Abstract
Increased electrification of various critical infrastructures has been recognized as a key to achieving decarbonization targets worldwide. This creates a need to better understand the risks associated with future power systems and how such risks can be defined, assessed, and mitigated. This paper surveys prior work on power system risk assessment and management and explores the various approaches to risk definition, assessment, and mitigation. As a result, the paper proposes how future grid developments should be assessed in terms of risk causes, what methodology may be used to reduce the risk impacts, and how such approaches can increase grid resilience. While we attempt to generalize and classify various approaches to solving the problem of risk assessment and mitigation, we also provide examples of how specific approaches undertaken by the authors in the past may be expanded in the future to address the design and operation of the future electricity system to manage the risk more effectively. The importance of the metrics for risk assessment and methodology for quantification of risk reduction are illustrated through the examples. The paper ends with recommendations on addressing the risk and resilience of the electricity system in the future resilient implementation while achieving decarbonization goals through massive electrification.

Abstract
Microgrids are able to improve several features of power systems, such as energy efficiencies, operating costs and environmental impacts. Nevertheless, microgrids' protection must work congruently with power distribution protection to safely take all advantages. This research contributes to enable their protection by proposing a bilevel method to simultaneously solve the allocation and coordination problems, where the proposed scheme also includes local protections of distributed energy resources. The uncertainties associated with generation and loads are categorized by the k-means method, as well. The non-dominated sorting genetic algorithm II is employed in the upper-level task to solve the protection and control devices allocation problem with two opposing objectives. In the lower-level task, a genetic algorithm ensures their coordination. Protection devices include reclosers and fuses from the network, and directional relays for the point of common coupling of microgrids, while control devices consist of remote-controlled switches. In contrast to related works, local devices installed at the point of coupling of distributed generation units are considered as well, such as voltage-restrained overcurrent relays and frequency relays. The optimal solution for the decision-maker is achieved by utilizing the compromise programming technique. Results show the importance of solving the allocation and coordination problems simultaneously, achieving up to $25,000 cost savings compared to cases that solve these problems separately. The integrated strategy allows the network operator to select the optimum solution for the protective system and avoid corrective actions afterward. The results also show the viability of the islanding operation depending on the decision maker's criteria.

Abstract
The integration of inverter-based distributed generation challenges the implementation of an reliable protection This work proposes an adaptive protection method for coordinating protection systems using directional overcurrent relays, where the settings depend on the distribution network operating conditions. The coordination problem is addressed through a specialized genetic algorithm, aiming to minimize the total operating times of relays with time-delayed operation. The pickup current is also optimized. Coordination diagrams from diverse fault scenarios illustrate the method's adaptability to different operational conditions, emphasizing the importance of employing multiple setting groups for optimal protection system performance. The proposed technique provides high-quality solutions, enhancing reliability compared to traditional protection schemes.

Abstract
In this work, the implementation of an efficient multi-threading algorithm for calculating the power flow in electricity distribution networks is carried out using recursion and parallel programming. With the integration of renewable energy, energy storage systems and distributed generation, the ability of power flow simulations becomes a crucial factor in finding the best solution in the shortest possible time. We propose the direct use of graph theory to represent distribution network topologies. In this data structure, the traversal algorithms are inherently recursive, thus enabling the development of algorithms with parallel programming to obtain the power flow calculation faster and more efficiently. Results under a 809 buses test system show that the implementation provides additional computation efficiency of 32% with recursion techniques and 27% with parallel programming, due the expense of threads' allocation the combined gain reaches 50%.

Abstract
The intensification of environmental impacts and the increased economic risks are triggering a technological race towards a low-carbon economy. In this socioeconomic scenario of increasing changes and environmental concerns, microgrids (MGs) play an important role in integrating distributed energy resources. Thus, a planning strategy for grid-connected MGs with distributed energy resources and electric vehicle (EV) charging stations is proposed in this paper. The developedmathematical model aims to defineMGexpansion decisions that satisfy the growing electricity demand (including EV charging demand) at the lowest possible cost; such decisions include investments in PV units, wind turbines, energy storage systems, and EV charging stations. The objective function is based on the interests of the MG owner, considering constraints associated with the main distribution grid. A mixed-integer linear programming model is used to formulate the problem, ensuring the solution's optimality. The applicability of the proposed model is evaluated in the 69-bus distribution grid. Promising results concerning grid-connected MGs were obtained, including the enhancement of energy exchange with the grid according to their needs.