The future distribution grid has complex analysis needs, which may not be met with the existing processes and tools. In addition there is a growing number of measured and grid model data sources becoming available. For these sources to be useful they must be accurate, and interpreted correctly. Data accuracy is a key barrier to the growth of the future distribution grid. A key goal for California, and the United States, is increasing the renewable penetration on the distribution grid. To increase this penetration measured and modeled representations of generation must be accurate and validated, giving distribution planners and operators confidence in their performance. This study will review the current state of these software and modeling barriers and opportunities for the future distribution grid.

Accurate evaluation of the performance of buildings participating in Demand Response (DR) programs is critical to the adoption and improvement of these programs. Typically, we calculate load sheds during DR events by comparing observed electric load against counterfactual predictions made using statistical baseline models. Many baseline models exist and these models can produce different shed estimates. Moreover, modelers implementing the same baseline model can make different modeling implementation choices, which may affect shed estimates. In this work, using real data, we analyze the effect of different modeling implementation choices on shed estimates. We focus on five issues: weather data source, resolution of data, methods for determining when buildings are occupied, methods for aligning building data with temperature data, and methods for power outage filtering. Results indicate sensitivity to the weather data source and data filtration methods as well as an immediate potential for automation of methods to choose building occupied modes.

Accurate evaluation of the performance of buildings participating in Demand Response (DR) programs is critical to the adoption and improvement of these programs. Typically, we calculate load sheds during DR events by comparing observed electric demand against counterfactual predictions made using statistical baseline models. Many baseline models exist and these models can produce different shed calculations. Moreover, modelers implementing the same baseline model can make different modeling implementation choices, which may affect shed estimates. In this work, using real data, we analyze the effect of different modeling implementation choices on shed predictions. We focused on five issues: weather data source, resolution of data, methods for determining when buildings are occupied, methods for aligning building data with temperature data, and methods for power outage filtering. Results indicate sensitivity to the weather data source and data filtration methods as well as an immediate potential for automation of methods to choose building occupied modes.

We describe a method to generate statistical models of electricity demand from Commercial and Industrial (C&I) facilities including their response to dynamic pricing signals. Models are built with historical electricity demand data. A facility model is the sum of a baseline demand model and a residual demand model; the latter quantifies deviations from the baseline model due to dynamic pricing signals from the utility. Three regression-based baseline computation methods were developed and analyzed. All methods performed similarly. To understand the diversity of facility responses to dynamic pricing signals, we have characterized the response of 44 C&I facilities participating in a Demand Response (DR) program using dynamic pricing in California (Pacific Gas & Electric’s Critical Peak Pricing Program). In most cases, facilities shed load during DR events but there is significant heterogeneity in facility responses. Modeling facility response to dynamic price signals is beneficial to the Independent System Operator for scheduling supply to meet demand, to the utility for improving dynamic pricing programs, and to the customer for minimizing energy costs.