On Smoothing the Duck Curve: A Control Perspective

Abstract

The increased adoption of small-scale solar photo-voltaics (PV s) has led to drastic changes in the aggregate load profile in multiple locations, resulting in what is called the “Duck Curve.” This adds a burden on system operators and might, in fact, jeopardize real-time operations and control. In this paper, we address these issues via learning-based control and develop an online method to flatten the duck curve by optimizing standard-sized batteries. In particular, we use deep learning in conjunction with model predictive control (MPC), i.e., we forecast solar power and demand and then utilize these forecasts to optimize storage over a prediction horizon. In our approach, forecasts take into account behavioral aspects of load consumption, and we also propose an objective function that mimics the Peak-to-Average power ratio. We have conducted numerical experiments using real data, and the results are promising, demonstrating a reduction of about 67% of the Peak-to-Average power ratio.