arXiv subjects

Anamika Dubey

Publications and source records attributed to Anamika Dubey.

At least 37 records · Page 2Linked to original sources

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.

physics.soc-ph↗

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.

eess.SY↗

arXiv2022

Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters

With massive penetrations of active grid-edge technologies, distributed computing and optimization paradigm has gained significant attention to solve distribution-level optimal power flow (OPF) problems. However, the application of generic distributed optimization techniques to OPF problems leads to a very large number of macro-iterations or communication rounds among the distributed computing agents delaying the decision-making process or resulting in suboptimal solutions. Moreover, the existing distribution-level OPF problems typically model inverter-interfaced distributed energy resources (DERs) as grid-following inverters; grid-supporting and grid-forming functionalities have not been explicitly considered. The added complexities introduced by different inverter models require further attention to developing an appropriate model for new types of inverter-based DERs and computationally-tractable OPF algorithms. In this paper, we expand the distribution-level OPF model to include a combination of the grid-forming, grid-supporting, grid-following inverter-based DERs and also present the application of a domain-specific problem decomposition and distributed algorithm for the topologically radial power distribution systems to efficiently solve distribution-level OPF problem.

eess.SY↗

arXiv2022

A Risk-Driven Probabilistic Approach to Quantify Resilience in Power Distribution Systems

It is of growing concern to ensure resilience in power distribution systems to extreme weather events. However, there are no clear methodologies or metrics available for resilience assessment that allows system planners to assess the impact of appropriate planning measures and new operational procedures for resilience enhancement. In this paper, we propose a resilience metric using parameters that define system attributes and performance. To represent extreme events (tail probability), the conditional value-at-risk of each of the parameters are combined using Choquet Integral to evaluate the overall resilience. The effectiveness of the proposed resilience metric is studied within the simulation-based framework under extreme weather scenarios with the help of a modified IEEE 123-bus system. With the proposed framework, system operators will have additional flexibility to prioritize one investment over the others to enhance the resilience of the grid.

eess.SY↗

arXiv2022

Spatiotemporal Impact Assessment of Hurricanes on Electric Power Systems

Almost 90% of the major power outages in the US are caused due to hurricanes. Due to the highly uncertain nature of hurricanes in both spatial and temporal dimensions, it is essential to quantify the effect of such hurricanes on a power grid. In this paper, we provide a Monte-Carlo-based framework in which several hurricane scenarios and their impact on a power grid are analyzed in spatiotemporal dimensions. The hurricane simulations are performed using samples from previously occurred hurricanes in the US whereas probabilistic assessment of the transmission lines is performed through line fragility model. Finally, a loss metric based on the amount of load disconnected due to hurricanes traveling inland is calculated for each time step. The simulation is performed on ACTIVSg2000: 2000-bus synthetic Texas grid while mapping the transmission lines of the test case on the geographical footprint of Texas. The simulation results show that the loss increases significantly for a few time steps when the wind field of a hurricane is intense and almost saturates when the intensity of the hurricane decays while traversing further. The proposed analysis can provide some insights for proactive planning strategies on improving the resilience of the power grid.

eess.SY↗

arXiv2021

Online Distributed Optimization in Radial Power Distribution Systems: Closed-Form Expressions

The limitations of centralized optimization methods in managing power distribution systems operations motivate distributed control and optimization algorithms. However, the existing distributed optimization algorithms are inefficient in managing fast varying phenomena, resulting from highly variable distributed energy resources (DERs). Related online distributed control methods are equally limited in their applications. They require thousands of time-steps to track the network-level optimal solutions, resulting in slow performance. We have previously developed an online distributed controller that leverages the system's radial topology to achieve network-level optimal solutions within a few time steps. However, it requires solving a node-level nonlinear programming problem at each time step. This paper analyzes the solution space for the node-level optimization problem and derives the analytical closed-form solutions for the decision variables. The theoretical analysis of the node-level optimization problem and obtained closed-form optimal solutions eliminate the need for embedded optimization solvers at each distributed agent and significantly reduce the computational time and optimization costs.

math.OC↗

arXiv2021

Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer

Reinforcement learning has been found useful in solving optimal power flow (OPF) problems in electric power distribution systems. However, the use of largely model-free reinforcement learning algorithms that completely ignore the physics-based modeling of the power grid compromises the optimizer performance and poses scalability challenges. This paper proposes a novel approach to synergistically combine the physics-based models with learning-based algorithms using imitation learning to solve distribution-level OPF problems. Specifically, we propose imitation learning based improvements in deep reinforcement learning (DRL) methods to solve the OPF problem for a specific case of battery storage dispatch in the power distribution systems. The proposed imitation learning algorithm uses the approximate optimal solutions obtained from a linearized model-based OPF solver to provide a good initial policy for the DRL algorithms while improving the training efficiency. The effectiveness of the proposed approach is demonstrated using IEEE 34-bus and 123-bus distribution feeders with numerous distribution-level battery storage systems.

cs.LG↗

arXiv2021

Extremum-Seeking Adaptive-Droop for Model-free and Localized Volt-VAR Optimization

