This study proposes a two-stage stochastic optimisation model for jointly wind turbine (WT) allocation and network reconfiguration (NR) so as to increase the resiliency of distribution system in face of natural disasters. In this regard, in the first level, a possibilistic-scenario method is proposed to select the line outage scenarios. The proposed model is capable with distribution systems and considers different failure probabilities for system components subject to the intensity of natural disaster in its associated zone. After selecting the line outage scenarios, in the second level, a multi-stage optimisation framework is proposed for jointly NR and WT allocation in a multi-zone and multi-fault system, considering the uncertainty of system load and wind power generation. This strategy makes an interconnection between NR and islanded WTs to increase the resiliency of system and decreases the load shedding. Different economic objectives including, costs of load shedding and power generation are considered in the model. In addition, hardening budget is taken into consideration for the transmission lines, which is minimised during the optimisation process. The simulation results demonstrate the capability and necessity of proposed resiliency-oriented method and prove the importance of hardening budgets.
Due to increasing the intricacies of cyber-physical systems (CPSs) and the severity of natural phenomena, upgrading network planning is vital to reduce the vulnerability of these systems. This study develops a novel preventive-corrective resilient energy management strategy (PC-REMS) for a CPS in two stages, exploiting the network reconfiguration (NR) and energy storage systems (ESSs) capacity. The first stage of the proposed PC-REMS follows preventive actions based on contingency faults. In contrast, the second stage applies corrective measures for improving the CPS resilience to cope with natural physical disasters. Vulnerability assessment data is sent to the physical power system daily through the communication network. The first stage of preparing the CPS for predictable faults focuses on pre-scheduled ESSs and preventive NR to minimise the expected energy curtailment cost. The second stage involves the network recovery in real-time through corrective NR to minimise energy curtailment cost after the faults. Three resistance, recovery, and resilience indices are introduced for evaluating the effectiveness of the model. The proposed model is examined by performing multiple simulations on the 33 and 118-bus radial test systems. The simulation results show the efficiency of the proposed PC-REMS model in dealing with predictable disasters to improve the CPS resilience.
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