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

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 CO2 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.

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

“…Particle Swarm Optimization used to carried out hourly scheduling in grid [20], optimal battery energy storage schedule [21], demand response for residential consumers [22] and economic load dispatch problem [23], Cuckoo Search Optimization Algorithm used to carried out optimal scheduling of time shift-able loads [24,25], Spider Monkey Optimization (SMO) used for allocation of DGs for management of demand side [26], Bat Algorithm used to optimize the cost in home energy management system [27], Firefly Optimization used to construct the efficient DSM system [28] and in this flow, Fruit Fly Optimization used load balancing for the applications of EHR [29] and Grasshopper Optimization Algorithm [30] used to design an efficient energy management in office. Gravitational Search Algorithm used optimal scheduling of building users electricity consumption in [31] and unit commitment problem solved for electric vehicles using GSA in [32]. Economic load dispatch (ELD) problem solved by using a hybrid BBBO [33] and optimality in power for residential consumers discussed using Lyapunov optimization approach in [34].…”

Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

“…Roy and Kumar proposed GSA to optimize UC problem [14]. ElAzab et al used GSA to reduce the incorporated cost for UC integrating plugin electric vehicles [15]. Raglend et al proposed PSO to solve profit based UC problem [16].…”

Low-Cost FPGA-Based Electronic Control Unit for Vehicle Control Systems

“…This work proposes to take advantage of the parallelism offered by the FPGA with the possibility of integrating a soft processor that executes a C or C++ code to execute low load and repetitive tasks [8] while freeing the processor for the execution of the algorithm without the operating system. The ECU uses the Zynq-7000 chip manufactured by Xilinx which also includes a dual-core Advanced RISC (reduced instruction set computer) Machine (ARM) processor [9]. This also allows the execution of two programs in parallel at a high-clock rate, which makes it perfect for the main control loop and data recording.…”