arXiv subjects

Amritanshu Pandey

Publications and source records attributed to Amritanshu Pandey.

At least 37 records · Page 2Linked to original sources

arXiv2024

A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model

An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-attack, can often result in topology errors. To maintain situation awareness under the possible topology errors and bad data, this paper develops ckt-GSE, a circuit-theoretic generalized state estimation method using node-breaker (NB) model. Ckt- GSE is a convex and scalable model that jointly estimates AC state variables and network topology, with robustness against different data errors. The method first constructs an equivalent circuit representation of the AC power grid by developing and aggregating linear circuit models of SCADA meters, phasor measurement units(PMUs), and switching devices. Then based on this circuit, ckt-GSE defines a constrained optimization problem using weighted least absolute value (WLAV) objective to form a robust estimator. The problem is a Linear Programming (LP) problem whose solution includes accurate AC states and a sparse vector of noise terms to identify topology errors and bad data.This paper is the first to explore a circuit-theoretic approach for an AC-network constrained GSE algorithm that is: 1) applicable to the real-world data setting, 2) convex without relaxation, scalable with our circuit-based solver; and 3) robust with the ability to identify and reject different data errors

eess.SY↗

arXiv2024

Unlearning Climate Misinformation in Large Language Models

Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A data for fine-tuning and evaluating LLMs on climate-related claims, we compare open-source models, assessing their ability to generate truthful responses to climate change questions. We investigate the detectability of models intentionally poisoned with false climate information, finding that such poisoning may not affect the accuracy of a model's responses in other domains. Furthermore, we compare the effectiveness of unlearning algorithms, fine-tuning, and Retrieval-Augmented Generation (RAG) for factually grounding LLMs on climate change topics. Our evaluation reveals that unlearning algorithms can be effective for nuanced conceptual claims, despite previous findings suggesting their inefficacy in privacy contexts. These insights aim to guide the development of more factually reliable LLMs and highlight the need for additional work to secure LLMs against misinformation attacks.

cs.CL↗

arXiv2024

Towards Energysheds: A Technical Definition and Cooperative Framework for Future Power System Operations

There is growing interest in understanding how interactions between system-wide objectives and local community decision-making will impact the clean energy transition. The concept of energysheds has gained traction in the areas of public policy and social science as a way to study these relationships. However, development of technical definitions of energysheds that permit system analysis are still largely missing. In this work, we propose a mathematical definition for energysheds, and introduce an analytical framework for studying energyshed concepts within the context of future electric power system operations. This framework is used to develop insights into the factors that impact a community's ability to achieve energyshed policy incentives within a larger connected power grid, as well as the tradeoffs associated with different spatial policy requirements. We also propose an optimization-based energyshed policy design problem, and show that it can be solved to global optimality within arbitrary precision by employing concepts from quasi-convex optimization. Finally, we investigate how interconnected energysheds can cooperatively achieve their objectives in bulk power system operations.

eess.SY↗

arXiv2024

Anomaly Detection in Power Grids via Context-Agnostic Learning

An important tool grid operators use to safeguard against failures, whether naturally occurring or malicious, involves detecting anomalies in the power system SCADA data. In this paper, we aim to solve a real-time anomaly detection problem. Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the network topology or measurement data? Existing methods, primarily optimization-based, mostly use only a single snapshot of the measurement values and do not scale well with the network size. Recent data-driven ML techniques have shown promise by using a combination of current and historical data for anomaly detection but generally do not consider physical attributes like the impact of topology or load/generation changes on sensor measurements and thus cannot accommodate regular context-variability in the historical data. To address this gap, we propose a novel context-aware anomaly detection algorithm, GridCAL, that considers the effect of regular topology and load/generation changes. This algorithm converts the real-time power flow measurements to context-agnostic values, which allows us to analyze measurement coming from different grid contexts in an aggregate fashion, enabling us to derive a unified statistical model that becomes the basis of anomaly detection. Through numerical simulations on networks up to 2383 nodes, we show that our approach is accurate, outperforming state-of-the-art approaches, and is computationally efficient.

