arXiv subjects

Yongge Yang

Publications and source records attributed to Yongge Yang.

3 recordsLinked to original sources

arXiv2026

Stable AgBiS2 near-infrared absorbers for thin film tandem photovoltaics

AgBiS2 has rapidly risen in efficiency as single-junction photovoltaics (PVs), but its potential for tandem PVs is unexplored. Herein, we perform optical modeling based on the experimental absorption spectrum of AgBiS2, revealing that, as the NIR sub-cell, efficiencies exceeding 47.9% are achievable in triple-junction tandems, superseding tandems using Pb/Sn perovskite NIR cells (35.9%). However, fulfilling this potential requires overcoming the limited transport properties of AgBiS2 nanocrystal films, which constrains film thicknesses to only 30-50 nm, severely restricting NIR response. A solution-phase ligand exchange process is herein developed, allowing one-step deposition of passivated AgBiS2 nanocrystals that maintain efficient charge-carrier extraction despite an order of magnitude increase in film thickness. These films enable 24.0%-efficient four-terminal tandems with visible-light-absorbing lead-halide perovskite sub-cells, and monolithic tandems successfully demonstrating voltage addition. AgBiS2 PVs have high stability, retaining >90% of the original efficiency after 3 months of storage in dry air without encapsulation. This work establishes the significant potential of AgBiS2 as a stable and scalable NIR absorber for tandem PVs.

cond-mat.mtrl-sci↗

arXiv2024

Competitive Demand Learning: A Non-cooperative Pricing Algorithm with Coordinated Price Experimentation

We consider a periodical equilibrium pricing problem for multiple firms over a planning horizon of T periods. At each period, firms set their selling prices and receive stochastic demand from consumers. Firms do not know their underlying demand curve, but they wish to determine the selling prices to maximize total revenue under competition. Hence, they have to do some price experiments such that the observed demand data are informative to make price decisions. However, uncoordinated price updating can render the demand information gathered by price experimentation less informative or inaccurate. We design a nonparametric learning algorithm to facilitate coordinated dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. We show that the pricing decisions, determined by estimated demand functions, converge to underlying equilibrium as time progresses. We obtain a bound of the revenue difference that has an order of O(F^2 T^3/4) and a regret bound that has an order of O(F T^1/2) with respect to the number of the competitive firms F and T . We also develop a modified algorithm to handle the situation where some firms may have the knowledge of the demand curve.

cs.GT↗

arXiv2024

Robustness of Online Proportional Response in Stochastic Online Fisher Markets: a Decentralized Approach

This study is focused on periodic Fisher markets where items with time-dependent and stochastic values are regularly replenished and buyers aim to maximize their utilities by spending budgets on these items. Traditional approaches of finding a market equilibrium in the single-period Fisher market rely on complete information about buyers' utility functions and budgets. However, it is impractical to consistently enforce buyers to disclose this private information in a periodic setting. We introduce a distributed auction algorithm, online proportional response, wherein buyers update bids solely based on the randomly fluctuating values of items in each period. The market then allocates items based on the bids provided by the buyers. Utilizing the known Shmyrev convex program that characterizes market equilibrium of a Fisher market, two performance metrics are proposed: the fairness regret is the cumulative difference in the objective value of a stochastic Shmyrev convex program between an online algorithm and an offline optimum, and the individual buyer's regret gauges the deviation in terms of utility for each buyer between the online algorithm and the offline optimum. Our algorithm attains a problem-dependent upper bound contingent on the number of items and buyers under stationary inputs in fairness regret. Additionally, we conduct analysis of regret under various non-stationary stochastic input models to demonstrate the algorithm's efficiency across diverse scenarios. The online proportional response algorithm addresses privacy concerns by allowing buyers to update bids without revealing sensitive information and ensures decentralized decision-making, fostering autonomy and potential improvements in buyer satisfaction. Furthermore, our algorithm is universally applicable to many worlds and shows the robust performance guarantees.

cs.GT↗