After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

Virtualization is the enabling technology that makes resource provisioning in Cloud computing feasible. With virtualization, virtual machines (VMs) can be migrated across physical hosts to achieve better utilization of resource with a minimum impact on service quality. The VM allocation problem can be formulated as a stable matching problem. In this paper, we propose a VM allocation mechanism based on stable matching. A deferred acceptance procedure is adopted to handle conflicts among preferences of VMs and physical hosts. Unlike ordinary stable matching problems, both involving party groups in our matching process are having a mutual objective, that is to reduce the overall energy consumption of a Cloud data center while maintaining a high level of Quality of Service. The proposed mechanism is evaluated using CloudSim with real-world workload data. Simulation results show that Cloud data centers with the proposed mechanism can reduce energy consumption and avoid violations of Service-Level Agreement.