arXiv subjects

Yuting Cai

Publications and source records attributed to Yuting Cai.

5 recordsLinked to original sources

arXiv2025

Expected Revenue, Risk, and Grid Impact of Bitcoin Mining: A Decision-Theoretic Perspective

Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and upside potential profit from the first principles of mining: Each hash is a Bernoulli trial with a Bitcoin block difficulty-based success probability. The model yields closed-form expected revenue per hash-rate unit, risk metrics in different scenarios, and upside-profit probabilities for different fleet sizes. Empirical calibration closely matches previously reported observations, yielding a unified, faithful quantification across hardware, pools, and operating conditions. This foundation enables more reliable analysis of mining impacts and behavior.

cs.CE↗

arXiv2025

Fr\'{e}chet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids

Generative artificial intelligence (AI) models in smart grids have advanced significantly in recent years due to their ability to generate large amounts of synthetic data, which would otherwise be difficult to obtain in the real world due to confidentiality constraints. A key challenge in utilizing such synthetic data is how to assess the data quality produced from such generative models. Traditional Euclidean distance-based metrics only reflect pair-wise relations between two individual samples, and could fail in evaluating quality differences between groups of synthetic datasets. In this work, we propose a novel metric based on the Fr\'{e}chet Distance (FD) estimated between two datasets in a learned feature space. The proposed method evaluates the quality of generation from a distributional perspective. Empirical results demonstrate the superiority of the proposed metric across timescales and models, enhancing the reliability of data-driven decision-making in smart grid operations.

cs.LG↗

arXiv2024

Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector

Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm towards adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this article identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.

eess.SY↗

arXiv2023

Optimization of Cryptocurrency Miners' Participation in Ancillary Service Markets

Proof-of-work computation used in cryptocurrencies has witnessed significant growth in the U.S. and many other regions around the world. One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand. On the other hand, the electric energy system is increasing the need for flexibility for energy balancing and ancillary services due to the intermittent nature of many new energy resources such as wind and solar. In this work, we model the operation of a cryptomining facility with heterogeneous mining devices participating in ancillary services. We propose a general formulation for the cryptominers to maximize their profit by strategically participating in ancillary services and controlling the loss of mining revenue, which requires taking into account the disparity in the efficiency of the mining machines. The optimization formulation is considered for both offline and online scenarios, and optimal algorithms are proposed to solve these problems. As a special case of our problem, we investigate cryptominers' participation in frequency regulation, where the miners benefit from their fast-responding devices and contribute to grid stability. In the second special setting, a risk-aware algorithm is proposed to jointly minimize the cost and the risk of participating in ancillary services with homogeneous mining devices. Simulation results based on real-world Electric Reliability Council of Texas (ERCOT) traces show more than 20\% gain in profit, highlighting the advantage of our proposed algorithms.

eess.SY↗

arXiv2021

Quantum emitter formation in carbon-doped monolayer hexagonal boron nitride

Single photon emitters (SPEs) in hexagonal boron nitride (hBN) are promising candidates for quantum light generation. Despite this, techniques to control the formation of hBN SPEs down to the monolayer limit are yet to be demonstrated. Recent experimental and theoretical investigations have suggested that the visible wavelength single photon emitters in hBN originate from carbon-related defects. Here we demonstrate a simple strategy for controlling SPE creation during the chemical vapor deposition growth of monolayer hBN via regulating surface carbon concentration. By increasing surface carbon concentration during hBN growth, we observe increases in carbon doping levels by 2.4 fold for B-C bonds and 1.6 fold for N-C bonds. For the same samples we observe an increase in SPE density from 0.13 to 0.30 emitters/um2. Our simple method enables the reliable creation of hBN SPEs in monolayer samples for the first time, opening the door to advanced 2D quantum state engineering.

cond-mat.mtrl-sci↗