The judicious placement of disconnecting switches is an efficient means to enhance the reliability of distribution networks. Aiming at optimizing the investment in these switches, this paper presents a mathematical programming-based model considering the installation of remote-controlled and manual switches at various locations in the distribution network. The proposed model not only yields the optimal location and type of switches in the main feeders but also specifies the optimal type of tie switches, i.e., backup switches at the reserve connection points. Incentive reliability regulation in the form of a reward-penalty scheme is incorporated into the proposed model to take the distribution service reliability worth into account realistically. In addition to this cost, the revenue lost due to energy undelivered during the distribution network faults is considered to determine the unreliability costs more accurately. In order to estimate such reliability-related costs, a novel reliability assessment technique is developed and integrated into the proposed switch optimization model. Formulated as an instance of mixed-integer linear programming, the proposed model is applied to a test distribution network, and the outcomes are investigated in detail. INDEX TERMS Electricity distribution system, mixed-integer linear programming, reliability, reward-penalty scheme, switch optimization. NOMENCLATURE INDICES

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In the past decade, enhancing the reliability of distribution networks by means of optimal switch placement has attracted much attention. In the case of failures in a distribution feeder, such disconnect switches will isolate the faulted section, and the customers downstream of the faulted point can be supplied by neighboring feeders through tie lines. Nevertheless, such reserve branches not only might experience failures themselves but also may not even exist prior to the switch placement. Accordingly, this paper presents a mathematical-programming-based model for the concurrent placement of disconnect switches and tie lines in the distribution networks to enhance the service reliability, considering both practical benefits and drawbacks of such reserve branches. In the proposed model, installation of remote-controlled and manual switches at various locations of distribution feeders together with potential tie lines are considered. Also, practical operational constraints regarding the utilization of tie lines, and the impact of failures in such reserve branches on the reliability indices are meticulously modeled in the proposed formulation. Unreliability cost is estimated based on a reward-penalty scheme and the revenue lost due to the not supplied demand during the network contingencies. As an instance of mixed-integer linear programming, the proposed optimization model can be efficiently solved to the global optimality using commercially available software. Aiming at investigating the applicability of the proposed model, it is implemented on a test network, and the results are thoroughly analyzed through various case studies.

Sectionalizing switches (SSs) and tie lines play essential roles in reducing the duration of customer interruptions in electricity distribution networks. The effectiveness of such assets is strongly influenced by their placement in the grid. Operation of SSs and tie lines is also inherently interdependent. Due to the structural complexities regarding the mathematical modeling of such dependencies, optimization of the planning and operation of switches and tie lines has typically required either leveraging heuristic and metaheuristic approaches or oversimplifying the network topology. To tackle such issues, this paper presents a computationally-efficient model for reliabilityoriented concurrent switch and tie line placement in distribution networks with complex topologies. The proposed model can be applied to grids with several tie lines and laterals per feeder, and yields the optimal location of tie lines, type of tie switches, namely manual or remote-controlled, and the location and type of SSs. Being cast as a mixed integer linear programming (MILP) problem, the model can be efficiently solved with guaranteed convergence to global optimality using off-the-shelf optimization software. The efficiency and scalability of the proposed model are demonstrated through implementation on five networks and the outcomes are thoroughly discussed.

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