Page 346 - From Smart Grid to Internet of Energy

310 From smart grid to internet of energy

reliability of the data sets. All of these Vs are assumed as crucial parameters to
achieve the last V which is value providing decision making and success of big
data evaluation [1, 2].
It is noted in [3] that International Data Corporation has reported that 1.8ZB
data was generated and reproduced in 2011 that is expected to be increased
around 50 times up to 2020. The generated data size is also increased by the
improvement of smart grid technologies since they are based on widespread
data acquisition applications inherited over wireless sensor networks (WSNs).
The entire power network is equipped with several types of sensor for instant
detection of generation, transmission, distribution, and consumption data. The
vital services and components such as demand side management (DSM),
distributed energy resources (DERs), renewable energy sources (RESs) and
others of power networks require some detailed and rapid monitoring infrastruc-
tures. The digitalization of power networks causes several challenges as
economic operation, control issues, stability and reliability of system.
The big data applications of networks bring new opportunities in terms of
smart energy management and incorporates several operational capabilities.
The smart grid is defined as system of systems integrating two-way flow of
communication and power. Therefore, large amount of measurement, monitor-
ing and control signals are required to be transmitted over smart grid infrastruc-
ture. The generated and transmitted data sets are utilized to perform monitoring,
optimization, forecasting, planning and management issues. Thereby, big data
analysis methods play vital role on management and processing of large amount
of data generated by smart grid infrastructure. It provides rapid detection
against failures, dynamic system restoration, rapid response to load and source
fluctuations, and increasing the reliability and flexibility of entire grid [3].
It is obvious that smart grid is advancing on green and environmentally
friendly generation of energy. It can be roughly said that the most important
components differing smart grid from conventional one is measurement and
monitoring devices. It is reported in [4] that the phasor measurement units
(PMUs) which are crucial on detecting magnitude and phase of a power network
generate around 900 TB data per year. On the other hand, smart meters, WSNs
that are used for estimation and predictions, intelligent electronic devices
(IEDs), smart transformers, smart reclosers and circuit breakers generate large
amounts of data. Therefore, big data and data analytic methods can leverage
advancements of smart grid by providing various opportunities on load plan-
ning, demand management, forecasting, and data analytics. The applications
and data analytics of smart grid that are based on big data processing operations
are illustrated in Fig. 8.1. The big data analytics are listed into two main
categories as grid operation and customer operations. The storage cycle of
big data servers is provided by several data suppliers from generation, transmis-
sion, distribution and consumption systems. Moreover, the DER systems are