Page 354 - From Smart Grid to Internet of Energy

318 From smart grid to internet of energy

high volumes over than 50 TB in Big Data usage. On the other hand, database
processing systems should consider to meet requirements of high volume of
data, relations between data stacks, indexing methods such as column and table
structures, and data transmission capability of devices. It is noted that high vol-
ume data of Big Data analytics are handled by using products such as Apache
Cassandra, Hadoop, Mango DB and so on [15].
The integration of IoT technology with smart power network causes addi-
tional storage and processing cost due to massive volume of data inherited from
every measurement and monitoring nodes. The generated data provides infor-
mation about power demand of consumers, power line parameters, network sit-
uations, demand response programs, advanced metering data, DSM and outage
control, and similar other information. Therefore, power system operators are
faced to meet software and hardware requirements to handle storage, manage-
ment and data processing duties. One of the most appropriate solution of this
situation is merging Big Data analytics with IoT technologies in terms of power
grid aspects. SCADA is one of the most widely used communication and mea-
surement system in power networks due to its real time metering and control
capabilities. The SCADA architecture is based on data acquisition over smart
sensors and measurement nodes along transmission and distribution systems.
The inherited data types are transmitted to control center by using highly reli-
able communication lines that are also used to transmit control commands to
nodes of smart or controllable devices on the same basis. The increasing amount
of power networks and distributed generation sources require improvements on
installed and aging power grid architectures. Therefore, SCADA based legacy
measurement and monitoring systems are being converted to IoT and emerging
communication system by the help of Big Data analytics. The cloud computing
methods provide appropriate solutions to IoT based measurement and manage-
ment applications due to its storage, API, service, and network supports. It is
noted in [14] that cloud computing operators provide three types of services
named as Infrastructure as a Service (IaaS), Platform as a Service (PaaS),
and Software as a Service (SaaS). The IaaS defines the environment that is com-
prised by operating systems, storage and network infrastructures, and database
services through the cloud as its name implies. On the other hand, the PaaS
accommodates programming and software development interfaces for end
users, and provides several libraries in the cloud ecosystems, while SaaS pre-
sents APIs for end users. The storage support of IaaS services provides benefits
to system operators in addition to excessive processing capabilities with low
cost investments. On the other hand, security and reliability of measured data
are ensured by IaaS services supported by cloud operators. The vulnerabilities
of cloud computing which are caused by data share with other parties can be
prevented by fog computing approaches. The fog computing increases system
security by the devices located at the edges of network, and data transferring
nodes are decreased by this way [14].