In thin-film photovoltaic (PV) research and development, it is of interest to determine where the chief losses are occurring within the active layer. Herein, a method is developed and presented by which the spatial distribution of charge collection, operando, is ascertained, and its application in colloidal quantum dot (CQD) solar cells is demonstrated at a wide range of relevant bias conditions. A systematic computational method that relies only on knowledge of measured optical parameters and bias-dependent external quantum efficiency spectra is implemented. It is found that, in CQD PV devices, the region near the thiol-treated hole-transport layer suffers from low collection efficiency, as a result of bad band alignment at this interface. The active layer is not fully depleted at short-circuit conditions, and this accounts for the limited short-circuit current of these CQD solar cells. The high collection efficiency outside of the depleted region agrees with a diffusion length on the order of hundreds of nanometers. The method provides a quantitative tool to study the operating principles and the physical origins of losses in CQD solar cells, and can be deployed in thin-film solar cell device architectures based on perovskites, organics, CQDs, and combinations of these materials.

We present the online Newton's method, a single-step second-order method for online nonconvex optimization. We analyze its performance and obtain a dynamic regret bound that is linear in the cumulative variation between round optima. We show that if the variation between round optima is limited, the method leads to a constant regret bound. In the general case, the online Newton's method outperforms online convex optimization algorithms for convex functions and performs similarly to a specialized algorithm for strongly convex functions. We simulate the performance of the online Newton's method on a nonlinear, nonconvex moving target localization example and find that it outperforms a first-order approach.

We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and does not depend explicitly on the time horizon. We use dynamic-online DWDA (D-ODWDA) and formulate a performance-guaranteed distributed online demand response approach for heating, ventilation, and air-conditioning (HVAC) systems of commercial buildings. We show the performance of our approach for fast timescale demand response in numerical simulations and obtain demand response decisions that closely reproduce the centralized optimal ones.

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