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This paper explores whether graph embedding methods can be used as a tool for analysing the robustness of power-grids within the framework of network science. The paper focuses on the strain elevation tension spring embedding (SETSe) algorithm and compares it to node2vec and Deep Graph Infomax, and the measures mean edge capacity and line load. These five methods are tested on how well they can predict the collapse point of the giant component of a network under random attack. The analysis uses seven power-grid networks, ranging from 14 to 2000 nodes. In total, 3456 load profiles are created for each network by loading the edges of the network to have a range of tolerances and concentrating network capacity into fewer edges. One hundred random attack sequences are generated for each load profile, and the mean number of attacks required for the giant component to collapse for each profile is recorded. The relationship between the embedding values for each load profile and the mean collapse point is then compared across all five methods. It is found that only SETSe and line load perform well as proxies for robustness with $R^2 = 0.89$ for both measures. When tested on a time series normal operating conditions line load performs exceptionally well ($R=0.99$). However, the SETSe algorithm provides valuable qualitative insight into the state of the power-grid by leveraging its method local smoothing and global weighting of node features to provide an interpretable geographical embedding. This paper shows that graph representation algorithms can be used to analyse network properties such as robustness to cascading failure attacks, even when the network is embedded at node level.
A power system electromechanical wave propagates from the disturbance location to the rest of system, influencing various types of protections. In addition, since more power-electronics-interfaced generation and energy storage devices are being integrated into power systems, electromechanical wave propagation speeds in the future power systems are likely to change accordingly. In this paper, GPS-synchronized measurement data from a wide-area synchrophasor measurement system FNET/GridEye are used to analyze the characteristics of electromechanical wave propagation in the U.S. Eastern Interconnection (EI) system. Afterwards, high levels of photovoltaic (PV) penetration are modeled in the EI to investigate the influences of a typical power-electronics--interfaced resource on the electromechanical wave propagation speed. The result shows a direct correlation between the local penetration level of inverter-based generation and the electromechanical wave propagation speed.
The present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The CATV sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the state of a distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored buses using random forest. The effectiveness of the proposed approach was demonstrated using a 1572-bus feeder from the SMART-DS data set for two case studies - passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.
Blackouts in power grids typically result from cascading failures. The key importance of the electric power grid to society encourages further research into sustaining power system reliability and developing new methods to manage the risks of cascading blackouts. Adequate software tools are required to better analyze, understand, and assess the consequences of the cascading failures. This paper presents MATCASC, an open source MATLAB based tool to analyse cascading failures in power grids. Cascading effects due to line overload outages are considered. The applicability of the MATCASC tool is demonstrated by assessing the robustness of IEEE test systems and real-world power grids with respect to cascading failures.
A major concern associated to the massive connection of distributed energy resources is the increasing share of power electronic interfaces resulting in the global inertia reduction of power systems. The recent literature advocated the use of voltage source converter (VSC) interfaced battery energy storage system (BESS) as a potential way to counterbalance this lack of inertia. However, the impact of VSCs on the dynamics of reduced-inertia grids is not well understood especially with respect to large transmission grids interfacing a mix of rotating machines and resources interfaced with power electronics. In this regards, we propose an extension of the IEEE 39-bus test network used to quantify the impact of VSCs on reduced-inertia grids. In this respect, a reduced-inertia 39-bus system is obtained by replacing 4 synchronous generators in the original 10-synchronous machine system, with 4 wind power plants modeled as aggregated type-3 wind turbines. Then, a large-scale BESS is integrated into the reduced-inertia network via a three-level neutral-point clamped (NPC) converter, thereby to be used for studying the impact of VSC on the dynamics of the inertia-reduced power system, as well as for comparing different VSC controls. The proposed models are implemented on a real-time simulator to conduct post-contingency analysis, respectively, for the original power system and the reduced-inertia one, with and without the BESS-VSC.
Electric power grids are critical infrastructure that support modern society by supplying electric energy to critical infrastructure systems. Incidents are increasing that range from natural disasters to cyber attacks. These incidents threaten the reliability of power systems and create disturbances that affect the whole society. While existing standards and technologies are being applied to proactively improve power system reliability and resilience, there are still widespread electricity outages that cause billions of dollars in economic loss annually and threaten societal function and safety. Improving resilience in preparation for such events warrants strategic network design to harden the system. This paper presents an approach to strengthen power system security and reliability against disturbances by expanding the network structure from an ecosystems perspective. Ecosystems have survived a wide range of disturbances over a long time period, and an ecosystems robust structure has been identified as the key element for its survivability. In this paper, we first present a study of the correlation of ecological robustness and power system structures. Then, we present a mixed-integer nonlinear programming problem (MINLP) that expands the transmission network structure to maximize ecological robustness with power system constraints for an improved ability to absorb disturbances. We solve the MINLP problem for the IEEE 24 Bus Reliability Test System and three synthetic power grids with 200-, 500- and 2000-buses, respectively. Our evaluation results show the optimized power systems have increased the networks robustness, more equally distributed power flows, and less violations under different levels of contingencies.