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In the highly interdependent environment of a large city, failures in the Electrical Distribution System (EDS) can cause direct or indirect consequences to other critical infrastructures and the well-being of the citizens. To increase the resilience of the supply of electricity to the city, this work combines the pre-training of an AI agent and very fast calculation of the optimum recovery path after the number and location of the electrical faults are known. In the introduced Soft-Hard Optimal Convergence (SHOC) method, machine learning techniques are used to train an AI agent with thousands of off-line scenarios for optimum system restoration. In real-time, after the actual fault information is known, the agent will provide a subset of solutions (soft solution) to be considered for hard optimization algorithms. The Infrastructure Interdependencies Simulator (i2SIM) is used to assist the prioritization of the sequence of fault recovery and topological reconfiguration to minimize the black-out time of the most critical loads. A 70-node distribution system case is used to demonstrate the proposed methodology, with solution times in the order of seconds to find the optimum repair sequence and switches topological reconfiguration to optimize the city's resilience index. [ABSTRACT FROM AUTHOR]

Power flow analysis is one of the tools that is required in most of the distribution system studies. An important characteristic of distribution systems is the load unbalance in the phases and a three-phase power flow analysis is needed. In this paper, a three-phase linear power flow (3LPF) formulation is derived based on the fact that in a typical distribution system, voltage angles and magnitudes vary within relatively narrow boundaries. The accuracy of the proposed 3LPF is verified using several test cases. Potential applications of the proposed method are in distribution systems state estimation and volt-VAR optimization. [ABSTRACT FROM PUBLISHER]

Conventionally, power flow equations are used for distribution systems (DS) analysis to find the nodal voltages. For the particular form of the DS reconfiguration problem, however, a direct formulation in terms of branch flows allows a substantial increase in solution efficiency from an optimization point of view. In this paper, a set of linear current flow (LCF) equations are derived for DS. This formulation is then used within the network reconfiguration problem for loss minimization. A mixed-integer quadratically constrained programming (MIQCP) formulation, together with a mixed-integer linear programming (MILP) formulation, are proposed in this paper and assessed through simulations. In these comparisons, the MILP formulation shows computational advantages over the MIQCP version and the preceding literature. The proposed methods are evaluated on several test systems. [ABSTRACT FROM PUBLISHER]

In this paper, a framework for distribution system optimization is proposed. In this framework, different control variables, such as switchable capacitors, voltage regulators, and system configuration can be optimally determined to satisfy objectives, such as loss minimization and voltage profile improvement. Linearized power-flow equations are used in the optimization, and the problem is formulated as mixed-integer quadratic programming (MIQP), which has a guaranteed optimal solution. Existing efficient solution algorithms developed for MIQP problems facilitate the application of the proposed framework. System operational constraints, such as feeder ampacities, voltage drops, radiality, and the number of switching actions are considered in the model. The performance of the proposed framework is demonstrated using a variety of distribution test systems. [ABSTRACT FROM PUBLISHER]