arXiv subjects

Anamika Dubey

Publications and source records attributed to Anamika Dubey.

At least 19 recordsLinked to original sources

arXiv2025

Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning

The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learning approaches to quantitatively model the relationship between weather parameters and power system resilience metrics. By leveraging real-world publicly available outage and weather data, we identify key weather variables of wind speed, temperature, and precipitation influencing a particular region's resilience metrics. A case study of Cook County, Illinois, and Miami-Dade County, Florida, reveals that these weather parameters are critical factors in resiliency analysis and risk assessment. Additionally, we find that these weather variables have combined effects when studied jointly compared to their effects in isolation. This framework provides valuable insights for understanding how weather events affect power distribution system performance, supporting decision-makers in developing more effective strategies for risk mitigation, resource allocation, and adaptation to changing climatic conditions.

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arXiv2025

House Thermal Model Estimation: Robustness Across Seasons and Setpoints

Achieving the flexibility from house heating, cooling, and ventilation systems (HVAC) has the potential to enable large-scale demand response by aggregating HVAC load adjustments across many homes. This demand response strategy helps distribution grid to flexibly ramp-up or ramp-down local load demand so that it can optimally match the bulk power system generation profile. However, achieving this capability requires house thermal models that are both computationally efficient and robust to operating conditions. In this work, parameters of the Resistance-Capacitance (RC) network thermal model for houses are estimated using three optimization algorithms: Nonlinear Least Squares (NLS), Batch Estimation (BE), and Maximum Likelihood Estimation (MLE). The resulting models are evaluated through a Forward-Simulation across four different seasons and three setpoints. The results illustrate a principled way of selecting reduced order models and estimation methods with respect to the robustness offered to seasonal and setpoint variations in training-testing datasets

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arXiv2025

Scalable Two-Stage Stochastic Optimal Power Flow via Separable Approximation

This paper proposes a Separable Projective Approximation Routine-Optimal Power Flow (SPAR-OPF) framework for solving two-stage stochastic optimization problems in power systems. The framework utilizes a separable piecewise linear approximation of the value function and learns the function based on sample sub-gradient information. We present two formulations to model the learned value function, and compare their effectiveness. Additionally, an efficient statistical method is introduced to assess the quality of the obtained solutions. The effectiveness of the proposed framework is validated using distributed generation siting and sizing problem in three-phase unbalanced power distribution systems as an example. Results show that the framework approximates the value function with over 98% accuracy and provides high-quality solutions with an optimality gap of less than 1%. The framework scales efficiently with system size, generating high-quality solutions in a short time when applied to a 9500-node distribution system with 1200 scenarios, while the extensive formulations and progressive hedging failed to solve the problem.

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arXiv2025

Stochastic Economic Dispatch with Battery Energy Storage considering Wind and Load Uncertainty

With the integration of renewable energy resources in power systems, managing operational flexibility and reliability while minimizing operational costs has become increasingly challenging. Battery energy storage system (BESS) offers a promising solution to address these issues. This paper presents a stochastic dynamic economic dispatch with storage (SDED-S) framework to assess the impact of BESS in managing uncertainty. The temporal correlation between wind and load uncertainties is captured, with scenarios generated using a method inspired by stratified and importance sampling. The proposed approach is demonstrated on a modified IEEE 39-bus system, where selected conventional generators are converted to wind power plants. Case studies show that strategic BESS deployment significantly improves system flexibility by reducing renewable curtailments and dispatch costs. Renewable energy curtailments decrease upon increasing BESS size and approach zero depending on wind penetration level. Higher wind penetrations result in greater curtailments without storage and yield larger cost savings when BESS is deployed, highlighting the growing need for flexibility as renewable energy penetrations increase.

