Transient stability analysis is performed to assess the power system's condition after a severe contingency and is carried out using simulations. To adequately assess the system's transient stability, the correct dynamic models for the machines (i.e., generators, condensers, and motors) along with their dynamic parameters must be defined. The IEEE test systems contain the data required for steady-state studies. However, neither the dynamic model of the machines nor their specific parameters have been established for transient studies. As a result, there is a demand for test bed systems suitable for transient analysis. This paper defines dynamic machine models along with their parameters for each IEEE test bed system, thus producing full dynamic models for all test systems. It is important to mention that the parameters of the proposed dynamic models are based on typical data. The test systems are subjected to large disturbances, and a case study for each test system, which examines the frequency, angle, and voltage stability, is presented. Furthermore, the proposed dynamic IEEE test systems, implemented in PowerWorld, are available online.

Cascading failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on cascading failure reviews and synthesizes how benchmarking and validation can be done for cascading failure analysis, summarizes and reviews the cascading test cases that are available to the international community, and makes recommendations for improving the state of the art.

High penetrations of domestic electric vehicles (EVs) in U.K. low voltage (LV) networks may result in significant technical problems. This paper proposes an implementable, centralized control algorithm, currently being trialed in 9 U.K. residential LV networks, that uses limited information to manage EV charging points to mitigate these technical problems. Two real U.K. LV networks are used to quantify the potential impacts of different EV penetration levels and to demonstrate the effectiveness of the control algorithm (using different control cycles) for simultaneous thermal and voltage management. Monte Carlo simulations (adopting 1-min resolution data) are undertaken to cater for domestic and EV demand uncertainties. Results for these LV networks show that problems may occur for EV penetrations higher than 20%. More importantly, they highlight that even for a 100% penetration and control cycles of up to 10 min, the control algorithm successfully mitigates problems on the examined LV networks. Crucially, to determine effects on the comfort of EV users, a metric is introduced and discussed. The results of different control settings are presented to analyze potential adaptations of the control strategy. Finally, a comparison with an optimization framework highlights that the proposed algorithm is as effective while using limited information.

In the coming years, hundreds of thousands of new electric vehicles (EVs), from plug-in hybrids to fully electric, will hit the roads around the world, adding to the current EV fleet of more than 2 million, according to the Global EV Outlook 2017. The electrification of transportation can bring environmental, health, and economic benefits when coupled with a low-carbon electricity generation portfolio; however, ensuring that this transition goes smoothly requires addressing several grid-integration challenges.

To truly quantify the impact of electric vehicles (EVs) on the electricity network and their potential interactions in the context of Smart Grids, it is crucial to understand their charging behavior. However, as EVs are yet to be widely adopted, these data are scarce. This work presents results of a thorough statistical analysis of the charging behavior of 221 real residential EV users (Nissan LEAF, i.e., 24kWh, 3.6 kW) spread across the UK and monitored over one year (68,000+ samples). Probability distribution functions (PDFs) of different charging features (e.g., start charging time) are produced for both weekdays and week-ends. Crucially, these unique PDFs can be used to create stochastic, realistic and detailed EV profiles to carry out impact and/or Smart Grid-related studies. Finally, the effects of the EV demand on future UK distribution networks are discussed.