Abstract
Power distribution system resilience is not considered in traditional planning of distributed generator (DG), which leads to severe power outages and economic losses during extreme weather event. To address this issue, this paper proposes a resilience-oriented comprehensive planning strategy of DG in power distribution system. The problem is formulated as a two-stage stochastic mixed-integer second-order cone programming (SMISOCP). The objective of the first stage is to determine the number, location and capacity of DG and obtain the economic cost of power distribution system. The second stage minimizes the resilience cost under uncertain failure scenarios. First, sufficient failure scenarios are generated by the Monte Carlo method. Then the failure scenarios are reduced to the most representative scenarios by using the K-means clustering algorithm to reduce computational burden. Finally, the two-stage SMISOCP is solved based on the reduced failure scenarios. The simulation results of the IEEE 33-bus test systems illustrate the effectiveness of the proposed two-stage strategy.
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