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Big data optimization in electric power systems: a review
There are different definitions of big data, and among them, the most common definition refers
to three or five characteristics, called volume, velocity, variety, value, and veracity from (Laney
(2001)). Volume could include Tera Byte, Peta Byte, Exa Byte, and Zetta Byte. Velocity
describes how fast the data are retrieved and processed ââBatch or streamingâ. Variety describes
structured, semi-structured, and unstructured data (Laney, 2001, Zikopoulos and Eaton, 2011).
Veracity explains the integrity and disorderliness of data, while value refers to how good is the
âvalueâ we derive from analyzing data? (Zicari et al., 2016).
Electrical power systems are networks of components arrayed to supply, transfer, and use
electric power. In power system since models are used to predict and characterize operations.
However, there is a necessity for powerful optimization algorithms for information processing to
learn models as the size increase of data is becoming a global problem to solve large-scale
optimization problems. Any optimization problem includes a real function to be maximized or
minimized by systematically determination of input values from an allowed set of values.
Richness and quantity of large data sets provide the potential to enhance statistical learning
performance but require smart models that use the latent low-dimensional structure for effective
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data separation.
This chapter reviews the most recent scientific articles related to large and big data optimization
in power systems. Optimization issues such as logistics in power systems and techniques
including nonsmooth, nonconvex, and unconstrained large-scale optimization are presented.
After a brief review of big data, scientometric analysis has been applied using keywords of âbig
dataâ and âpower system.â Besides, keywords analysis, network visualization, journal map, and
bibliographic coupling analysis have been done to draw a path on big data works in power
system problems. Also, the most common useful techniques in large-scale optimization in power
system have been reviewed. At the end of this chapter, metaheuristic techniques in big data
optimization are reviewed to show that many efforts have been involved in big data optimization
in power system and systematically highlight some perspectives on big data optimization