This study delves into network topology optimization (NTO) as a corrective switching mechanism, designed to effectively manage congestion within power system networks. Our proposed model employs an exact node-breaker representation of the network by utilizing sparse tableau formulation (STF), and incorporates a full set of network switching actions that include both bus and line switching. NTO is formulated with nonlinear AC power flow constraints, capturing the physical behavior and engineering limits of the network comprehensively. Solving the NTO problem that incorporates AC power flow constraints presents significant challenges. To tackle this computational issue, a modified STF representation has been introduced, which reduces both the number of variables and constraints and also eliminates the non-convexity inherent in power balance equations. This modification, which alters the geometry of the STF, reduces the computational complexity of the problem. The revised NTO model is subsequently tackled through an iterative algorithm. Our numerical results demonstrate the effectiveness of the proposed model in managing network congestion by reducing congestion/overloads in the branches, under both base case and contingency scenarios, through strategic bus/line switching actions. In particular, it shows that the proposed method has a better convergence rate compared to the existing STF-based method. Furthermore, the reconfigured network topologies proposed by our method result in fewer congested branches than those proposed by other exiting techniques examined in this study.

A novel topology control algorithm is proposed here to reduce congestion and overloads, thereby improving security margins in a power network. The proposed heuristic relies on normalized squares of the line currents as a direct indicator of transmission line loading. The sensitivity of this measure with respect to breaker positions forms the basis of the proposed heuristic to select switching actions for a reconfigured network topology that reduces the congestion and overloads while holding generation dispatch essentially fixed. While many prior works have examined this class of problem for line switching actions (i.e., topology changes that insert or remove branches), bus-bar switching actions (topology changes that merge or split nodes/buses) have received less research focus. Here, both line switching and bus-bar switching are implemented using a detailed node-breaker representation of the network. The algorithm adopts a novel form of the AC power flow that allows sparse calculation and reduced computation time. Case studies demonstrate that the proposed approach efficiently reduces congestion and overloads in the base case and in contingency situations.

This paper summarizes a grid optimization (GO) competition effort in the United States to find the best solution strategies for up to interconnect-scale power system networks with around 32,000 buses. The optimization problem is a mixedinteger, non-convex non-linear problem, (MINLP) and includes discrete variables such as unit commitment and line switching, control settings (transformer taps and phase shifters with impedance correction tables), and bus shunts. The case study includes six actual industry grids as well as 16 realistic synthetic grids created by three different dataset teams. The winners are selected and ranked based on scoring criteria, which consider the solution quality (such as objective functions) within time limits. Nine winner teams are selected from 26 competitor teams. The results achieved by different teams are described and the performance of different algorithms on synthetic grids and actual industry grids are compared and analyzed.

Network topology optimization (NTO) in power networks expands the range of operational decisions that can contribute to an efficient market and/or system operation. This work focuses on corrective switching actions within the NTO problem to effectively manage congestion in power system networks. Notably, many existing NTO formulations utilize the bus-branch model, which either ignores or approximates a detailed description of substation switching actions, and thus overlooks potential benefits resulting from a realistic picture of all substation configurations. To this end, we develop an innovative corrective switching algorithm utilizing both substation reconfiguration and line switching based on the exact "node-breaker" representations with full nonlinear power flow constraints by employing the sparse tableau formulation and leveraging the piece-wise McCormick relaxation technique. Numerical case studies illustrate the effectiveness of the proposed NTO-based congestion management tool, and provide evidence of its potential to be extended for larger power system networks.

This paper addresses optimal power flow (OPF) problems that include a broader range of discrete decision variables using detailed node-breaker models of power networks, and it extends some of the existing heuristics to incorporate the node-breaker model. These heuristics can be used standalone or as a pre-screening step to generate a manageably small set of breaker candidates to be considered in a more computationally intensive mixed integer program (MIP) optimization. The computation time and quality of solution for the proposed heuristics here are benchmarked against some of the existing approaches. Numerical case studies demonstrate significant examples in which the methods proposed here yield improved operating points (i.e., lower operating cost), with low computational burden while more accurately representing the broader range of discrete decision variables.

