We demonstrated in this paper the shape-controlled synthesis of ZnIn2S4, CuInS2, and CuInSe2 nano- and microstructures through a facile solution-based route. One-dimensional ZnIn2S4 nanotubes and nanoribbons were synthesized by a solvothermal method with pyridine as the solvent, while ZnIn2S4 solid or hollow microspheres were hydrothermally prepared in the presence of a surfactant such as cetyltrimethylammonium bromide (CTAB) or poly(ethylene glycol) (PEG). The mechanisms related to the phase formation and morphology control of ZnIn2S4 are proposed and discussed. The UV-vis absorption spectra show that the as-prepared nano- and micromaterials have strong absorption in a wide range from UV to visible light and that their band gaps are somewhat relevant to the size and morphology. The photoluminescence measurements of the ZnIn2S4 microspheres at room temperature reveal intense excitation at approximately 575 nm and red emission at approximately 784 nm. Furthermore, CuInS2 and CuInSe2 with different morphologies such as spheres, platelets, rods, and fishbone-like shapes were also obtained by similar hydrothermal and solvothermal synthesis.

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach.Index Terms-Smart power grid, plug-in electric vehicles, model predictive control, optimal power flow.

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