FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

Heba-Allah I. ElAzab, R. A. Swief, Hanady H. Issa, Noha H. El-Amary, Alsnosy Balbaa and H. K. Temraz
Additional contact information
Heba-Allah I. ElAzab: Faculty of Engineering, Ahram Canadian University, Giza 12573, Egypt
R. A. Swief: Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Hanady H. Issa: Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt
Noha H. El-Amary: Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt
Alsnosy Balbaa: Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo P.O. Box 2033, Egypt
H. K. Temraz: Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt

Energies, 2018, vol. 11, issue 10, 1-17

Abstract: Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO 2 emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed.

Keywords: Combined Economic Emission Dispatch (CEED); Field Programming Gate Array (FPGA); Gravitational Search Algorithm (GSA); Plug-in Electric Vehicles (PEVs); quadratic programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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