Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch-and-Bound, Branch-and-Cut, dynamic programming and mixed-integer linear programming (MILP), remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to initial conditions, and can become prohibitively time-consuming for large-scale systems. In this paper, we introduce a quantum-classical hybrid algorithm for UC, and other resource scheduling problems by extension, that leverages Benders decomposition to decouple binary commitment decisions from continuous economic dispatch. The binary “master problem” is formulated as a quadratic unconstrained binary optimization (QUBO) model and solved on a quantum annealer. The continuous “subproblem,” which minimizes generation costs, with Lagrangian cuts feeding back to the master until convergence. We evaluate our hybrid framework on systems scaled from 10 to 1000 generation units. Compared against a classical mixed-integer nonlinear programming (MINLP) baseline, the hybrid algorithm achieves a consistently lower computation-time growth rate and maintains an absolute optimality gap below 1.63%. These results demonstrate that integrating quantum annealing within a hybrid quantum-classical Benders decomposition loop can significantly accelerate large-scale resource scheduling without sacrificing solution quality, pointing toward a viable path for addressing the escalating complexity of modern power grids.
The rapid growth of hyperscale data centers, driven by rising demand for artificial intelligence workloads, presents new challenges for grid reliability, resiliency, and clean-energy goals. These facilities impose significantly higher and more complex demands on power systems, with AI-related workloads causing fast, millisecond-scale load fluctuations known as pulsating loads. Such behavior can strain the bulk power system, complicating energy management, interconnection, and local generation planning. To address this problem, this paper investigated the use of energy storage systems to manage these high-frequency load dynamics while maintaining continuous data center operation. Through a detailed analysis of AI load characteristics, this paper presents an electromagnetic transient study evaluating the performance of a grid-forming battery energy storage system for data center applications in both grid-connected and islanded modes, including transitions between these operating states. The proposed approach treats the battery energy storage system as a medium-voltage, line-interactive uninterruptible power supply while simultaneously smoothing AI-driven load transients at the point of interconnection. Results show that the proposed architecture can significantly attenuate fast power fluctuations, improve voltage and power quality at the grid interface, and enhance overall data center reliability.
Autonomous coding agents are generating code at an unprecedented scale, with OpenAI Codex alone creating over 400,000 pull requests (PRs) in two months. As agentic PR volumes increase, code review agents (CRAs) have become routine gatekeepers in development workflows. Industry reports claim that CRAs can manage 80
Although microgrids are a prevalent research subject, design objectives of actual projects pose significant uncertainties for planning engineers. Typically, real microgrid systems, unlike those created solely for demonstration, aim primarily to enhance reliability for customers within the microgrid boundary and ensure resilience against prolonged outages caused by high-impact-low-probability events. A secondary goal often includes reducing fuel consumption during normal and emergency operations. Stability and reliable operation are imperative in both standalone and networked microgrids, where multiple microgrids form a cluster functioning independently or as a system. Design considerations extend beyond planning to include operational modes, clustering strategies, smooth transition and continuous operation in both grid-tied and off-grid conditions. An overlooked aspect is the design of protection and grounding strategies, which represent great concern for safe and reliable operation of the microgrid system. This paper presents an assessment of resource choices for microgrid equipment in Puerto Rico and their implications for protection system requirements.
AI coding agents are increasingly integrated into modern software engineering workflows, actively collaborating with human developers to create pull requests (PRs) in open-source repositories. Although coding agents improve developer productivity, they often generate code with more bugs and security issues than human-authored code. While human-authored PRs often break backward compatibility, leading to breaking changes, the potential for agentic PRs to introduce breaking changes remains underexplored. The goal of this paper is to help developers and researchers evaluate the reliability of AI-generated PRs by examining the frequency and task contexts in which AI agents introduce breaking changes. We conduct a comparative analysis of 7,191 agent-generated PRs with 1402 human-authored PRs from Python repositories in the AIDev dataset. We develop a tool that analyzes code changes in commits corresponding to the agentic PRs and leverages an abstract syntax tree (AST) based analysis to detect potential breaking changes. Our findings show that AI agents introduce fewer breaking changes overall than humans (3.45
