We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of two detailed mathematical models and coordination algorithms, which can benefit the operation of both entities involved, i.e., the grid operations and EV charging. The first model comprises of a comprehensive optimal power flow model at the distribution grid level, while the second model represents detailed optimal EV charging with reactive power support to the grid. We demonstrate benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that in constrained distribution grids, coordinated charging significantly reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to unity power factor.

The guts of Tenebrio Molitor Linnaeus (yellow mealworm) were used as inocula to isolate reducing sugar-producing bacteria during bioconversion of lignocellulose to reducing sugars in this study. Three carbon sources, i.e., carboxymethyl cellulose (CMC), filter paper (FP), and lignocellulosic waste (LIG), were specifically selected; and two types of culturing media (M1 and M2) were used. After 6 months of sequential cultivation, lignocellulose (i.e., polysaccharides) degradation of enrichments M1-CMC (47.5%), M1-FP (73.3%), M1-LIG (70.4%), M2-CMC (55.7%), M2-FP (73.1%) and M2-LIG (71.7%) was achieved, respectively, with incubation for 48 h. Furthermore, seven bacterial strains were successfully isolated corresponding to most of the major bands detected by denaturing gradient gel electrophoresis analysis. The maximum reducing sugars yield by the combination of Agromyces sp. C42 and Stenotrophomonas sp. A10b was 56.7 mg g·LIG-1 of 48 h, which is approximate 2-5 times higher than the original enrichments and individual microbial strains. These findings suggest that bioconversion by microorganisms from mealworm guts has great application potential for lignocellulose hydrolysis.

Conventionally, voltage control in distribution grids has been achieved through load tap changers, capacitor banks, and network switches. With the advancements in on-board electric vehicle (EV) chargers, voltage control can be achieved through control of EV charging/discharging as EVs can operate in any of the four P-Q quadrants with slight modifications to the charger. In this work, we use a high-fidelity on-board EV charger model, which has been validated using actual measurements. Then, we coordinate the four quadrant operation of EVs and distribution feeder to support voltage control in the grid. The efficacy of the developed models are demonstrated by using an LV secondary feeder. Operation of EVs in all four quadrants are shown to compensate for the feeder voltage fluctuations caused by daily time varying residential loads, while satisfying operational constraints of the feeder.

This paper first presents a brief review of existing computation techniques and challenges in distribution system state estimation. Then, the impacts of measurement points, network configuration, load model, distributed generation, real-time measurement data, and computational parameters on the state estimation of distribution grids are investigated. Simulations are carried out on an example utility distribution network which is fed by a large high voltage transmission network at multiple points. The results show that greater observability of downstream distribution network enhances accurate estimation of network states. Similarly, load models and distributed generations have significant impact on state estimation. The results also show that the accuracy of state estimation depends on the choice of computational parameters.