arXiv subjects

Yu Weng

Publications and source records attributed to Yu Weng.

7 recordsLinked to original sources

arXiv2026

Modeling of Network Constraints in Large-scale Capacity Expansion Optimization of Power Grids

Capacity expansion modeling plays a critical role in optimizing the deployment of new generation, storage, and transmission, typically at national and regional levels. To support long-term planning, these models consider a large set of energy technologies and policies, along with decades of weather and demand data. Realistic capacity expansion models thus become high-dimensional optimization problems, with hundreds of millions of variables and constraints, which are challenging to solve. A common strategy to address this complexity is to omit non-linear, non-convex AC optimal power flow (ACOPF) constraints and instead use linearized power balance equations or transport formulations. While these simplifications improve tractability, they limit our understanding of how power flow and the physical properties of power networks impact investment decisions across generation, storage, and transmission infrastructure. This paper addresses this gap by extending the GenX capacity expansion model to incorporate fixed point theorem-based network constraints. These embed ACOPF-based considerations while maintaining the tractability of the planning model, nearly preserving the dimensionality of the transport formulation and incurring only modest runtime increases. This approach is much cheaper than embedding ACOPF directly, making it appropriate for large-scale capacity planning problems. We compare our approach to the original transport-based GenX model as well as a non-linear, non-convex version that incorporates the full ACOPF constraints, for a case study of the ISO New England grid.

math.OC↗

arXiv2025

MacroEnergy.jl: A large-scale multi-sector energy system framework

MacroEnergy.jl (aka Macro) is an open-source framework for multi-sector capacity expansion modeling and analysis of macro-energy systems. It is written in Julia and uses the JuMP package to interface with a wide range of mathematical solvers. It enables researchers and practitioners to design and analyze energy and industrial systems that span electricity, fuels, bioenergy, steel, chemicals, and other sectors. The framework is organized around a small set of sector-agnostic components that can be combined into flexible graph structures, making it straightforward to extend to new technologies, policies, and commodities. Its companion packages support decomposition methods and other advanced techniques, allowing users to scale models across fine temporal and spatial resolutions. MacroEnergy.jl provides a versatile platform for studying energy transitions at the detail and scale demanded by modern research and policy.

physics.soc-ph↗

arXiv2025

Spotlighter: Revisiting Prompt Tuning from a Representative Mining View

CLIP's success has demonstrated that prompt tuning can achieve robust cross-modal semantic alignment for tasks ranging from open-domain recognition to fine-grained classification. However, redundant or weakly relevant feature components introduce noise and incur unnecessary computational costs. In this work, we propose Spotlighter, a lightweight token-selection framework that simultaneously enhances accuracy and efficiency in prompt tuning. Spotlighter evaluates each visual token's activation from both sample-wise and semantic-wise perspectives and retains only the top-scoring tokens for downstream prediction. A class-specific semantic memory bank of learned prototypes refines this selection, ensuring semantic representativeness and compensating for discarded features. To further prioritize informative signals, we introduce a two-level ranking mechanism that dynamically weights token--prototype interactions. Across 11 few-shot benchmarks, Spotlighter outperforms CLIP by up to 11.19\% in harmonic mean accuracy and achieves up to 0.8K additional FPS, with only 21 extra parameters. These results establish Spotlighter as an effective and scalable baseline for prompt tuning. Code for our method will be available at https://github.com/greatest-gourmet/Spotlighter.

cs.CV↗

arXiv2025

Gaussian Processes in Power Systems: Techniques, Applications, and Future Works

The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern power systems. Concurrently, the widespread deployment of smart meters, phasor measurement units (PMUs) and other sensors has generated vast spatiotemporal data streams, enabling advanced data-driven analytics and decision-making in grid operations. In this context, Gaussian processes (GPs) have emerged as a powerful probabilistic framework, offering uncertainty quantification, non-parametric modeling, and predictive capabilities to enhance power system analysis and control. This paper presents a comprehensive review of GP techniques and their applications in power system operation and control. GP applications are reviewed across three key domains: GP-based modeling, risk assessment, and optimization and control. These areas serve as representative examples of how GP can be utilized in power systems. Furthermore, critical challenges in GP applications are discussed, and potential research directions are outlined to facilitate future power system operations.

eess.SY↗

arXiv2025

Sectoral and spatial decomposition methods for multi-sector capacity expansion models

Multi-sector capacity expansion models play a crucial role in energy planning by providing decision support for policymaking in technology development. To ensure reliable support, these models require high technological, spatial, and temporal resolution, leading to large-scale linear programming problems that are often computationally intractable. To address this challenge, conventional approaches rely on simplifying abstractions that trade accuracy for computational efficiency. Benders decomposition has been widely explored to improve computational efficiency in electricity capacity expansion models. Specifically, state-of-the-art methods have primarily focused on improving performance through temporal decomposition. However, multi-sector models introduce additional complexity, requiring new decomposition strategies. In this work, we propose a budget-based formulation to extend decomposition to the sectoral and spatial domains. We test the developed sectoral and spatial Benders decomposition algorithms on case studies of the continental United States, considering different configurations in terms of spatial and temporal resolution. Results show that our algorithms achieve substantial performance improvement compared to existing decomposition algorithms, with runtime reductions within 15%-70%. The proposed methods leverage the generic structure of multi-sector capacity expansion models, and can thus be applied to most existing energy planning models, ensuring computational tractability without sacrificing resolution.

math.OC↗

arXiv2022

Gaussian Process-based Approach for Bilevel Optimization in the Power System -- A Critical Load Restoration Case

Bilevel optimization problems can be used to represent the collaborative interaction between a power system and grid-connected entities, called the followers, such as data centers. Most existing approaches assume that such followers' response behaviors are made available to the power system in the operation decision-making, which may be untenable in reality. This work presents a novel idea of solving bilevel optimization problems without assuming power systems' omniscience. The followers' responses will be represented by a function of the power system's decisions using Gaussian Process Regression. Then the two layers in the bilevel problem can be solved separately by the power system and its followers. This not only avoids the omniscience assumption, but also significantly increases the computational efficiency without compromising accuracy, especially for the problems with a complex lower layer. Moreover, a bilevel critical load restoration model is developed to test the proposed technique. Compared to the conventional methods, the proposed restoration model considers the load-side operation and the varying load marginal value, and can accurately estimate load-side loss and achieve better restoration solutions. Two case studies validate the advantages of the proposed approaches from different perspectives.

eess.SY↗

arXiv2020

A Framework for Health-informed RUL-constrained Optimal Power Flow with Li-ion Batteries

Battery energy storage systems are widely adopted in grid-connected applications to mitigate the impact of intermittent renewable generations and enhance power system resiliency. Degradation of the battery during its service time is one of the major concerns in the deployment that strongly affects the long-term lifetime. Apart from environmental factors, this intrinsic property of a battery depends on the daily operating conditions. Thus, optimally engaging the daily operation of the battery based on its current status in order to meet the required remaining useful life becomes a practical and demanding need. To address this issue, this paper proposes a health-informed RUL-constrained optimal power flow framework to characterize the corresponding optimal feasible operation space. The targeted service lifespan is achieved if the battery's working condition is confined within this feasible domain. Equivalent box constraints are then constructed for better computational efficiency in solving the optimization problem. In this framework, a Monte Carlo-based data-driven approach and a health indicator (HI) representing the battery's current states are introduced. The performance of the proposed method is illustrated with the IEEE 39-bus system.

eess.SY↗