cs.LG↗

arXiv2024

Contingency Analyses with Warm Starter using Probabilistic Graphical Model

Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operations. We propose to extend contingency analysis to include cyberthreat evaluations. However, unlike the traditional N-1 or N-2 contingencies, cyberthreats (e.g., MadIoT) require simulating hard-to-solve N-k (with k >> 2) contingencies in a practical amount of time. Purely physics-based power flow solvers, while being accurate, are slow and may not solve N-k contingencies in a timely manner, whereas the emerging data-driven alternatives are fast but not sufficiently generalizable, interpretable, and scalable. To address these challenges, we propose a novel conditional Gaussian Random Field-based data-driven method that performs fast and accurate evaluation of cyberthreats. It achieves speedup of contingency analysis by warm-starting simulations, i.e., improving starting points, for the physical solvers. To improve the physical interpretability and generalizability, the proposed method incorporates domain knowledge by considering the graphical nature of the grid topology. To improve scalability, the method applies physics-informed regularization that reduces model complexity. Experiments validate that simulating MadIoT-induced attacks with our warm starter becomes approximately 5x faster on a realistic 2000-bus system.

cs.CR↗

arXiv2024

Predictive Optimization of Hybrid Energy Systems with Temperature Dependency

Hybrid Energy Systems (HES), amalgamating renewable sources, energy storage, and conventional generation, have emerged as a responsive resource for providing valuable grid services. Subsequently, modeling and analysis of HES has become critical, and the quality of grid services hedges on it. Currently, most HES models are temperature-agnostic. However, the temperature-dependent factors can significantly impact HES performance, necessitating advanced modeling and optimization techniques. With the inclusion of temperature-dependent models, the challenges and complexity of solving optimization problem increases. In this paper, the electro-thermal modeling of HES is discussed. Based on this model, a nonlinear predictive optimization framework is formulated. A simplified model is developed to address the challenges associated with solving nonlinear problems. Further, projection and homotopy approaches are proposed. In the homotopy method, the NLP is solved by incrementally changing the C-rating of the battery. Simulation-based analysis of the algorithms highlights the effects of different battery ratings, ambient temperatures, and energy price variations. Finally, comparative assessments with a temperature-agnostic approach illustrates the effectiveness of electro-thermal methods in optimizing HES.

math.OC↗

arXiv2024

Optimization-based Framework for Selecting Under-frequency Load Shedding Parameters

High penetration of renewable resources results in a power system with lower inertia and higher frequency sensitivity to power imbalances. Such systems are becoming increasingly susceptible to frequency collapse during extreme disturbances. Under-Frequency Load Shedding (UFLS) is a last-resort protection scheme and acts as an emergency brake by shedding load to arrest frequency decline. Current and emerging efforts to optimize UFLS settings and frequency thresholds are mostly network agnostic, ignoring network spatial information. With the prevalence of Distributed Energy Resources (DERs) in the high-renewable paradigm, the power grid is becoming more bidirectional, making some locations in the network less effective for UFLS action than others. This work proposes a Mixed Integer Linear Program that optimizes the UFLS setpoints (prioritizing one location over another) to minimize frequency deviation and load-shed for a given disturbance. The formulation considers system information and DER generation mix at different network locations, increasing model fidelity. The formulation also captures the discrete nature and practical time delays and deadbands associated with UFLS using a minimal set of binary variables, reducing problem complexity. We empirically validate the optimization approach on the dynamic IEEE 39-bus system for performance metrics, including frequency nadir, steady-state frequency and total load shed.

eess.SY↗

arXiv2023

Making Distribution State Estimation Practical: Challenges and Opportunities

In increasingly digitalized and metered distribution networks, state estimation is generally recognized as a key enabler of advanced network management functionalities. However, despite decades of research, the real-life adoption of state estimation in distribution systems remains sporadic. This systematization of knowledge paper discusses the cause for this while comparing industrial and academic experiences and reviewing well- and less-established research directions. We argue that to make distribution system state estimation more practical and applicable in the field, new perspectives are needed. In particular, research should move away from conventional approaches and embrace generalized problem specifications and more comprehensive workflows. These, in turn, require algorithm advancements and more general mathematical formulations. We discuss lines of work to enable the delivery of tangible research.

