We explore the behavior of wind speed over time, using a subset of the Eastern Wind Dataset published by the National Renewable Energy Laboratory. This dataset gives modeled wind speeds over three years at hundreds of potential wind farm sites. Wind speed analysis is necessary to the integration of wind energy into the power grid; short-term variability in wind speed affects decisions about usage of other power sources, so that the shape of the wind speed time series becomes as important as the overall level. To assess differences in intra-day time series, we propose a functional distance measure, the band distance, which extends the band depth of López-Pintado and Romo. This measure emphasizes the shape of time series or functional observations relative to other members of a dataset and allows clustering of observations without reliance on pointwise Euclidean distance. We show a method for adjusting for seasonal effects in wind speed, and use these standardizations as input for the band distance. We demonstrate the utility of the new method in simulation studies and an application to the MOST power grid algorithm, where the band distance improves reliability over standard methods at a comparable cost.

As the level of renewable and distributed energy resources (DER) increases in power systems, there is a coincident effort to ensure ongoing reliability. Microgrids are likely to play a central role in this development globally. However, a counterpoint is the high cost of microgrid operations, and there exists a need to develop efficient tools to operate microgrids optimally and economically. In this paper, the potential of demand response (DR) to reduce microgrid operation cost while supporting renewable integration is investigated. Three types of DR, namely thermostatically controlled load (TCL), deferrable load (DL), and elastic load (EL), are explored in the context of various system conditions. Because systems with significant renewables and DER are subject to high levels of uncertainty, the investigation is conducted under a stochastic rolling-horizon optimization framework that leverages the update of renewable generation forecast and the energy market real-time prices (RTP). A case study illustrates that certain system conditions, such as price peaks and moderate temperatures, facilitate best demand response performance. Conversely, inaccurate price forecast information can lead to ineffectual operation of microgrids and result in higher cost. The insights provided by the study of various types of DR are helpful for microgrid design with consumers’ preferences taken into consideration.