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arXiv2025

Smart Residential Community Simulator for Developing and Benchmarking Energy Management Systems

Home Energy Management Systems (HEMS) are being actively developed for both individual houses and communities to support demand response in on-grid operation, and ensure resilience during off-grid scenarios. However, most simulators used for closed-loop HEMS testing are tailored to a specific distributed energy resource (DER) configuration with a fixed number of houses, limiting flexibility and scalability. This leads to additional development efforts to support diverse DER configurations across any number of houses and to integrate appropriate weather and load data pipelines. To address these limitations, we present a scalable simulator capable of modeling any number of houses in both on-grid and off-grid modes as a Gymnasium environment. Each house can have a unique DER configuration - Rooftop Solar Photovoltaics (PV), Battery-only, PV-only, or no DER - and includes models for air-conditioning and eight grouped circuit-level loads. The simulator integrates National Solar Radiation Database (NSRDB) weather and Pecan Street load datasets, supports three default controllers (two for off-grid, and one for on-grid scenarios), and includes performance metrics and visualization tools. We demonstrate its flexibility through simulations on individual houses and a four-house community with heterogeneous DERs, benchmarking the controllers across built-in metrics and computation time. The results highlight the simulator's capability to systematically evaluate control policy performance under varying system configurations.

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arXiv2025

Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications

We need computationally efficient and accurate building thermal dynamics models for use in grid-edge applications. This work evaluates two grey-box approaches for modeling building thermal dynamics: RC-network models and structured regression models. For RC-network models, we compare parameter estimation methods including Nonlinear Least Squares, Batch Estimation, and Maximum Likelihood Estimation. We use the Almon Lag Structure with Linear Least Squares for estimating the structured regression models. The performance of these models and methods is evaluated on simulated house and commercial building data across three different simulation types.

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arXiv2025

An Open-Source Simulation and Data Management Tool for EnergyPlus Building Models

We present a new open-source, GUI-based application created using Plotly-Dash, along with an integrated PostgreSQL-based relational database, developed to streamline EnergyPlus building model simulation workflows. The application facilitates data generation, aggregation (across thermal zones), and visualization based on customizable user preferences, while the database efficiently stores and retrieves complex simulation data generated by EnergyPlus. We demonstrate the need for this application and database, emphasizing how existing approaches for generating, managing, and analyzing EnergyPlus simulation data can be cumbersome, particularly when handling a large number of building models with varying simulation setups. This integrated framework enables building energy engineers and researchers to simplify their EnergyPlus simulations, manage generated simulation data, perform data analyses, and support data-driven modeling tasks.

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arXiv2025

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes

Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and robustness compared to model-free methods. However, effective model learning requires a learning-based approximator for the underlying power flow model. This study extends existing work by introducing a data-driven power flow method based on Gaussian Processes (GPs) to approximate the multiphase power flow model, by mapping net load injections to nodal voltages. Simulation results using the IEEE 123-bus and 8500-node distribution test feeders demonstrate that the trained GP model can reliably predict the nonlinear power flow solutions with minimal training data. We also conduct a comparative analysis of the training efficiency and testing performance of the proposed GP-based power flow approximator against a deep neural network-based approximator, highlighting the advantages of our data-efficient approach. Results over realistic operating conditions show that despite an 85% reduction in the training sample size (corresponding to a 92.8% improvement in training time), GP models produce a 99.9% relative reduction in mean absolute error compared to the baselines of deep neural networks.

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arXiv2024

Model Predictive Control based Energy Management System for Home Energy Resiliency

As the occurrence of extreme weather events is increasing so are the outages caused by them. During such unplanned outages, a house needs to be provided with an energy supply to maintain habitable conditions by maintaining thermal comfort and servicing at least critical loads. An energy system consisting of rooftop photovoltaic (PV) panels along with battery storage is an excellent carbon-free choice to provide energy resiliency to houses against extreme weather-related outages. However, to provide habitable conditions this energy system has to provide not only for the non-air-conditioning (non-AC) load demand but also for the turning on of the AC system which has a considerably higher startup power requirement as compared to its rated power. Hence, an intelligent automated decision-making controller is needed which can manage the trade-off between competing requirements. In this paper, we propose such an intelligent controller based on Model Predictive Control (MPC). We compare its performance with a Baseline controller which is unintelligent, and a Rule-Based controller which has some intelligence, based on three resiliency metrics that we have developed. We perform extensive simulations for numerous scenarios involving different energy system sizes and AC startup power requirements. Every simulation is one week long and is carried out for a single-family detached house located in Florida in the aftermath of Hurricane Irma in 2017. The simulation results show that the MPC controller performs better than the other controllers in the more energy-constrained scenarios (smaller PV-battery size, larger AC startup power requirement) in providing both thermal comfort and servicing non-AC loads in a balanced manner.