math.OC↗

arXiv2023

Tutorial: Circuit-based Electromagnetic Transient Simulation

The growing penetration of inverter-based resources and associated controls necessitates system-wide electromagnetic transient (EMT) analyses. EMT tools and methods today were not designed for the scale of these analyses. In light of the emerging need, there is a great deal of interest in developing new techniques for fast and accurate EMT simulations for large power grids; the foundations of which will be built on current tools and methods. However, we find that educational texts covering the fundamentals and inner workings of current EMT tools are limited. As such, there is a lack of introductory material for students and professionals interested in researching the field. To that end, in this tutorial, we introduce the principles of EMT analyses from the circuit-theoretic viewpoint, mimicking how time-domain analyses are performed in circuit simulation tools like SPICE and Cadence. We perform EMT simulations for two examples, one linear and one nonlinear, including induction motor (IM) from the first principles. By the document's end, we anticipate the readers will have a \textit{basic} understanding of how power grid EMT tools work.

eess.SY↗

arXiv2023

Continuous Switch Model and Heuristics for Mixed-Integer Problems in Power Systems

Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints make this problem NP-hard. For large real-world networks, obtaining an AC feasible optimum solution for these problems is computationally challenging and often unattainable with state-of-the-art tools today. In this work, we map the MINLP decision problem into a set of equivalent circuits by representing binary variables with a circuit-based continuous switch model. We characterize the continuous switch model by a controlled nonlinear impedance that more closely mimics the physical behavior of a real-world switch. This mapping effectively transforms the MINLP problem into an NLP problem. We mathematically show that this transformation is a tight relaxation of the MINLP problem. For fast and robust convergence, we develop physics-driven homotopy and Newton-Raphson damping methods. To validate this approach, we empirically show robust convergences for large, realistic systems ($>$ 70,000 buses) in a practical wall-clock time to an AC-feasible optimum. We compare our results and show improvement over industry-standard tools and other binary relaxation methods.

eess.SY↗

arXiv2023

Actionable Three-Phase Infeasibility Optimization with Varying Slack Sources

Modern distribution grids that include numerous distributed energy resources (DERs) and battery electric vehicles (BEVs) will require simulation and optimization methods that can capture behavior under infeasible operating scenarios to assess reliability. A three-phase infeasibility analysis (TPIA) localizes and identifies power deficient areas in distribution feeders via a non-convex optimization that injects and subsequently minimizes slack sources, subject to AC network constraints. In this paper, we extend the TPIA framework by introducing operational bounds to ensure realistic, actionable solutions. We incorporate current, reactive power, and susceptance slack sources to model real-world assets, and discuss their potential use cases. We show that the voltage-bounded TPIA formulations provide actionable solutions for realistic networks of up to 5360 nodes where power flow simulations either fail or return low-voltage solutions. We demonstrate reactive power compensation using the slack susceptance formulation on an infeasible test case.

math.OC↗

arXiv2022

A Risk-Managed Steady-State Analysis to Assess the Impact of Power Grid Uncertainties

Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability. Existing steady-state methods for assessing system-level operability and reliability are primarily deterministic, therefore, ill-suited to capture randomness and variability. This work introduces a probabilistic steady-state analysis inspired by statistical worst-case circuit analysis to evaluate the risk of operational violations due to stochastic resources. Compared to parallelized Monte Carlo analyses (MCS), we have seen up to 24x improvement in runtime speed using our approach without significant loss of probabilistic accuracy for a Texas7k low-wind day test system.

