3 research outputs found
Model Predictive BESS Control for Demand Charge Management and PV-Utilization Improvement
Adoption of battery energy storage systems for behind-the-meters application
offers valuable benefits for demand charge management as well as increasing
PV-utilization. The key point is that while the benefit/cost ratio for a single
application may not be favorable for economic benefits of storage systems,
stacked services can provide multiple revenue streams for the same investment.
Under this framework, we propose a model predictive controller to reduce demand
charge cost and enhance PV-utilization level simultaneously. Different load
patterns have been considered in this study and results are compared to the
conventional rule-based controller. The results verified that the proposed
controller provides satisfactory performance by improving the PV-utilization
rate between 60% to 80% without significant changes in demand charge (DC)
saving. Furthermore, our results suggest that batteries can be used for
stacking multiple services to improve their benefits. Quantitative analysis for
PV-utilization as a function of battery size and prediction time window has
also been carried out.Comment: Accepted in: Conference on Innovative Smart Grid Technology (ISGT),
Washington, DC, 201
Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation
The pulse-recloser uses pulse testing technology to verify that the line is
clear of faults before initiating a reclose operation, which significantly
reduces stress on the system components (e.g. substation transformers) and
voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are
essential to increases the overall utility of the devices, especially when
there are numerous devices installed throughout the distribution system. In
this paper, field data recorded from several devices were analyzed to identify
specific activity and fault locations. An algorithm is developed to screen the
data to identify the status of each pole and to tag time windows with a
possible pulse event. In the next step, selected time windows are further
analyzed and classified using a sparse representation technique by solving an
l1-regularized least-square problem. This classification is obtained by
comparing the pulse signature with the reference dictionary to find a set that
most closely matches the pulse features. This work also sheds additional light
on the possibility of fault classification based on the pulse signature. Field
data collected from a distribution system are used to verify the effectiveness
and reliability of the proposed method.Comment: Accepted in: 19th International Conference on Intelligent System
Application to Power Systems (ISAP), San Antonio, TX, 201