arXiv subjects

Yihsu Chen

Publications and source records attributed to Yihsu Chen.

6 recordsLinked to original sources

arXiv2026

Carbon-Aware Data Center Workload Allocation: Emission Disclosure, Capacity Leasing, and Contract Reshuffling

The rapid adoption of AI has driven rapid growth in computational demand, with large language models (LLMs) at the forefront since ChatGPT's debut in 2022. Meanwhile, large amounts of renewable energy are ultimately curtailed due to transmission congestion and inadequate demand. This work develops a power market model that allows hyperscalers to spatially migrate LLM inference workloads to geo-distributed modular datacenters (MDCs) co-located with renewable generation at the edge of the network. We introduce the optimization problems faced by the hyperscaler and MDCs in addition to consumers, producers, and the electric grid operator, where the hyperscaler leases MDC capacity while ensuring that required service level objectives (SLOs) are met. The overall market model is formulated as a complementarity problem, for which we establish equilibrium existence and uniqueness of certain aggregate market quantities. We further show that bilateral contract allocations can vary while preserving the same physical market outcome, so cleaner contract-attributed procurement need not imply additional clean generation. Applying the model to the IEEE RTS-24 bus system, we find that even when MDCs disclose the CO$_2$ emissions associated with their energy supply, renting less polluting MDCs yields limited system emission reductions because of \textit{contract reshuffling}. This effect can be mitigated when conventional loads are supplied through forward contracts such as power purchase agreements. Interestingly, this also reduces system congestion as the hyperscaler becomes increasingly cost-aware.

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arXiv2026

Learning in Stackelberg Markov Games

Designing socially optimal policies in multi-agent environments is a fundamental challenge in both economics and artificial intelligence. This paper studies a general framework for learning Stackelberg equilibria in dynamic and uncertain environments, where a single leader interacts with a population of adaptive followers. Motivated by pressing real-world challenges such as equitable electricity tariff design for consumers with distributed energy resources (such as rooftop solar and energy storage), we formalize a class of Stackelberg Markov games and establish the existence and uniqueness of stationary Stackelberg equilibria under mild continuity and monotonicity conditions. We then extend the framework to incorporate a continuum of agents via mean-field approximation, yielding a tractable Stackelberg-Mean Field Equilibrium (S-MFE) formulation. To address the computational intractability of exact best-response dynamics, we introduce a softmax-based approximation and rigorously bound its error relative to the true Stackelberg equilibrium. Our approach enables scalable and stable learning through policy iteration without requiring full knowledge of follower objectives. We validate the framework on an energy market simulation, where a public utility or a state utility commission sets time-varying rates for a heterogeneous population of prosumers. Our results demonstrate that learned policies can simultaneously achieve economic efficiency, equity across income groups, and stability in energy systems. This work demonstrates how game-theoretic learning frameworks can support data-driven policy design in large-scale strategic environments, with applications to real-world systems like energy markets.

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arXiv2026

Resilient Grid Hardening against Multiple Hazards: An Adaptive Two-Stage Stochastic Optimization Approach

The growing prevalence of extreme weather events driven by climate change poses significant challenges to power system resilience. Infrastructure damage and prolonged power outages highlight the urgent need for effective grid-hardening strategies. While some measures provide long-term protection against specific hazards, they can become counterproductive under conflicting threats. In this work, we develop an adaptive two-stage stochastic optimization framework to support dynamic decision-making for hardening critical grid components under multiple hazard exposures. Unlike traditional approaches, our model adapts to evolving climate conditions, enabling more resilient investment strategies. Furthermore, we integrate long-term (undergrounding) and short-term (vegetation management) hardening actions to jointly minimize total system costs. Extensive simulation results validate the effectiveness of the proposed framework in reducing outage and repair costs while enhancing the adaptability and robustness of grid infrastructure planning.

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arXiv2025

Wildfire Modeling: Designing a Market to Restore Assets

In the past decade, summer wildfires have become the norm in California, and the United States of America. These wildfires are caused due to variety of reasons. The state collects wildfire funds to help the impacted customers. However, the funds are eligible only under certain conditions and are collected uniformly throughout California. Therefore, the overall idea of this project is to look for quantitative results on how electrical corporations cause wildfires and how they can help to collect the wildfire funds or charge fairly to the customers to maximize the social impact. The research project aims to propose the implication of wildfire risk associated with vegetation, and due to power lines and incorporate that in dollars. Therefore, the project helps to solve the problem of collecting wildfire funds associated with each location and incorporate energy prices to charge their customers according to their wildfire risk related to the location to maximize the social surplus for the society. The thesis findings will help to calculate the risk premium involving wildfire risk associated with the location and incorporate the risk into pricing. The research of this submitted proposal provides the potential contribution towards detecting the utilities associated wildfire risk in the power lines, which can prevent wildfires by controlling the line flows of the system. Ultimately, the goal of this proposal is a social benefit to save money for the electrical corporations and their customers in California, who pay flat charges for Wildfire Fund each month $0.00580/kWh (in dollars). Therefore, this proposal will propose new method to collect wildfire fund with maximum customer surplus for future generations.

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arXiv2022

Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system's resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers' bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.

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arXiv2022

Optimal Retail Tariff Design with Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?

Distributed renewable resources owned by prosumers can be an effective way of fortifying grid resilience and enhancing sustainability. However, prosumers serve their own interests and their objectives are unlikely to align with that of society. This paper develops a bilevel model to study the optimal design of retail electricity tariffs considering the balance between economic efficiency and energy equity. The retail tariff entails a fixed charge and a volumetric charge tied to electricity usage to recover utilities' fixed costs. We analyze solution properties of the bilevel problem and prove an optimal rate design, which is to use fixed charges to recover fixed costs and to balance energy equity among different income groups. This suggests that programs similar to CARE (California Alternative Rate of Energy), which offer lower retail rates to low-income households, are unlikely to be efficient, even if they are politically appealing.

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