This paper proposes an enhanced cyber secure energy and data transaction framework for the optimal operation and management of the smart city. Recently, the concept of smart city within the power systems has taken the center stage. Although the power system full monitoring and accessibility is guaranteed through the smart city concept, the risk of cyber-attacks to the system has been promoted, severely. With this in mind, in this paper an effective smart city model including the smart grid, smart transportation systems (STSs) that consist of the metro and electric vehicles (EVs), microgrid and smart energy hub (EH) is presented. In the proposed model, an improved directed acyclic graph (DAG) approach is represented in order to enhance the security of data transaction within the smart city. In this approach, a security layer is added to the blockchain which will prevent the cyber hackers access to the system information. An effective energy management schedule is also developed in this paper. To do so, an intelligent priority selection (IPS) based on advanced math operators is provided to allocate the metro-owned charging stations (MCSs), optimally. Furthermore, the unscented transform (UT) is utilized to handle the uncertainty of the system parameters. The results showed that the proposed IPS could improve the method CPU time over 75% compared to other well-known meta-heuristic methods in the area. Moreover, the results showed that the proposed framework has remarkably reduced the run time and increased the accuracy of the solution compared to the other meta-heuristic algorithms.

Due to its advantages, the Proportional Integral Derivative (PID) controller has been the most widely used controller in the industrial sector. It allows linear systems to have good performance, but if the system is subjected to physical variation conditions, the system’s behavior becomes non-linear, in which case the PID controller is insufficient. The use of the PID controller for speed control in rotating machines, such as the doubly fed induction motor (DFIM) is widely used, but the non-linearity of the machine parameters allows for undesirable behaviors, resulting in overshoots and torque ripples. For this reason, several techniques have been adopted to increase the DTC’s robustness. One finds the integration of artificial intelligence as optimization algorithms. These algorithms are used to generate gains close to the optimum, converging the behavior of the DFIM to its optimum. In this work, an Ant Colony Optimization (ACO) algorithm was proposed to adjust the PID controller gains of the DTC control to control the DFIM, using a combined weighting cost function, to obtain efficient torque and speed control. This paper presents a new hybrid structure resulting from the intelligent ACO-DTC control implemented on Matlab-Simulink. The performance results extracted from the simulation showed the effectiveness of the intelligent ACO-DTC control, which provides satisfactory performance in terms of rapidity, stability, precision, and torque ripples compared to the conventional DTC.

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

The paper introduces a comprehensive dynamic analysis for a renewable energy based water pumping system. The complete system components are described in details. The components include a wind turbine power system, a permanent magnet synchronous generator (PMSG), a water pumping system and a battery. The storage system is used to enhance the power delivery under weak wind production which consequently enhances the system reliability. Considering the PMSG as the fundamental power unit in the system, a new predictive control procedure is presented to enhance the PMSG performance. To validate the effectiveness of the proposed control scheme, a detailed comparison is accomplished between the designed controller and other three traditional controllers to evaluate the most effective in between. A power management scheme is constructed to manage the power flow and ensuring a sufficient power delivery to the pumping system. The obtained results are presented and analyzed in details to compare between the dynamics of the four predictive controllers used to manage the generator operation. The results report that the formulated control scheme has the best performance in terms of the reduced fluctuations, low calculation capacity, structure simplicity and low currents THD. The obtained results also approve the validness of the designed power management strategy in balancing the power flow and stabilizing the DC bus voltage as well.