arXiv subjects

Emmanuel O. Badmus

Publications and source records attributed to Emmanuel O. Badmus.

4 recordsLinked to original sources

arXiv2026

PowerDAG: Supervisory Agentic AI System for Automating Distribution Grid Analysis

Distribution grid analyses include tasks such as network information retrieval, power-flow analysis, hosting-capacity assessment, DER planning, and state estimation. Completing these tasks often requires long-horizon, stateful workflows in which an engineer retrieves data, loads a feeder, runs simulations, evaluates results, and exports outputs. The growing volume of these analyses is outpacing the limited engineering workforce, causing suboptimal outcomes and delays. Large Language Model (LLM)-orchestrated agents can help, but they often struggle for two reasons: (i) they lack algorithms to determine the right context for an unseen grid task, and (ii) they cannot verify proposed actions against the environment state beforehand and instead rely on feedback after execution. We propose PowerDAG, an agentic artificial intelligence (AI) system that formalizes workflows as directed acyclic graphs (DAGs) and addresses current gaps in this formalism through two mechanisms, adaptive retrieval and Just-in-Time supervision. To dynamically retrieve relevant context, it curates and ranks expert exemplars using an adaptive score-decay cutoff that matches the query complexity. For supervision, it evaluates prerequisites before every tool call. If an agent proposes an invalid action, the supervisor blocks execution, preserves the environment, and returns a corrective advisory. We evaluate PowerDAG on 150 held-out queries from a 200-record expert-verified benchmark that covers 10 of the most commonly performed distribution-grid analyses, comparing 6 agentic systems across 10 LLMs for a total of 9,000 runs. PowerDAG reaches a success rate of 98.0% with GPT-5.5, 97.3% with Gemini 3.1 Pro, and 92.7% with Qwen3.6-27B, improving success rates by 6 to 50 percentage points over baselines.

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arXiv2026

Two-Stage Bidirectional Inverter Equivalent Circuit Model for Distribution Grid Steady-State Analysis and Optimization

This paper presents a \textit{physics-based} steady-state equivalent circuit model of a two-stage bidirectional inverter. These inverters connect distributed energy resources (DERs), such as photovoltaic (PV) and battery systems, to distribution grids. Existing inverter models have technical gaps on three fronts: i) inadequate modeling of inverter losses; ii) use of mathematical abstractions for bidirectional flow of power; and iii) inability to integrate different control modes into nonlinear solvers without loss of generality. We propose a physics-first model that explicitly captures losses in passive circuit components based on circuit-level principles. We enable bidirectional power flow without binary or complementarity constraints by formulating loss terms as smooth, sign-aware expressions of current. We introduce and parameterize controlled current sources with twice-differentiable continuous functions to enable inverter control modes without loss of generality. We integrate DERs with the proposed inverter model at the load buses of distribution networks to perform power flow and optimization studies on real-world distribution networks with over 20,000 nodes. We demonstrate that the proposed model is more accurate, integrates seamlessly with various control modes without loss of generality, and scales robustly to large optimization problems. Index Terms: bidirectional inverter model, circuit-based modeling, DERs, inverter efficiency, power control, steady-state analysis.

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arXiv2025

PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses

Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.

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arXiv2024

ANOCA: AC Network-aware Optimal Curtailment Approach for Dynamic Hosting Capacity

With exponential growth in distributed energy resources (DERs) coupled with at-capacity distribution grid infrastructure, prosumers cannot always export all extra power to the grid without violating technical limits. Consequently, a slew of dynamic hosting capacity (DHC) algorithms have emerged for optimal utilization of grid infrastructure while maximizing export from DERs. Most of these DHC algorithms utilize the concept of operating envelopes (OE), where the utility gives prosumers technical power export limits, and they are free to export power within these limits. Recent studies have shown that OE-based frameworks have drawbacks, as most develop power export limits based on convex or linear grid models. As OEs must capture extreme operating conditions, both convex and linear models can violate technical limits in practice because they approximate grid physics. However, AC models are unsuitable because they may not be feasible within the whole region of OE. We propose a new two-stage optimization framework for DHC built on three-phase AC models to address the current gaps. In this approach, the prosumers first run a receding horizon multi-period optimization to identify optimal export power setpoints to communicate with the utility. The utility then performs an infeasibility-based optimization to either accept the prosumer's request or dispatch an optimal curtail signal such that overall system technical constraints are not violated. To explore various curtailment strategies, we develop an L1, L2, and Linf norm-based dispatch algorithm with an exact three-phase AC model. We test our framework on a 1420 three-phase node meshed distribution network and show that the proposed algorithm optimally curtails DERs while guaranteeing the AC feasibility of the network.

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