arXiv subjects

Samim Konjicija

Publications and source records attributed to Samim Konjicija.

4 recordsLinked to original sources

arXiv2026

Integrating Agentic Artificial Intelligence with High-Performance Computing for Grid Planning

We present AgentiGrid, an agentic artificial intelligence (AI) framework that integrates large language models (LLMs) intelligence and high-performance computing (HPC) to streamline and accelerate the multi-scenario power flow studies. AgentiGrid is an autonomous decision-making agent that proposes parameter modifications, invokes analyses through HPC analysis toolkit ExaGO, interprets results, and determines subsequent actions. ExaGO provides multiple power flow applications that can perform deterministic, stochastic and security constrained optimal power flow analyses. AgentiGrid provides backends to multiple LLMs (OpenAI, Anthropic, Ollama, and Ollama cloud) augmented with context specific and task specific prompts. Key features include interactive mid-search steering, goal-type-aware post-search analysis, and concurrent variant exploration for power flow optimization. A Streamlit-based graphical launcher provides real-time visualization of iteration progress and generates reports in natural language. AgentiGrid is capable of autonomously converging transmission constrained alternating current optimal power flow in under 20 iterations, with near-perfect reliability

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arXiv2026

Agentic Artificial Intelligence for Power Systems: Strategies to Identify and Close Capability Gaps

The rapid expansion of AI-driven information infrastructure, particularly data centers, is placing unprecedented pressure on power systems and accelerating the pace at which new assets must interconnect with the grid. As bulk transmission expansion rolls out slowly, new loads and generation are increasingly deployed within existing network constraints. Agentic AI is urgently needed to automate the numerous and repetitive connection processes, but its maturity has not been systematically validated on complex tasks and large-scale systems. We replicate the current state of the art in agentic AI for power systems planning and evaluate it against a structured suite of nodal planning problems spanning six levels of task complexity and four grid scales. We find that only the two lowest complexity levels are solvable on some of the test grid sizes, and identify the specific capability upgrades required to close this gap. Adopting stricter testing protocols and reproducible evaluation benchmarks is essential for assessing both genuine progress and the operational readiness of agentic AI.

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arXiv2026

HPC-Enabled Generator Importance Assessment for RTO-Scale Resource Adequacy Planning

Modern power systems are increasingly under stress as aging assets approach retirement and load growth outpaces new generation construction. The severity of this challenge varies by region: in the EU, the transmission grid can partially compensate for local generation shortfalls, while in the US, generation tends to be more localized, making retirements harder to offset. Retirement of generation has consequences for system reserves, fuel supply chain, and public health. We present an high-performance computing (HPC) framework for rapidly assessing the grid importance of individual generating units and ranking them by primary fuel type, operating cost, or grid impact. Historically, such studies were computationally intensive and therefore conducted infrequently. This work demonstrates that such assessments can be completed in minutes, enabling planners to evaluate a much broader range of generation portfolio scenarios than was previously possible.

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arXiv2025

Investigation of lightning effects on solar power plants connected to transmission networks

The increasing integration of solar power plants into transmission grids has raised concerns about their vulnerability to disturbances, particularly lightning strokes. Solar energy, while offering significant environmental and economic benefits, faces challenges when connected to transmission lines that are prone to lightning discharges. This paper investigates the impact of lightning events on solar power plants, focusing on overvoltage effects. Lightning stroke simulations were conducted at various distances from the solar power plant along the transmission line, considering scenarios with and without surge arrester. Key lightning parameters such as peak current, front time, and tail time were varied to simulate different lightning strokes. The study also includes a Fourier transform analysis of the resulting overvoltages with and without a surge arrester, along with the Hilbert marginal spectrum of these overvoltages. The results provide insights into the effectiveness of surge arresters in mitigating lightning overvoltages and highlight the importance of proper protective measures for enhancing the reliability and safety of solar power plants connected to transmission networks.

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