Abstract
Energy hubs are defined as energy systems that receive various energy carriers and convert or store them to serve different types of load demands. Stochastic scheduling methods can be used to optimally manage the energy hubs. However, in the stochastic approach, the main deficiency is that there exists the risk of experiencing the worst scenario, so a viable solution is needed to address this possibility. This paper addresses the two-stage operation scheduling of energy hubs based on the worst scenarios. A novel robust scenario-based approach is proposed and compared to the stochastic approach. A robustness parameter is defined to control the compromise between the expected operating costs and the model robustness. It can be seen that the model is robust against all the realization of worst scenarios.

Abstract
In this paper, an advanced control of a DC Micro Grid (MG)-connected Proton Exchange Membrane (PEM) Fuel Cell connected with a DC/DC boost converter is addressed to achieve an overall appropriate control scheme in the power management system. In this context, a nonlinear PEM Fuel Cell stack, which is the main source of the continuous power to the load, is modelled and controlled by an optimal Linear Parameter Varying (LPV) technique in the presence of uncertainties and variation in its operating parameters i.e. output current and temperature. To this end, a polytopic-LPV model is considered for nonlinear PEM Fuel Cell stack and sufficient conditions for designing a stabilizing continuous time LPV controller based on state feedback controlling law is derived in terms of Linear Matrix Inequality (LMI). On the other hand, a feedback linearization controller is developed simultaneously to control the duty cycle of the DC/DC boost converter, which is connected between the PEM Fuel Cell stack and the load, aiming to regulate the DC output voltage of the grid to an arbitrary and predefined reference value. The performance of the proposed approach and controllers are verified through simulation results.

Abstract
Fault current limiters (FCLs) are getting high-degree of attention since they can properly overcome the transient conditions of modern power systems. FCLs have the ability to limit the short-circuit currents before reaching to its maximum value so that they can be cut off by the available switches. In fact, FCLs show negligible resistance in the normal operation, but their resistance suddenly increases with a short-circuit and prevents it from rising. The technical and economic benefits of FCLs in power systems depend on their number, locations and optimal structural parameters. In this paper, a two-stage approach is proposed for determining the number, location and impedance of FCLs in the transmission network with high penetration of distributed generations (DGs). The suggested algorithm determines the number and locations of FCLs in the first step using a sensitivity based technique and the value of FCL impedance is chosen in the second step utilizing an optimization objective function. The improved grey wolf optimizer (IGWO) is developed to solve the optimization problem which its main objective is reducing the short-circuit level of the network. The proposed approach is assessed on the IEEE-30 bus transmission network considering the effects of different kind of DGs. The results shown that FCLs can significantly reduce the short-circuit level of the network along with other advantages.

Abstract
This paper investigates the issue of frequency regulation of a single-area alternating current (AC) power system connected to an electric vehicle (EV) aggregator through a nonideal communication network. It is assumed that the command control action is received by the EV aggregator with constant delay and the power system experiences uncertain parameters. A novel effective iterative algorithm, direct search, is proposed for the time-delayed system to design the gains of a proportional-integral (PI) controller. The proposed direct search algorithm can find a feasible solution whenever at least one solution lays in the space search. Thus, by choosing a wide space search, we can expect that the PI controller assures the closed-loop stability, theoretically. The proposed approach has low conservative results over the existing approaches. For the uncertain time-delayed system, a robust PI controller is designed, which is resilient against the system uncertainties and time delay. Numerical simulations are carried out to show the merits of the developed controller.

Abstract
Electrical energy consumption pattern has always been important for power distribution companies, because load variations and method of electricity consumption affect energy losses amount. For this, distribution companies frequently encourage the network users to correct their energy consumption behavior by suggesting some incentives. Reconfiguration of distribution systems for a specific load pattern is an effective way to reduce the losses. Hence, some papers have considered load variations in distribution system reconfiguration (DSR) to show importance of consumption pattern for reconfiguration decisions. However, most of specialized studies have been ignored load changes in their reconfiguration models because of a significant increase in computational burden and processing time. On the other hand, neglecting the consumption pattern causes the energy losses is calculated inaccurately. Therefore, this paper intends to evaluate effect of load pattern on reconfiguration plans in order to find out importance of considering load variations in energy losses minimization via DSR. The analysis has been conducted on well-known distribution systems by AMPL (a classic optimization tool).

Abstract
This paper explores the problem of model-based detecting and reconstructing occurring actuator and sensor faults in direct current (DC) microgrids (MGs) connected to resistive and constant power loads (CPLs) and energy storage units. Both the actuator and sensor faults are modeled as an additive time-varying term in the state-space representation, which highly degrade the system response performance if they are not compensated. In this paper, a novel advanced extended Kalman filter (EKF), called dualEKF (D-EKF) is proposed to estimate the system states as well as the accruing actuator and sensor faults. The main property of the developed approach is that it offers a systematic estimation procedure by dividing the estimating parameters into three parts and these parts are estimated in parallel. A first-order filter is utilized to turn the sensor faulty system into an auxiliary sensor faults-free representation. Thereby, the artificial output contains the filter states. The proposed D-EKF estimator does not require restrictive assumptions on the power system matrices and is highly robust against stochastic Gaussian noises. At the end, the proposed approach is applied on a practical faulty DC MG benchmark connected to a CPL, a resistive load, and an energy storage system and the obtained simulation results are analyzed form the accuracy and convergence speed viewpoints.