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arXiv2024

Small-Signal Dynamics of Lossy Inverter-Based Microgrids for Generalized Droop Controls

A network-level small-signal model is developed for lossy microgrids, which considers coupled angle and voltage dynamics of inverter-based microgrids and uses a more general framework of droop controls in the inverter. It is shown that when relative resistances of the lines in the microgrid are reasonably consistent and differences of voltage angles across the lines are small at the operating point, the generalized droop controls can be designed to enforce decoupling between angle dynamics and voltage dynamics. Next, structural results for the asymptotic stability of small-signal angle and voltage dynamics are given for the case when generalized droop control achieves decoupling. Simulated transient responses of a modified IEEE 9-bus system are presented to validate the theoretical findings which show the effectiveness of generalized droop controls in independently shaping the settling times of the angle and voltage responses of the lossy microgrid system.

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arXiv2023

Resilience-driven Planning of Electric Power Systems Against Extreme Weather Events

With the increasing frequency of natural disasters, operators must prioritize improvements in the existing electric power grid infrastructure to enhance the resilience of the grid. Resilience to extreme weather events necessitates lowering the impacts of high-impact, low-probability (HILP) events, which is only possible when such events are considered during the planning stage. This paper proposes a two-stage stochastic planning model where the generation dispatch, line hardening, line capacity expansion, and distributed generation sizing and siting decisions are proactively decided to minimize the overall load shed and its risk for extreme weather scenarios, where the risk is modeled using conditional value-at-risk. To alleviate computational complexity without sacrificing solution quality, a representative scenario sampling method is used. Finally, the overall framework is tested on a standard IEEE reliability test system to evaluate the effectiveness of the proposed approach. Several planning portfolios are presented that can help system planners identify trade-offs between system resilience, planning budget, and risk aversion.

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arXiv2023

Hurricane and Storm Surges-Induced Power System Vulnerabilities and their Socioeconomic Impact

This paper introduces a probabilistic framework to quantify community vulnerability towards power losses due to extreme weather events. To analyze the impact of weather events on the power grid, the wind fields of historical hurricanes from 2000 to 2018 on the Texas coast are modeled using their available parameters, and probabilistic storm surge scenarios are constructed utilizing the hurricane characteristics. The vulnerability of hurricanes and storm surges is evaluated on a 2000 bus synthetic power grid model on the geographical footprint of Texas. The load losses, obtained via branch and substation outages, are then geographically represented at the county level and integrated with the publicly available Social Vulnerability Index to evaluate the Integrated Community Vulnerability Index (ICVI), which reflects the impacts of these extreme weather events on the socioeconomic and community power systems. The analysis concludes that the compounded impact of power outages due to extreme weather events can amplify the vulnerability of affected communities. Such analysis can help the system planners and operators make an informed decision.

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arXiv2023

Resilience assessment and planning in power distribution systems:Past and future considerations

Over the past decade, extreme weather events have significantly increased worldwide, leading to widespread power outages and blackouts. As these threats continue to challenge power distribution systems, the importance of mitigating the impacts of extreme weather events has become paramount. Consequently, resilience has become crucial for designing and operating power distribution systems. This work comprehensively explores the current landscape of resilience evaluation and metrics within the power distribution system domain, reviewing existing methods and identifying key attributes that define effective resilience metrics. The challenges encountered during the formulation, development, and calculation of these metrics are also addressed. Additionally, this review acknowledges the intricate interdependencies between power distribution systems and critical infrastructures, including information and communication technology, transportation, water distribution, and natural gas networks. It is important to understand these interdependencies and their impact on power distribution system resilience. Moreover, this work provides an in-depth analysis of existing research on planning solutions to enhance distribution system resilience and support power distribution system operators and planners in developing effective mitigation strategies. These strategies are crucial for minimizing the adverse impacts of extreme weather events and fostering overall resilience within power distribution systems.