In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and measurement uncertainties. In this paper, we proposed a model-free local Volt-VAR control approach for network-level optimization that does not require communication with other decision-making agents. The proposed algorithm is based on extremum-seeking approach that uses only local measurements to minimize the network power losses. To prove that the proposed extremum-seeking controller converges to the optimum solution, we also derive mathematical conditions for which the loss minimization problem is convex with respect to the control variables. Local controllers pose stability concerns during highly variable scenarios. Thus, the proposed extremum-seeking controller is integrated with an adaptive-droop control module to provide a stable local control response. The proposed approach is validated using IEEE 4-bus and IEEE 123-bus systems and achieves the loss minimization objective while maintaining the voltage within the pre-specific limits even during highly variable DER generation scenarios.

eess.SY↗

arXiv2021

Bilateral Market for Distribution-level Coordination of Flexible Resources using Volttron

Increasing penetrations of distributed energy resources (DERs) and responsive loads (RLs) in the electric power distribution systems calls for a mechanism for joint supply-demand coordination. Recently, several transactive/bilateral coordination mechanisms have been proposed for the distribution-level coordination of flexible resources. Implementing a transactive market coordination approach requires a secure, reliable, and computationally efficient multi-agent platform. An example of such a platform is VOLTTRON, developed by the Pacific Northwest National Laboratories (PNNL). The VOLTTRON platform allows the market actors to exchange information and execute proper control actions in a decentralized way. This paper aims to provide a proof-of-concept of the transactive market coordination approach via a small-scale demonstration on the VOLTTRON platform. The steps needed to implement the proposed market architecture using virtual machines and VOLTTRON are thoroughly described, and illustrative examples are provided to show the market-clearing process for different scenarios.

eess.SY↗

arXiv2021

Network-Level Optimization for Unbalanced Power Distribution System: Approximation and Relaxation

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system.

math.OC↗

arXiv2020

Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems

The limitations of centralized optimization methods for power systems operation have led to the distributed computing paradigm, particularly in power distribution systems. The existing techniques reported in recent literature for solving distributed optimization problems are not viable for power distribution systems applications. The essential drawback remains a large number of required communication rounds, i.e., macro-iterations among the computing agents to solve one instance of the optimization problem; the typical number of macro-iterations are in the order of 10^2~10^3. In this paper, a new and scalable distributed optimization method based on Equivalent Network Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a balanced radial distribution system. Specifically, the distribution system's radial topology is leveraged to reduce the decomposed systems into upstream and downstream network equivalents. The proposed innovations reduce the required number of macro-iterations/communication-rounds for convergence by order of magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test systems and is shown to converge to the same optimal solution as obtained using an equivalent centralized OPF (C-OPF) model.

eess.SY↗

arXiv2020

Conservation Voltage Reduction (CVR) via Two-Timescale Control in Unbalanced Power Distribution Systems

Voltage control devices are employed in power distribution systems to reduce the power consumption by operating the system closer to the lower acceptable voltage limits; this technique is called conservation voltage reduction (CVR). The different modes of operation for system's legacy devices (with binary control) and new devices (e.g. smart inverters with continuous control) coupled with variable photovoltaic (PV) generation results in voltage fluctuations which makes it challenging to achieve CVR objective. This paper presents a two-timescale control of feeder's voltage control devices to achieve CVR that includes (1) a centralized controller operating in a slower time-scale to coordinate voltage control devices across the feeder and (2) local controllers operating in a faster timescale to mitigate voltage fluctuations due to PV variability. The centralized controller utilizes a three-phase optimal power flow model to obtain the decision variables for both legacy devices and smart inverters. The local controllers operate smart inverters to minimize voltage fluctuations and restore nodal voltages to their reference values by adjusting the reactive power support. The proposed approach is validated using the IEEE-123 bus (medium-size) and R3-12.47-2 (large-size) feeders. It is demonstrated that the proposed approach is effective in achieving the CVR objective for unbalanced distribution systems.

eess.SY↗

arXiv2020

A Two-Stage Service Restoration Method for Electric Power Distribution Systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. The emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, although enhance the self-healing capability and allow faster recovery, pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. In this paper, we propose a novel two-stage restoration framework to generate restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and CLPU models. It is demonstrated using a multi-feeder test case that the proposed framework is effective in utilizing all available resources to quickly restore the service and generates an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

eess.SY↗

arXiv2019

Distributed PV Penetration Impact Analysis on Transmission System Voltages using Co-Simulation

With the growing penetrations of distributed energy resources (DERs), it is imperative to evaluate their impacts on transmission system operations. In this paper, an iteratively coupled transmission and distribution (T&D) co-simulation framework is employed to study the impacts of increasing penetrations of distribution-connected photovoltaic (PV) systems on transmission system voltages. The co-simulation framework introduces iterative coupling and unbalanced transmission system analysis that help accurately replicate the standalone T&D system results without resorting to the computational challenge of developing large-scale standalone T\&D models. The integrated T&D systems are evaluated for multiple PV deployment scenarios based on randomly generated PV locations and sizes. A test system is simulated using the IEEE-9 bus transmission system model integrated at each load point with three EPRI's Ckt-24 distribution feeder models. The results are thoroughly validated using a standalone T&D system model simulated in OpenDSS.

