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.

Abstract
The increasing penetration of renewable energy sources in areas with wholesale energy markets may have significant impacts on the prices of electricity within these markets. These renewable energy sources typically have low or zero marginal prices and thus can bid into energy markets at prices which might be below plants using other generating technologies. This work seeks to understand the impact of these zero marginal cost plants in the Iberian Energy Market. This work makes use of an Artificial Neural Network (ANN) to evaluate the impact of growing renewable energy generation on the market-clearing price. Real data from the Iberian Energy Market is chosen and used to train the ANN. The scenarios used for renewable energy generation are taken from the newly published national energy and climate plans for both Spain and Portugal. Results show that increasing penetration of renewable energy leads to significant reductions in the forecasted energy price, showing a price decrease of about 23 (sic)/MWh in 2030 compared to the baseline. Increasing solar PV generation has the largest effect on market prices.

Abstract
Hydrogen vehicle stations (HVSs) that convert electricity into hydrogen have appeared as a new arrival asset to the power system with the raising interest in hydrogen vehicles (HVs). In order to safely power these new assets, microgrids, including different flexible resources, are an ideal option. This paper presents an efficient MG scheduling in the presence of HVSs, renewable energy resources, energy storage systems (ESS) and demand response. This model also takes the uncertainties associated with electrical loads, renewables, and HVs into consideration. In order to create an MILP problem, linearized AC optimal power flow equations are considered. A 21-bus MG is examined by applying the proposed model to various case studies, thereby proving that the MG schedule meets the demand of HVs and electrical load. Employing DR programs can reduce operation costs and reduce the load during peak usage hours. Furthermore, the physical constraints of the network satisfy the security in operation. Finally, numerical analysis illustrates the effectiveness of the proposed method.