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arXiv2023

Convergence Guarantees of a Distributed Network Equivalence Algorithm for Distribution-OPF

The massive integration of distributed energy resources changes the operational demands of the electric power distribution system, motivating optimization-based approaches. The added computational complexities of the resulting optimal power flow (OPF) problem have generally been managed by approximated or relaxed models; however, they may lead to infeasible or inaccurate solutions. Decomposition-based methods have also been used to solve the OPF problems. But the existing methods require several message passing rounds for relatively small systems, causing significant delays in decision making; related feedback-based methods also suffer from slow tracking of the optimal solutions. In this paper, we propose a provably convergent distributed algorithm to solve the nonlinear OPF problem for power distribution systems. Our method is based on a previously developed decomposition-based optimization method that employs the network equivalence method. We present a thorough mathematical analysis that includes sufficient conditions that guarantee convergence of the method. We also present simulation results using the IEEE-123 bus test system to demonstrate the algorithm's effectiveness and provide additional insights into theoretical results.

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arXiv2022

Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems

Distributed optimization methods have been extensively applied for the optimization of electric power distribution systems, especially for grid-edge coordination. Existing distributed optimization algorithms applied to power distribution systems require many communication rounds among the distributed agents and may pose convergence challenges in difficult nonlinear settings. The communication network parameters also significantly impact the algorithm's performance. In this paper, we propose a scalable, equivalent network approximation-based, distributed optimization algorithm that employs simulation within optimization using the system's digital twin (DT) to solve the optimal power problems (OPF) for a three-phase unbalanced distribution system. The proposed approach is implemented using a cyber-physical co-simulation platform to validate the robustness of the proposed distributed algorithm under stressed communication. The proposed approach is thoroughly validated using the IEEE 123-bus test system.

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arXiv2022

Sensitivity Analyses of Resilience-oriented Risk-averse Active Distribution Systems Planning

This paper presents sensitivity analyses of resilience-based active distribution system planning solutions with respect to different parameters. The distribution system planning problem is formulated as a two-stage risk-averse stochastic optimization model with conditional value-at-risk (CVaR) as the risk measure. The probabilistic scenarios are obtained using regional wind profiles, and Monte Carlo simulations are conducted to obtain failure scenarios based on component fragility models. The planning measure includes advanced distribution grid operations with intentional islanding measures. The three main parameters used in this work for sensitivity analysis are the number of scenarios, risk preference, and planning budget allocation. Such analysis can provide additional information to system operators on dispatching the planning budget and available resources properly to enhance the grid's resilience.

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arXiv2022

Spatiotemporal Impact Analysis of Hurricanes and Storm Surges on Power Systems

This paper develops a spatiotemporal probabilistic impact assessment framework to analyze and quantify the compounding effect of hurricanes and storm surges on the bulk power grid. The probabilistic synthetic hurricane tracks are generated using historical hurricane data, and storm surge scenarios are generated based on observed hurricane parameters. The system losses are modeled using a loss metric that quantifies the total load loss. The overall simulation is performed on the synthetic Texas 2000-bus system mapped on the geographical footprint of Texas. The results show that power substation inundation due to storm surge creates additional load losses as the hurricane traverses inland.

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arXiv2022

Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources

Solving the non-convex optimal power flow (OPF) problem for large-scale power distribution systems is computationally expensive. An alternative is to solve the relaxed convex problem or linear approximated problem, but these methods lead to sub-optimal or power flow infeasible solutions. In this paper, we propose a fast method to solve the OPF problem using distributed computing algorithms combined with a decomposition technique. The full network-level OPF problem is decomposed into multiple smaller sub-problems defined for each decomposed area or node that can be easily solved using off-the-shelf nonlinear programming (NLP) solvers. Distributed computing approach is proposed via which sub-problems achieve consensus and converge to network-level optimal solutions. The novelty lies in leveraging the nature of power flow equations in radial network topologies to design effective decomposition techniques that reduce the number of iterations required to achieve consensus by an order of magnitude.

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