arXiv subjects

Amritanshu Pandey

Publications and source records attributed to Amritanshu Pandey.

At least 55 records · Page 3Linked to original sources

arXiv2021

Combined Transmission and Distribution State-Estimation for Future Electric Grids

Proliferation of grid resources on the distribution network along with the inability to forecast them accurately will render the existing methodology of grid operation and control untenable in the future. Instead, a more distributed yet coordinated approach for grid operation and control will emerge that models and analyzes the grid with a larger footprint and deeper hierarchy to unify control of disparate T&D grid resources under a common framework. Such approach will require AC state-estimation (ACSE) of joint T&D networks. Today, no practical method for realizing combined T&D ACSE exists. This paper addresses that gap from circuit-theoretic perspective through realizing a combined T&D ACSE solution methodology that is fast, convex and robust against bad-data. To address daunting challenges of problem size (million+ variables) and data-privacy, the approach is distributed both in memory and computing resources. To ensure timely convergence, the approach constructs a distributed circuit model for combined T&D networks and utilizes node-tearing techniques for efficient parallelism. To demonstrate the efficacy of the approach, combined T&D ACSE algorithm is run on large test networks that comprise of multiple T&D feeders. The results reflect the accuracy of the estimates in terms of root mean-square error and algorithm scalability in terms of wall-clock time.

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arXiv2021

Analytical Inverter-Based Distributed Generator Model for Power Flow Analysis

Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous. Existing distribution system tools predominately model distributed generation sources as either negative PQ loads or as a PV generator and then employed a PV-PQ switching algorithm to mimic Volt/VAR support. These models neglect the unique characteristics of inverter-based distributed generation sources, have scalability and convergence issues, and are ill-suited for increasing solar penetration scenarios. This work proposes an inverter-based DG model accounting for the inverter's topology, sensing position, and control strategies. The model extends recently introduced analytical positive sequence generator models for three-phase studies. The use of circuit-simulation based heuristics help achieve robust convergence. Simulation of the PG&E prototypical feeders using a prototype solver demonstrate the model's accuracy and efficacy.

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arXiv2021

Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge

Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow. Current commercial tools, however, prioritize speed of convergence over accuracy by relying on initial conditions that are taken from the steady state solution of similar network configurations that are not guaranteed to lie within a convex region of a valid solution. In this paper we introduce a globally convergent algorithm to facilitate fast and accurate AC steady state simulation and optimization based on prior knowledge from similar networks. The approach uses a homotopy method that gradually and efficiently translates a previously known network configuration to the current network configuration. The proposed formulation is highly scalable, and its efficacy is demonstrated for resiliency study and optimization of large networks up to 70k buses.

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arXiv2021

A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach

For reliable and secure power grid operation, AC state-estimation (ACSE) must provide certain guarantees of convergence while being resilient against bad-data. This paper develops a circuit-theoretic weighted least absolute value (WLAV) based hybrid ACSE that satisfies these needs to overcome some of the limitations of existing ACSE methods. Hybrid refers to the inclusion of RTU and PMU measurement data, and the use of the LAV objective function enables automatic rejection of bad data while providing clear identification of suspicious measurements from the sparse residual vector. Taking advantage of linear construction of the measurement models in circuit-theoretic approach, the proposed hybrid SE is formulated as a LP problem with guaranteed convergence. To address efficiency, we further develop problem-specific heuristics for fast convergence. To validate the efficacy of the proposed approach, we run ACSE on large cases and compare the results against WLS-based algorithms. We further demonstrate the advantages of our solution methodology over standard commercial LP solvers through comparison of runtime and convergence performance.

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arXiv2020

Incremental Model Building Homotopy Approach for Solving Exact AC-Constrained Optimal Power Flow

Alternating-Current Optimal Power Flow (AC-OPF) is framed as a NP-hard non-convex optimization problem that solves for the most economical dispatch of grid generation given the AC-network and device constraints. Although there are no standard methodologies for obtaining the global optimum for the problem, there is considerable interest from planning and operational engineers in finding a local optimum. Nonetheless, solving for the local optima of a large AC-OPF problem is challenging and time-intensive, as none of the leading non-linear optimization toolboxes can provide any timely guarantees of convergence. To provide robust local convergence for large complex systems, we introduce a homotopy-based approach that solves a sequence of primal-dual interior point problems. We utilize the physics of the grid to develop the proposed homotopy method and demonstrate the efficacy of this approach on U.S. Eastern Interconnection sized test networks.

