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Fault localization in power distribution systems plays an important role in the resiliency of large-scale power distribution grids since its accuracy can influence the restoration time that needs to be minimized. In this article, we proposed a real-time deep learning algorithm to classify and localize the faults that occurred in the system based on measured data. To get the accurate fault location that may cause the potential fire, we extracted the feature vectors with the measured values of the distribution lines and applied a deep convolutional neural network classifier on them. Also, the presented method was able to detect the fault type, and compute its released energy that can contribute to wildfire based on the measured voltages and currents of the distribution nodes. Unlike prior data-driven methods, the proposed classifier was based on the single observability of the sensor measurement devices due to the limited access to the data and a limited number of measurement units. The performances of our scheme were validated through simulations of four types of faults in a real distribution test feeder under varying system observability, measurement attribute with and without distributed generation units. The proposed classifiers were able to determine the possible fire due to different fault types under 100% accuracy and find the possible fire location with an average accuracy of 85%. [ABSTRACT FROM AUTHOR]

This paper discusses a market-based pool strategy for a microgrid (MG) to optimally trade electric power in the distribution electricity market (DEM). The increasing penetration levels of distributed energy resources and MGs in distribution system (DS) stress distribution system operator and require higher levels of coordinated control strategies. The distribution system operator has limited visibility and control over such distributed resources. To reduce the complexity of the system and improve the efficiency of the electricity market operation, we propose a decentralized pool strategy for an MG to integrate with a distribution system through a market mechanism. A market-based interactions procedure between MGs and DS is developed for MGs as price makers to find an optimal bidding/offering strategy efficiently. To achieve a market equilibrium among all entities, we initially cast this problem as a bi-level programming problem, in which the upper level is an MG optimal scheduling problem and the lower level presents a DEM clearing mechanism. The proposed bi-level model is converted to a single mix-integer model, which is easier to solve. Uncertainties associated with MG's rivals’ offers and demands’ bids are considered in this problem. The solution results from a modified IEEE 33-Bus distribution system are presented and discussed. Finally, some conclusions are drawn and examined. [ABSTRACT FROM AUTHOR]

This paper addresses the microgrid expansion planning problem. In such a competitive electricity market, it will assist community microgrid (COMG) companies in deciding whether or not they should invest in microgrid installation. A two-stage stochastic optimization approach is proposed to eliminate the traditional centralized planning, which has led to competition among COMGs, generation companies (GENCOs), and transmission companies (TRANSCOs) for power delivery. The objective of the two-stage stochastic programming model is to maximize the expected revenue from these three power companies while ensuring the cost-effectiveness and reliability of the power system under uncertain factors, such as load growth and component outages. The proposed model is solved by decomposing the planning problem into two stages. The goal of the first stage is to maximize the profits of COMGs, GENCOs, and TRANSCOs; the goal of second stage is to minimize short-term operation cost considering uncertainty to enhance the reliability of the system. Computational results from two IEEE test systems are presented to analyze the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Presents corrections to the above named paper. [ABSTRACT FROM AUTHOR]