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We study the problem of detecting abnormal inactivities within a single-occupied household based on smart meter readings. Such abnormal events include immobilizing medical conditions or sudden deaths of elderly or disabled occupants who live alone, the delayed discovery of which poses realistic social concerns as the population ages. Two novel unsupervised learning algorithms are developed and compared: one is based on nested dynamic time warping (DTW) distances and the other based on Mahalanobis distance with problem-specific features. Both algorithms are able to cold-start from limited historical data and perform well without extended parameter tuning. In addition, the algorithms are small profile in terms of data usage and computational need, and thus are suitable for implementation on smart meter hardware. The proposed methods have been thoroughly validated against real data sets with simulated target scenarios and have exhibited satisfactory performance. An implementation scheme on smart meter hardware is also discussed. 

Compared to a conventional mono-facial photovoltaic (PV) module, a bifacial one is more efficient as it receives light from not only the front but also the backside. The daily irradiance profile of a bifacial PV module is of a two-peak trajectory that almost coincides with the morning and evening peak demands. This interesting property helps distribution network operators better handle the issues caused by the abundance of conventional PVs during midday (i.e., Duck curve). Moreover, this two-humped profile can be incorporated into network operation strategies such as conservation voltage reduction (CVR). Thus, this paper proposes a new CVR framework that best uses the double-peak profile of bifacial PV modules to improve the voltage profile of a distribution network. The proposed framework optimally coordinates legacy voltage control devices, including on-load tap changers and voltage regulators, as well as Volt/VAr control of smart inverters. The effectiveness of the proposed framework is simulated and verified on the well-known modified 34-bus system using the Matlab-COM-OpenDSS platform. The results clearly demonstrate the advantages of bifacial PVs over their mono-facial counterparts. 

A high-voltage-gain dc-dc converter topology is proposed for renewable energy applications. The proposed coupled-inductor-based high-gain dc-dc converter features reduced input current ripple. The semiconductor elements voltage spikes due to the leakage inductance are prevented through the use of a clamping circuit. The Clamping circuit helps recover the leakage inductance stored energy, which causes voltage spikes on the switch. This results in the selection of elements with lower voltage ratings. Power switches with lower voltage ratings lead to lower conduction losses and improved system efficiency. The DC component of the inductor magnetizing current is zero. Consequently, no energy is stored in the inductor core, and the losses are further reduced. 

A new high-voltage-gain non-isolated dc-dc topology for applications in renewable energies is proposed. A coupled inductor with three windings is used to increase the proposed topology voltage gain. In addition to increasing the voltage gain, the proposed topology also has other prominent features including continuous input current and zero dc magnetizing inductance current, which reduces the losses and size of coupled inductor core. Furthermore, the continuous input current guarantees a low-volume input filter, which is essential for renewable energy applications. The leakage inductor stored energy is recycled via the diode and capacitor and transferred to the converter output for increasing the efficiency and reducing voltage stresses on the converter components. 

This paper proposes a solar energy harvesting based modular battery balance system for electric vehicles. The proposed system is designed to charge the battery module with minimum SOC/voltage by solar power during charging and discharging. With the solar power input, the useful energy of the battery can be improved while vehicle driving. For vehicle charging, the charging energy from grid and total charging time can be reduced as well. Simulation analysis shows that for a 50Ah rated battery pack, the overall pure electric drive mileage can be improved by 22.9%, while consumed grid energy and total charging time can be reduced by 9.6% and 9.3% respectively. In addition, the battery life can be improved around 10%~11%. The prototype design and test of a 48V battery pack vehicle consisting of four 12V battery modules are carried out. The experimental results validate that the system has good modular balance performance for the 100Ah battery modules with 5~7A charging current from solar power, and the overall usable battery energy has been increased.