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arXiv2020

A Circuit-Theoretic Approach to State Estimation

Traditional state estimation (SE) methods that are based on nonlinear minimization of the sum of localized measurement error functionals are known to suffer from non-convergence and large residual errors. In this paper we propose an equivalent circuit formulation (ECF)-based SE approach that inherently considers the complete network topology and associated physical constraints. We analyze the mathematical differences between the two approaches and show that our approach produces a linear state-estimator that is mathematically a quadratic programming (QP) problem with closed-form solution. Furthermore, this formulation imposes additional topology-based constraints that provably shrink the feasible region and promote convergence to a more physically meaningful solution. From a probabilistic viewpoint, we show that our method applies prior knowledge into the estimate, thus converging to a more physics-based estimate than the traditional observation-driven maximum likelihood estimator (MLE). Importantly, incorporation of the entire system topology and underlying physics, while being linear, makes ECF-based SE advantageous for large-scale systems.

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arXiv2020

SUGAR-R: Robust Online Restoration Platform for SCADA-Absent Grid

Secure and fast grid restoration from a collapsed state is increasingly critical as blackouts are becoming more prevalent around the globe. Generally, the restoration of grid during a blackout is achieved with the help of Supervisory Control and Data Acquisition System (SCADA) based central control; however, with the threat of cyber-blackouts, this presumption of an available and secure SCADA system is not valid. This is also true for grids in developing countries as well as for many distribution and micro grids that lack SCADA. In this paper, we introduce an online framework for localized grid restoration that validates and updates a pre-defined crank path in real-time based on the vital grid states of voltages, currents and frequency. The proposed framework maintains an online network topology of the localized grid that can continuously sample local measurements and update the grid model, thereby circumventing SCADA based central control. In the results section we demonstrate the efficacy of this framework for blackstart by ensuring a feasible crank path with voltage and frequency states within bounds, while further assisting in synchronization of two disconnected sub-grids during the re-energization process using a distributed framework.

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arXiv2019

Evaluating Feasibility within Power Flow

Recent development of techniques that improve the convergence properties of power flow simulation have been demonstrated to facilitate scaling to large system sizes (80k+ buses). However, the problem remains to reliably identify cases that are infeasible, system configurations that have no solution. In this paper, we use the circuit theoretic approach based on adjoint networks to evaluate the feasibility of a power flow test case and further locate and quantify the source of infeasibility in the cases operating beyond the tip of the nose curve. By creating infeasibility current source models that are added to each node of the system model and further coupling each source to its corresponding node of the adjoint network, any locations of insufficient real or reactive power are captured by a non-zero response of the adjoint network. Furthermore, it is shown that the proposed joint simulation of power flow and its adjoint network models provide the optimally minimized currents that can be later utilized to inform corrective actions to restore the feasibility of power flow problems.

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arXiv2019

Robust Sequential Steady-State Analysis of Cascading Outages

Simulating potential cascading failures can be useful for avoiding or mitigating such events. Currently, existing steady-state analysis tools are ill-suited for simulating cascading outages as they do not model frequency dependencies, they require good initial conditions to converge, and they are unable to distinguish between a collapsed grid state from a hard-to-solve test case. In this paper, we extend a circuit-theoretic approach for simulating the steady-state of a power grid to incorporate frequency deviations and implicit models for underfrequency and undervoltage load shedding. Using these models, we introduce a framework capable of robustly solving cascading outages of large-scale systems that can also locate infeasible regions. We demonstrate the efficacy of our approach by simulating entire cascading outages on more than 8000 nodes sample testcase.

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arXiv2019

A LASSO-Inspired Approach for Localizing Power System Infeasibility

A method is proposed to identify and localize the cause of network collapse with augmented power flow analysis for a grid model with insufficient resources. Owing to heavy network loading, insufficient generation, component failures and unavoidable disturbances, power grid models can sometimes fail to converge to a feasible solution for a steady-state power flow study. For applications such as system expansion planning, it is desirable to locate the system buses that are contributing to network infeasibilities to facilitate corrective actions. This paper proposes a novel LASSO-inspired regularization of the power flow matrix that enforces sparsity to localize and quantify infeasibilities in the network. One of the examples demonstrates how the proposed method is capable of localizing a source of blackout to a single dominant bus on an 80k+ bus eastern interconnection model.

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arXiv2019

Implicitly Modeling Frequency Control within Power Flow

In this paper, we extend a circuit-based, current-voltage power flow formulation to include frequency deviations and implicitly model generator primary and secondary control actions as a function of their temporal dependence. This includes extending the slack bus generator model(s) to better represents its true behavior with frequency controls. These implicit models obviate the need for outer iteration loops and improve the robustness of the simulation convergence when frequency deviations are considered. The simulation framework is highly scalable and is demonstrated on 85k+ bus systems.

