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
The number of electric vehicles (EVs) on the road is expected to continue to increase during the next decades due to various factors such as the rapid progress in EV technology and decreasing battery prices. The prolonged battery charging process, which is one of the main problems that affects the increased EV penetration, makes the fast charging units more attractive and efficient option for the charging stations. In this study, a control strategy for a DC microgrid including electric vehicle fast charging station (EVFCS) and distributed generation units is presented to examine the impacts of EVFCS on the grid as well as their potential contributions to the system operation in the case of considering the vehicle-to-grid (V2G) technology. It is especially aimed to mitigate the voltage sag and swell problems by using the EV battery as a DC source of a distribution static compensator (D-STATCOM) device. Simulation studies in MATLAB Simulink/SimPower systems show that considerable improvements can be achieved from the perspective of distribution system operation such as improved voltage quality and from the perspective of end users such as decreased charging durations.

Abstract
The resilient smart distribution system is intended to cope with low probability, high-risk extreme events, including extreme natural disasters. In this regard, the flexible partitioning distribution system into supply-sufficient microgrids can be considered an interesting subject. This paper introduces a novel technique for partitioning a smart distribution system into supply-sufficient microgrids. In the presented model, the impact of demand response programs and energy storage has been considered in the proposed framework. Furthermore, besides the electricity aspects, the effects of gas network infrastructure on flexible portioning are also investigated to improve system resiliency facing natural disasters. The proposed model aims to maximize served load, which has been structured as a mixed integer linear programming (MILP) problem. The IEEE 34 bus standard test system is used to investigate the effectiveness of the proposed structure. The results of the study show that the proposed structure can increase the served load and the supply-sufficiently of the smart grid power system.

Abstract
The publication of the 2020 Special Section of Invited Papers on Emerging Topics in the Power and Energy Society (PES) received broad interest from diverse readers of the PES community. We continued to publish the special section for another year. After a rigorous peer-review process, a total of 13 papers [A1]-[A13] were accepted for publication in this invited section of the IEEE Open Access Journal of Power and Energy (OAJPE) in 2021.

Abstract
The potential of end-users to modify their consumption pattern makes them an interesting resource for providing energy flexibility in energy communities. Thus, active end-users require sufficient incentives and automated trading and management schemes. In order to enable increased small-scale end-users participation for flexibility service provision, a new design for flexibility trading is required to model the behavior of different agents and their interactions in energy communities. The novelty of our work lies in proposing an iterative game-based approach in which all agents - consisting of the distribution system operator (DSO), aggregators, and customers- can determine their decision variables to optimize their own objective functions and interact with others to modify their decisions according to others' decisions. In addition, three scenarios are considered to study the effects of agents' freedom while setting their decision variables (by removing one of their constraints in their corresponding decision-making problem). Moreover, the impact of the presence of interruptible loads in comparison with shiftable loads is investigated in this paper. According to the simulation results, it is found that in the scenario where end-users have fewer constraints, in presence of interruptible loads, end-users gain greater income compared to the absence of interruptible loads.

Abstract
The active participation of small-scale prosumers and consumers with demand-response capability and renewable resources can be a potential solution to the environmental issues and flexibility-related challenges. Local energy markets based on peer-to-peer trading is defined as one of solutions to exploit the maximum flexibility potential of prosumers. However, the existing literature that proposed peer-to-peer based local energy markets did not lead to respecting the peers' energy trading preferences simultaneously in the profitable market settlement. To solve this issue, a new local energy market model is presented in which network users can trade with their preferred trading partners within the local market as well as the grid. The proposed trading model includes two levels to consider both the democracy and the profitability of energy trading. At the first level, the model considers the trading preferences of each player to respect the peers' choices. The second level matches the rest of the bids and offers of the local buyers and sellers aiming to maximize the social welfare of all of the players participating in the local market. Our proposed local market is implemented for a test system consisting of fifteen residential players, and the results are compared to other trading models through different comparison criteria such as social-welfare of all players and the net cost of each individual player from consuming electricity. According to the results, the proposed model stands in the second rank compared to the other models that do not simultaneously consider preferences and social welfare of the peers, in terms of social welfare, total profits of the players, and the sustainability and liquidity-based criteria. The proposed model achieves 1416-Cent as the total net energy costs of all peers and the total accepted blocks equaling 76. This means that the proposed local market model can still be profitable and liquid while respecting the players' trading preferences and choices.