Ram Rajagopal

Oskar Triebe, Fletcher Passow, Simon Wittner et al.

The reliability of local power grid infrastructure is challenged by sustainable energy developments increasing electric load uncertainty. Transmission System Operators (TSOs) need load forecasts of higher spatial resolution, extending current forecasting operations from zonal aggregates to individual nodes. However, nodal loads are less accurate to forecast and require a large number of individual forecasts, which are hard to manage for the human experts assessing risks in the control room's daily operations (operator). In collaboration with a TSO, we design a multi-level system that meets the needs of operators for hourly day-ahead load forecasting. Utilizing a uniquely extensive dataset of zonal and nodal net loads, we experimentally evaluate our system components. First, we develop an interpretable and scalable forecasting model that allows for TSOs to gradually extend zonal operations to include nodal forecasts. Second, we evaluate solutions to address the heterogeneity and volatility of nodal load, subject to a trade-off. Third, our system is manageable with a fully parallelized single-model forecasting workflow. Our results show accuracy and interpretability improvements for zonal forecasts, and substantial improvements for nodal forecasts. In practice, our multi-level forecasting system allows operators to adjust forecasts with unprecedented confidence and accuracy, and to diagnose otherwise opaque errors precisely.

Eric Zelikman, Sharon Zhou, Jeremy Irvin et al.

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting.

Gustavo Cezar, Ram Rajagopal, Baosen Zhang

This paper addresses stability issues of DC networks with constant power loads (CPL). Common DC networks, such as automotive electrical systems and DC microgrids, typically have a step-up/down converter connected in one side to the main bus and, on the other, to the load. When load is constant power it can generate destabilizing effects if not proper controlled. This paper shows that converters driving CPLs can make the system unstable, even if they are individually stable, depending on network parameters. We mitigate this problem by means of passive components externally connected to the converter/CPL subsystem. The analysis is verified through simulations. We are able to show that certain converter circuit configurations achieve the so called plug-and-play property, which stabilizes the interconnected system for all network parameters. This property is desirable since it is does not require the knowledge of detailed system topology and parameters, which can be time varying and difficult to obtain. This method also contrasts to existing practices of load augmentation, which can lead to severe efficiency losses.