaction and power interaction, which are achieved through the deployment of various types of DR programs. Meanwhile, the development of smart grid also depends on demand response mechanism in the open market environment. DR coincides with the goal of smart grid which is to achieve a high-security, economical, reliable and efficient power system. So the collaboration between smart grid and demand response is talked in this paper. First,we analyze the relationship between smart grid and demand response systematically.Second,the development conditions of DR project in China and abroad are expounded. Furthermore, the necessity and significance of pushing DR project into smart grid is discussed and summarized, finally, methods and suggestions for achieving the collaboration are presented. (4 pages) <s> BIB003 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> This paper focuses on the use of extremely large data sets in power system operation, control, and protection, which are difficult to process with traditional database tools and often termed big data. We will discuss three aspects of using such data sets: feature extraction, systematic integration for power system applications, and examples of typical applications in the utility industry. The following analytics tasks based on big data methodology are elaborated upon: corrective, predictive, distributed and adaptive. The paper also outlines several research topics related to asset management, operation planning, realtime monitoring and fault detection/protection that present new opportunities but require further investigation. <s> BIB004 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> This short paper reports some initial experimental demonstrations of the theoretical framework: the massive amount of data in the large-scale cognitive radio network can be naturally modeled as (large) random matrices. In particular, using experimental data we will demonstrate that the empirical spectral distribution of the large sample covariance matrix---a Hermitian random matrix---agree with its theoretical distribution (Marchenko-Pastur law). On the other hand, the eigenvalues of the large data matrix ---a non-Hermitian random matrix---are experimentally found to follow the single ring law, a theoretical result that has been discovered relatively recently. To our best knowledge, our paper is the first such attempt, in the context of large-scale wireless network, to compare theoretical predictions with experimental findings. <s> BIB005 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> The IEEE PSRC System Protection Subcommittee Working Group C14 has produced a report that describes practical applications of synchrophasors in protection applications. The report begins with the history of synchrophasors and then goes into issues to consider in their application. Some existing applications are described and then future applications that have been considered or are in development are described. The appendix contains applications that use synchrophasor data but are not considered protection applications. This is a summary of the complete report found on the PSRC website (http://www.pes-psrc.org click on Published Reports). <s> BIB006 </s> Energy big data: A survey <s> C. BIG DATA IN SMART GRID <s> The $\nu $ -support vector classification has the advantage of using a regularization parameter $\nu $ to control the number of support vectors and margin errors. Recently, a regularization path algorithm for $\nu $ -support vector classification ( $\nu $ -SvcPath) suffers exceptions and singularities in some special cases. In this brief, we first present a new equivalent dual formulation for $\nu $ -SVC and, then, propose a robust $\nu $ -SvcPath, based on lower upper decomposition with partial pivoting. Theoretical analysis and experimental results verify that our proposed robust regularization path algorithm can avoid the exceptions completely, handle the singularities in the key matrix, and fit the entire solution path in a finite number of steps. Experimental results also show that our proposed algorithm fits the entire solution path with fewer steps and less running time than original one does. <s> BIB007
Data size in electric power systems have increased dramatically, which results in gaps and challenges. The 4Vs data (data with characteristics of volume, variety, velocity, and veracity) in smart grid are difficult to be handled within a tolerable operating time and hardware resources BIB004 . Therefore, the big data technology has been introduced to the power system. The emerging big data technology does not conflict with classical analysis or pretreatment. In fact, it has already been applied successfully as a powerful data-driven tool in numerous research areas such as financial systems  , quantum systems BIB001 , biological systems  , and wireless communication networks BIB005 . Big data sets and their relationship with several sample applications are depicted in Fig. 1 . In the aspect of the field measurement showed in Fig. 1 , the deployment of PMUs has improved the measurements on instantaneous values of voltages and currents for operators with accurate timestamps BIB002 . PMUs can be imported to the state estimator to provide direct state measurement, since they provide measurements in both magnitude and angle aspects  . This is especially interrelated when the system topology such as operations on automated breaker change rapidly. PMU data can also be applied in protective relaying operations BIB006 . The use of PMU data is helpful for more accurate and more rapid tripping response in measurement. In the aspect of distribution, the management of big data is also crucial in order to facilitate applications in the fields of demand response, Distributed Energy Resources (DERs), and Electric Vehicles (EVs) BIB007 . Improved and secure bi-direction communication and management of the associated big data are required for turning loads into dispatchable resources, thus promoting DERs to participate in electricity markets BIB003 .