eess.SY↗

arXiv2022

Towards Practical Physics-Informed ML Design and Evaluation for Power Grid

When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interpretability. To address these challenges for power grids, many recent works have explored the inclusion of grid physics (i.e., domain expertise) into their method design, primarily through including system constraints and technical limits, reducing search space and defining meaningful features in latent space. Yet, there is no general methodology to evaluate the practicality of these approaches in power grid tasks, and limitations exist regarding scalability, generalization, interpretability, etc. This work formalizes a new concept of physical interpretability which assesses how a ML model makes predictions in a physically meaningful way and introduces an evaluation methodology that identifies a set of attributes that a practical method should satisfy. Inspired by the evaluation attributes, the paper further develops a novel contingency analysis warm starter for MadIoT cyberattack, based on a conditional Gaussian random field. This method serves as an instance of an ML model that can incorporate diverse domain knowledge and improve on these identified attributes. Experiments validate that the warm starter significantly boosts the efficiency of contingency analysis for MadIoT attack even with shallow NN architectures.

cs.LG↗

arXiv2022

Three-Phase Infeasibility Analysis for Distribution Grid Studies

With the increase of distributed energy resources in the distribution grid, planning to ensure sufficient infrastructure and resources becomes critical. Planning at the distribution level is limited by the complexities of optimizing unbalanced systems. In this paper we develop a three-phase infeasibility analysis that identifies weak locations in a distribution network. This optimization is formulated by adding slack current sources at nodes in the system and minimizing their norm subject to distribution power flow constraints. Through this analysis we solve instances of power flow that would otherwise be infeasible and diverge. Under conditions when power flow is feasible, our approach is equivalent to standard three-phase power flow; however, for cases where power flow fails, the nonzero slack injection currents compensate for missing power to make the grid feasible. Since an uncountable number of injected currents can provide feasibility, we further explore the optimization formulation that best fits the solution objective through use of both a least squares and an L1 norm objective. Our L1 norm formulation localizes power deficient locations through its inherent sparsity. We show the efficacy of this approach on realistic unbalanced testcases up to 8500 nodes and for a scenario with a high penetration of electric vehicles.

math.OC↗

arXiv2022

Circuit-theoretic Line Outage Distribution Factor

This work presents the design of AC line outage distribution factor created from the circuit-theoretic power flow models. Experiment results are shown to demonstrate its efficacy in quantifying the impact of line outages on the grid, and its resulting potential for fast contingency screening.

eess.SY↗

arXiv2022

Practical Adoption of Cloud Computing in Power Systems- Drivers, Challenges, Guidance, and Real-world Use Cases

Motivated by The Federal Energy Regulatory Commission's (FERC) recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Force's perspective, after extensive review and deliberation by its members, including grid operators, utility companies, software vendors, and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of the challenges and risks of migrating power grid utility workloads to the cloud. Next, for each corresponding challenge or risk, the paper provides appropriate guidance. Notably, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look toward to design and select their own future cloud solutions. We hope that the information in this paper will serve as helpful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.

eess.SY↗

arXiv2021

Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose DYNWATCH, a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20% or more (F-measure) in experiments; and fast, running in less than 1.7ms on average per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly in the size of the graph.

cs.LG↗

arXiv2021

Two-Stage Homotopy Method to Incorporate Discrete Control Variables into AC-OPF

Alternating-Current Optimal Power Flow (AC-OPF) is an optimization problem critical for planning and operating the power grid. The problem is traditionally formulated using only continuous variables. Typically, control devices with discrete-valued settings, which provide valuable flexibility to the network and improve resilience, are omitted from AC-OPF formulations due to the difficulty of integrality constraints. We propose a two-stage homotopy algorithm to solve the AC-OPF problem with discrete-valued control settings. This method does not rely on prior knowledge of control settings or other initial conditions. The first stage relaxes the discrete settings to continuous variables and solves the optimization using a robust homotopy technique. Once the solution has been obtained using relaxed models, second homotopy problem gradually transforms the relaxed settings to their nearest feasible discrete values. We test the proposed algorithm on several large networks with switched shunts and adjustable transformers and show it can outperform a similar state-of-the-art solver.

math.OC↗