Remote-controlled switches (RCSs) have the ability to quickly isolate the faulted area from other parts of distribution systems. On the other hand, the distributed generation resources (DGs) and tie lines can supply the interrupted loads after fault occurrence as a microgrid. In this regard, this paper proposes a planning model for simultaneous placement of RCSs, dispatchable DGs (DDGs), and tie lines in distribution systems with complex topologies to improve their reliability. Presence of renewable DGs (RDGs) including photovoltaic cells (PVs) and wind turbines (WTs), as well as the uncertainties of loads, RDGs, and outage duration of the faulted areas are also considered in the proposed planning model. Conditional value at risk (CVaR) is used to manage the system risk. The proposed planning model is formulated as a mixed integer linear programming model (MILP), which can be solved using various commercial solvers and give the global optimal solution. Finally, the proposed planning model is implemented on the IEEE 33 bus system as various cases to illustrate its effectiveness on improving the reliability of distribution systems and managing the system risk.
Due to the accelerated climate change, it is anticipated that the number and severity of natural disasters such as hurricanes, blizzards, and floods will be increased in the coming years. In this regard, this paper presents a distribution system planning model to improve the system resilience against hurricane. A scenario-based mathematical model is proposed to capture the random nature of weather events. Moreover, a stochastic optimization model is developed to simultaneously harden the distribution lines and place different types of distributed generation (DG) units such as microturbines (MTs), wind turbines (WTs), and photovoltaic cells (PVs). The conditional value at risk (CVaR) is used as a risk index to manage the system risk against different failure scenarios. The problem is formulated as a mixed integer linear programming (MILP) model that can be solved by various commercial solvers. Finally, to illustrate the effectiveness of the proposed model, it is implemented on the IEEE 33 bus system, and various case studies are defined. The results show the effectiveness of our mathematical model in improving the distribution system resiliency and managing the system risk.
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