This paper presents results from comparison of an industry-accepted model of the western United States with two different synthetic network models of the same geographic area. The synthetic models were independently produced by different construction algorithms but based on public information concerning the location and size of generation and demand. New realism measures examined include Fiedler Vector Partitioning (FVP), the Spectrum of the Normalized Laplacian (SNL), Topological and Impedance Element Ranking (TIER) and Singular Value decomposition of the power flow Jacobian (SVJ). The synthetic models differ greatly in size and granularity, but each has similarities to the industry-accepted model. However, the industry-accepted model exhibits behaviour not replicated in either of the synthetic models. The suitability of each analysis to assess the realism of a network model is discussed.

A novel topology control algorithm using a reactive power loss function is proposed in this paper as a means to reduce congestion and improve margins in the network. A broad range of network switching actions, including both line switching and bus-bar switching, are implemented using a detailed node-breaker representation of the network. The algorithm uses a particular form of the AC power flow equations that allow for the sparse calculation of reactive power loss sensitivity. The numerical results obtained from the case studies show that the proposed approach efficiently reduces congestion or overload in pre-contingency or post-contingency situations. Thus, the proposed algorithm can be used in real-time operation or as a pre-screening step in remedial action plans (RAP) to determine the proper switching actions to reduce the congestion or overload in the network.

Synchrophasors generate large volumes of data that greatly benefit from compression for its efficient storage and transfer. In this paper, we investigate the effect of the quality of phasor measurement data on lossy compression, and propose effective data conditioning approaches to improve the compression performance. Specifically, three conditioning methods are employed: a Hamming window-based low-pass filtering scheme to clean measurements corrupted by noise, an algorithm to identify and eliminate measurements with significant amount of noise, and a simple scaling scheme to normalize the magnitudes of different types of measurements. Numerical results obtained on PMU data showcase the capability of the proposed approaches in improving compression performance.

Deployments of high-sampling rate synchronised phasor measurement units (PMUs) are growing rapidly throughout the world, and with the advent of microPMUs, spreading from bulk transmission through distribution systems. The growing volume of PMU data presents challenges in its communication and storage, motivating consideration of compression algorithms. This study presents a novel lossy compression algorithm that exploits particular characteristics of power system measurements to improve the compression. Concepts successfully applied in image compression are tailored to the spatio-temporal correlations induced between electrical quantities via their network interconnections. The quality of the resulting compression is judged on the balance of storage space savings versus the accuracy of data reconstruction. In representative real-world and transient simulation datasets, the technique developed can provide storage compression in the range of 40:1 when different physical quantities are compressed together. The compression ratios can be in the range of 90:1 for voltage magnitudes and 190:1 for frequency when the measurements are compressed separately. The high-compression ratios are achieved while maintaining low-loss (high-accuracy) reconstruction.

It has been demonstrated that network topology optimization (NTO) may change the topology of power system networks, and consequently, provide additional flexibility to reduce network congestion and violations. Most NTO problems are formulated based on the bus-branch model in which it is challenging to represent a realistic picture of all substation configurations. In this paper, we explore advantages of substation reconfiguration modeling based on node-breaker representations for NTO problem with full nonlinear alternating current power flow. It also proposes a tailored solution algorithm to solve this nonconvex mixed-integer nonlinear programming through the outer approximation method. The proposed solution approach iterates between a mixed-integer linear programming and a nonlinear subproblem. Additional enhancements to further accelerate the iteration process are illustrated. Numerical case studies demonstrate the relative economic and operational impact of optimal network topology with node-breaker representations.

Realistic representations of contingencies in AC optimal power flow (ACOPF) often challenge traditional bus-branch network models. Derived from nodal analysis, such approaches represent network constraints in terms of the familiar bus admittance matrix, Y-bus. A fixed Y-bus is unable to represent common circuit breaker actions such as bus splitting. Work-arounds for Y-bus-based analysis typically rely on topology processing, switching between different Y-bus matrices depending on breaker settings. In this paper, we propose a very general node-breaker approach, employing multi-port element models and using a sparse tableau formulation (STF) for network constraints. Instead of treating breaker action as altering network topology, and hence changing the structure of Kirchhoff's voltage law (KVL) and Kirchhoff's current law (KCL) equations, this approach represents a breaker's position as impacting only constraints associated with a single component, thereby maintaining fixed structure in the KVL and KCL constraints. While larger numbers of variables are required, STF proves sparser than Y-bus formulations. Numerical case studies herein demonstrate that STF provides computational efficiency comparable to a Y-bus-based ACOPF at the scale of several hundred buses, and lower computational cost in an example of over one thousand buses.

