This article presents a robust model predictive control (R-MPC)-based scheduling approach for electric vehicle (EV) charging service at a station with power supply from a photovoltaic (PV) system and grid. The designed algorithm aims to maximize the operational revenue while handling high power loads in the grid, intermittent solar power supply, charging demands uncertainties, and online computational burdens. To this end, the scheduling problem is formulated as a mixed-integer nonlinear program (MINLP) with solar power generation and charging demands models embedded. The time-of-use price is employed for cost calculation. The resulting optimization problem then can be further simplified as a mixed-integer linear program (MILP). The proposed R-MPC is evaluated in the IEEE-13 node test feeder with simulated power load profiles and real charging demands data for demonstrations. The results show that R-MPC achieves near-optimal profit and meets all charging requests even under high uncertainties.

This paper presents the feature extraction, selection and K-Nearest Neighbors (K-NN) algorithm to classify behaviors of sharks based on the data collected by tri-axial acceleration data loggers (ADLs). Because these behaviors are hard to observe in the wild and do not occur frequently, being able to adequately classify them is extremely challenging. In the proposed scheme, we first employ several transformations to enrich the static and dynamic acceleration data. Then, the enhanced data is converted from time to the frequency domain through the fast Fourier transform (FFT) for noise removal. A modified K-NN approach integrated with feature selection is developed and conducted on the frequency domain data to improve the F1-score for minority classes. The acceleration data of California horn sharks (Heterodontus francisci) gathered through ADLs mounted on the first dorsal fin is used to demonstrate our algorithm. A comparison study shows that the features extracted and selected by our proposed scheme can significantly improve the performance of classification on the imbalanced dataset.

This paper develops a model predictive controller (MPC) via photovoltaic (PV)-based volt-ampere reactive scheme to minimize the power loss and stabilize voltage fluctuation when PV cells are connected to the power distribution line. The nominal power load data from California independent system operator is used to simulate dynamics of the system with DistFlow equations. Since power consumptions in fact may deviate from nominal values, an estimator is further developed to reconstruct the state variables and power loads from measurements. The integration of the MPC and estimator forms a closed-loop control framework and enables the system to quickly recover from undesired disturbances by effectively changing the real and reactive powers provided by PV cells. We use a bidirectional, single branch distribution circuit to demonstrate the performance of proposed scheme. The results show that our MPC indeed reduces power loss and keeps the voltage within a desired bound. Additionally, the estimator successfully detects and correctly estimates the abnormal change of power consumption and directs the MPC to compensate such unexpected power loads promptly.