Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations

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

Investigating potential biases in observed and modeled metrics of aerosol-cloud-precipitation interactions

This study utilizes large eddy simulation, aircraft measurements, and satellite observations to identify factors that bias the absolute magnitude of metrics of aerosol-cloud-precipitation interactions for warm clouds. The metrics considered are precipitation susceptibility <i>S</i><sub>o</sub>, which examines rain rate sensitivity to changes in drop number, and a cloud-precipitation metric, χ, which relates changes in rain rate to those in drop size. While wide ranges in rain rate exist at fixed cloud drop concentration for different cloud liquid water amounts, χ and <i>S</i><sub>o</sub> are shown to be relatively insensitive to the growth phase of the cloud for large datasets that include data representing the full spectrum of cloud lifetime. Spatial resolution of measurements is shown to influence the liquid water path-dependent behavior of <i>S</i><sub>o</sub> and χ. Other factors of importance are the choice of the minimum rain rate threshold, and how to quantify rain rate, drop size, and the cloud condensation nucleus proxy. Finally, low biases in retrieved aerosol amounts owing to wet scavenging and high biases associated with above-cloud aerosol layers should be accounted for. The paper explores the impact of these effects for model, satellite, and aircraft data

Accounting conservatism and banking expertise on board of directors

Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual directors on the board. We find that accounting conservatism is negatively affected by banking expertise on the board. Also, the results indicate that banking expertise on the board has a more pronounced impact on accounting conservatism when firms have high bankruptcy risk and when firms have high financial leverage. The evidence has some implications for boards of directors