In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods. However, it is often difficult to directly compare these studies due to subtle differences in the AC-OPF problem formulation as well as the network, generation, and loading data that are used for evaluation. To help address these challenges, this IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPF networks for benchmarking AC-OPF algorithms. A motivating study demonstrates some limitations of the established network datasets in the context of benchmarking AC-OPF algorithms and a validation study demonstrates the efficacy of using the PGLib-OPF networks for this purpose. In the interest of scientific discourse and future additions, the PGLib-OPF benchmark library is open-access and all the of network data is provided under a creative commons license.

Assembly of power-flow equations has traditionally begun from a nodal analysis formulation of the underlying transmission circuit's behavior. Most power-flow formulations encapsulate network constraints in the bus admittance matrix, Y bus . From a circuit perspective, this admittance representation restricts the network elements to be voltage controlled; resulting drawbacks treating zero-impedance branches in such applications as state estimation have long been recognized. This paper explores the advantages of the alternatives to Y bus -based formulations in power flow. It proposes a sparse tableau formulation (STF), and it demonstrates its computational efficiency, robustness, and generality in detailed comparison to traditional Y bus -based solution algorithms. In the examples of power networks ranging from 1888 to 82 000 buses, computational case studies indicate that STF provides comparable computational speed, while allowing simple treatment of zero-impedance branches and more reliably converging to solutions in many cases for which Y bus -based Newton algorithms diverge.

Distributed energy resources (DERs) are projected to be significant contributors in electric power grid modernization efforts. Yet many distribution and transmission system operators are concerned about potential reliability and security impacts of high penetration of DERs, and cite the need of greater visibility and controllability of decision variables associated with these DERs. Tractable modeling of DERs in AC optimal power flow (ACOPF) is one of the barriers to their integration into the existing operation, planning and market paradigm. Similarly, as more intermittent renewable resources are deployed, power flow control becomes more valuable, and accurate representation of Flexible AC Transmission System (FACTS) devices in ACOPF will likely grow in importance. This paper focuses on the detailed representation of substations with the high penetration of DERs, and on the Unified Power Flow Controller (UPFC) FACTS devices, in OPF. To this end, a novel Sparse Tableau Formulation (STF) for ACOPF is used to accurately model such features as substation reconfiguration to accommodate DERs, and to represent decision variables appropriate to the voltage source converters within UPFCs. Numerical studies are provided to demonstrate the impacts of detailed DER and UPFC representations on optimal operation, and the computational efficiency.

Optimal power flow (OPF) approaches employing such methods as semi-definite programming (SDP) have garnered considerable interest in the literature of the last decade. The OPF formulations for these approaches have almost universally relied on Ybus admittance matrix representations, which derive from nodal analysis, and restrict allowable network elements to be voltage-controlled only. Limitations of nodal analysis long been recognized, and to overcome these, commercial power system software often employs modified nodal analysis (MNA). Here, we consider a novel general power system modeling approach based on multi-port representation of individual components with Sparse Tableau Formulation (STF) of network constraints, which is more versatile than MNA for OPF requiring many monitored links with constrained flows. In STF, one is better able to exploit the fact that the vast majority of power grid network elements have voltage-current behavior that is well-modeled as linear. This opens the door to simple, engineering-based convex relaxations. We discuss two relaxations, admittance-based and current-based. The tightness of the relaxation is shown to improve when angle constraints and narrow bounds for active power generation are provided. Sequential bound tightening and reduced spatial branch-and-bound are discussed to obtain stronger relaxation solution. We conduct case studies to show the effectiveness of our relaxations with standard test cases.

For large-scale power networks, the failure of particular transmission lines can offload power to other lines and cause self-protection trips to activate, instigating a cascade of line failures. In extreme cases, this can bring down the entire network. Learning where the vulnerabilities are and the expected timescales for which failures are likely is an active area of research. In this article we present a novel stochastic dynamics model for a large-scale power network along with a framework for efficient computer simulation of the model including long timescale events such as cascade failure. We build on an existing Hamiltonian formulation and introduce stochastic forcing and damping components to simulate small perturbations to the network. Our model and simulation framework allow assessment of the particular weaknesses in a power network that make it susceptible to cascade failure, along with the timescales and mechanism for expected failures.