In this paper, we developed a dynamic energy-efficient virtual machine (VM) migration and consolidation algorithm based on a multi-resource energyefficient model. It can minimize energy consumption with Quality of Service guarantee. In our algorithm, we designed a method of double threshold with multiresource utilization to trigger the migration of VMs. The Modified Particle Swarm Optimization method is introduced into the consolidation of VMs to avoid falling into local optima which is a common defect in traditional heuristic algorithms. Comparing with the popular traditional heuristic algorithm Modified Best Fit Decrease, our algorithm reduced the number of active physical nodes and the amount of VMs migrations. It shows better energy efficiency in data center for cloud computing.
With the rapid development of clean energy, photovoltaic (PV) generation has been utilized in the harmonic compensation of power systems. This paper presents a novel multi-function PV micro-inverter with three stages (pseudo-two-stage). It can inject active power and compensate harmonic currents in the power grid at the same time. In order to keep the micro-inverter working under the maximum allowable output power, an optimized capacity limitation strategy is presented. Moreover, the harmonic compensation can be adjusted according to the customized requirements of power quality. Additionally, a phase shedding strategy in the DC/DC stage is introduced to improve the efficiency of parallel Boost converters in a wide range. Compared with existing capacity limitation methods, the proposed strategy shows better performance and energy efficiency. Simulations and experiments verify the feasibility of the micro-inverter and the effectiveness of the strategy.
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