Coordinated charging of electric vehicles (EVs) is critical to provide safe and cost effective operation of distribution systems where household single phase charging of EV could contribute to imbalance of the distribution system. To date, reported researches on optimization methods for coordinated charging aiming at minimizing power losses have the disadvantages of low calculation efficiency when applied to large systems or have not taken the voltage constraints into account. The phase component and polar coordinates power flow equations of an unbalanced distribution system are derived. Primal dual interior point dynamic programming is introduced for coordinated charging of EVs to minimize distribution system losses where charging demand, voltage and current constraints have been taken into account. The proposed optimization is evaluated using an actual 423-bus case as the test system. Results are promising with the proposed method having good convergence under time-efficient calculations while providing optimization of power losses, lower load variance, and improvement of voltage profile versus uncoordinated scenarios.
In this paper, a spatial-temporal correlation aware data collection mechanism is proposed for a event-driven sensor network in terms of the realistic requirements such as real-time data sensing and dynamic network topology. Firstly, in order to reduce the path congestion and the data transmission delay, the perceived data states are classified based on binary representation. Secondly, a low cost manner is studied to aggregate the perceived data at the representative nodes and aggregation nodes respectively based on the spatial-temporal correlation. Furthermore, the best data collection path is obtained by carrying out a particle swarm optimization (PSO). Simulation results validate that the proposed algorithm can effectively reduce the amount of data transmissions in the network event area. Besides, the proposed mechanism also has advantages in reducing the delay and energy consumption. INDEX TERMS Event-driven sensor network, spatio-temporal correlation, data fusion, data collection.
Low frequency mechanical vibrations are ubiquitous in practical environments, and how to efficiently harvest them with piezoelectric materials remains a challenge. Frequency up-conversion strategies—up-converting low frequency vibrations to high frequency self-oscillations—can improve the power density of piezoelectric materials. This paper mainly introduces a kind of frequency which up-converts piezoelectric energy harvesters based on an internal resonance mechanism, compared with the other mechanisms caused by mechanical impact, mechanical plucking, etc.; the internal resonance-based harvesters can up-convert the frequency under a condition of lower excitation level, less energy loss, and less wideband operation bandwidth. Benefits to practical vibrations also exist in these multi-degree-of-freedom nonlinear dynamic systems. Moreover, the value of the frequency up-conversion factor based on the 1:2:6 internal resonance mechanism can reach as much as six so far, which is also a quite a high frequency up-conversion value.
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