eess.SY↗

arXiv2019

A Framework to Analyze Interactions between Transmission and Distribution Systems

Analyzing the interactions between the transmission and distribution (T&D) system is becoming imperative with the increased penetrations of distributed energy resources (DERs) on electric power distribution networks. An assessment of the impacts of distribution system connected DERs on the transmission system operations is required especially pertaining to system unbalance when modeling both T&D systems. In this paper, a coupled T&D analysis framework is developed through the co-simulation approach. The framework utilizes legacy software to separately solve the decoupled T&D models. The T&D interactions are captured by exchanging network solutions at the point of common coupling (PCC). The proposed co-simulation framework adopts an iterative coupling approach resulting in a co-simulation model close to a stand-alone T&D platform. The proposed framework is tested using the IEEE-9 bus transmission system model and EPRI's Ckt-24 test distribution feeder model. A case study in which the IEEE 9 bus model interfaced with three ckt24 models is also presented to demonstrate the scalability of the approach. The conditions of convergence by exchanging the boundary variables at the PCC are examined in detail using several case studies with varying levels of load unbalance.

eess.SY↗

arXiv2019

Hybrid Transmission Distribution Co-simulation: Frequency Regulation using Battery Energy Storage

Battery energy storage systems (BESS) are proving to be an effective solution in providing frequency regulation services to the bulk grid. However, there are several concerns for the transmission/distribution system operators (TSO/DSO) with the frequent dispatching of the distribution-connected fast-responding storage systems. Unfortunately, the existing decoupled models for transmission and distribution (T&D) simulations are unable to capture the complex interactions between the two systems especially concerning frequency regulation problems due to rapidly varying distribution-connected distributed energy resources (DERs). In this paper, an iteratively coupled T&D hybrid co-simulation framework is developed to facilitate the planning studies for the system operators to help integrate distribution-connected BESS in providing frequency regulation services in response to highly variable DERs such as photovoltaic generation (PVs). Specifically, the proposed framework helps evaluate: (1) the effects of distribution-connected DERs/PVs on the response of the system's automatic generation control (AGC) response, and (2) highlights the use of BESS in providing frequency regulation services using integrated T&D model. The proposed framework is demonstrated using the IEEE 9-bus transmission system model (operating in dynamics mode) coupled with multiple EPRI Ckt-24 distribution system models (operating in quasi-static mode). It is shown that the proposed co-simulation framework helps better visualize the system AGC response and frequency regulation especially in the presence of high-levels of DER generation variability requiring frequent dispatch of BESS.

eess.SY↗

arXiv2019

Transmission-Distribution Co-Simulation: Analytical Methods for Iterative Coupling

With the increased penetrations of distributed energy resources (DERs), the need for integrated transmission and distribution system analysis (T&D) is imperative. This paper presents an integrated unbalanced T&D analysis framework using an iteratively coupled co-simulation approach. The unbalanced T&D systems are solved separately using dedicated solvers. An iterative approach is developed for T&D interface coupling and to ensure convergence of the boundary variables. To do so, analytical expressions governing the T&D interface are obtained. First-order and second-order convergent methods using the Fixed-point iteration (FPI) method and Newton's method, respectively are proposed to solve the system of nonlinear T&D interface equations. The proposed framework is tested using an integrated T&D system model comprised of a 9-bus IEEE transmission test system integrated with a real-world 6000-bus distribution test system. The results show that the proposed framework can model the impacts of system unbalance and increased demand variability on integrated T&D systems and converges during stressed system conditions. As expected, Newton's method converges faster with a fewer number of iterations as compared to the FPI method and the improvements are more pronounced during high levels of system unbalance and high loading conditions.

eess.SY↗

arXiv2019

Critical Load Restoration using Distributed Energy Resources for Resilient Power Distribution System

Extreme weather events have a significant impact on the aging and outdated power distribution infrastructures. These high-impact low-probability (HILP) events often result in extended outages and loss of critical services, thus, severely affecting customers' safety. This calls for the need to ensure resilience in distribution networks by quickly restoring the critical services during a disaster. This paper presents an advanced feeder restoration method to restore critical loads using distributed energy resources (DERs). A resilient restoration approach is proposed that jointly maximizes the amount of restored critical loads and optimizes the restoration times by optimally allocating grid's available DER resources. The restoration problem is modeled as a mixed-integer linear program with the objective of maximizing the resilience to post-restoration failures while simultaneously satisfying grid's critical connectivity and operational constraints and ensuring a radial operation for a given open-loop feeder configuration. Simulations are performed to demonstrate the effectiveness of the proposed approach using IEEE 123-node feeder with 5 DERs supplying 11 critical loads and IEEE 906-bus feeder with 3 DERs supplying 17 critical loads. The impacts of DER availability and fuel reserve on restored networks are assessed and it is shown that the proposed approach is successfully able to restore a maximum number of critical loads using available DERs.

eess.SY↗