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arXiv2019

Steady-State Simulation for Combined Transmission and Distribution Systems

The future electric grid will consist of significant penetration of renewable and distributed generation that is likely to create a homogenous transmission and distribution (T&D) system, requiring tools that can model and robustly simulate the combined T&D networks. Existing tools use disparate models and formulations for simulation of transmission versus distribution grids and solving for the steady-state solution of the combined T&D networks often lacks convergence robustness and scalability to large systems. In this paper, we show that modeling both the T&D grid elements in terms of currents and voltages using an equivalent circuit framework enables simulation of combined positive sequence networks of the transmission grids with three-phase networks of the distribution grids without loss of generality. We further demonstrate that we can ensure robust convergence for these resulting large-scale complex T&D systems when the circuit simulation methods are applied to them. Our results illustrate robust convergence of combined T&D networks using a direct Newton-Raphson solver on a single machine for smaller sized systems and using a parallel Gauss-Seidel-Newton solver on multiple machines for larger sized systems with greater than million nodes.

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arXiv2019

Improving Voltage Profile of the Nigerian Power Grid

Extreme voltages at the system nodes are one of the primary causes for total and partial collapse of the Nigerian grid. In this paper, we develop a framework to re-dispatch the voltage set-points of committed generators in the grid to improve the voltage profile of the system nodes thus lowering the likelihood of a grid collapse. This framework is an extension of circuit-theoretic formulation that can robustly solve general-purpose grid optimization problems. In the results section, we re-dispatch the voltage setpoints for committed generators in real-life Nigerian grid operation and planning test cases to improve the overall voltage profile of the grid. We further demonstrate that the re-dispatched grid is more resilient and secure than the base case through running contingency analyses.

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arXiv2019

A Probabilistic Approach to Power System State Estimation using a Linear Algorithm

An equivalent circuit formulation for power system analysis was demonstrated to improve robustness of Power Flow and enable more generalized modeling, including that for RTUs (Remote Terminal Units) and PMUs (Phasor Measurement Units). These measurement device models, together with an adjoint circuit based optimization framework, enable an alternative formulation to Power System State Estimation (SE) that can be solved within the equivalent circuit formulation. In this paper, we utilize a linear RTU model to create a fully linear SE algorithm that includes PMU and RTU measurements to enable a probabilistic approach to SE. Results demonstrate that this is a practical approach that is well suited for real-world applications.

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arXiv2019

Impact of Load Models on Power Flow Optimization

Aggregated load models, such as PQ and ZIP, are used to represent the approximated load demand at specific buses in grid simulation and optimization problems. In this paper we examine the impact of model choice on the optimal power flow solution and demonstrate that it is possible for different load models to represent the same amount of real and reactive power at the optimal solution yet correspond to completely different grid operating points. We introduce the metric derived from the maximum power transfer theorem to identify the behavior of an aggregated model in the OPF formulation to indicate its possible limitations. A dataset from the Carnegie Mellon campus is used to characterize three types of load models using a time-series machine learning algorithm, from which the optimal power flow results demonstrate that the choice of load model type has a significant impact on the solution set points. For example, our results show that the PQ load accurately characterizes the CMU data behavior correctly for only 16.7% of the cases.

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arXiv2019

Robust and Efficient Power Flow Convergence with G-min Stepping Homotopy Method

Recent advances have shown that the circuit simulation algorithms that allow for solving highly nonlinear circuits of over one billion variables can be applicable to power system simulation and optimization problems through the use of an equivalent circuit formulation. It was demonstrated that large-scale (80k+ buses) power flow simulations can be robustly solved, independent of the initial starting point. In this paper, we extend the electronic circuit-based G-min stepping homotopy method to power flow simulations. Preliminary results indicate that the proposed algorithm results in significantly better simulation runtime performance when compared to existing homotopy methods.

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arXiv2018

Continuously Differentiable Analytical Models for Implicit Control within Power Flow

Achieving robust and scalable convergence for simulation of realistic power flow cases can be challenging. One specific issue relates to the disconnected solution space that is created by the use of piecewise-discontinuous models of power grid devices that perform control mechanisms. These models are generally resolved by outer iteration loops around power flow, which can result in solution oscillations, increased iteration count, divergence or even convergence to a solution in an unstable operational region. This paper introduces a continuously differentiable model for device control mechanisms that is incorporated within the power flow formulation. To ensure robust power flow convergence properties, recently introduced homotopy methods are extended to include these continuous models. The scalability and efficacy of the proposed formulation is demonstrated on several large-scale test cases that represent the US Eastern Interconnect network, the Synthetic USA, and the Nigerian grid.

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arXiv2018

Unified power system analyses and models using equivalent circuit formulation

In this paper we propose and demonstrate the potential for unifying models and algorithms for the steady state and transient simulation of single-phase and three-phase power systems. At present, disparate algorithms and models are used for the different analyses, which can lead to inconsistencies such as the transient analysis as time approaches infinity not matching the steady state analysis of the same conditions. Using our equivalent circuit formulation of the power system, we propose a methodology for forming physics-based models that can facilitate transient, balanced power flow, and three-phase power flow in one simulation environment. The approach is demonstrated on a three-phase induction motor. Existing industry tools are used to validate the model and simulation results for the different analyses.

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