This study models the coincidence factor (CF) of electric vehicle (EV) charging given driving and plug-in behaviors by combining data sources from travel surveys and recorded EV charging data. From these, we generate travel and plug-in behaviors by using a Monte Carlo approach to derive CFs. By varying the EV battery size, the rated charging power, and the plug-in behavior, their influence on the CF is examined. The key results show that the CF decreases to less than 25% when considering more than 50 EVs with a charging level of 11 kW, with the CF strongly depending on the number of EVs considered. By contrast, the driving behavior and the battery size have a minor influence on the CF. Furthermore, when mixing the parameters, such as EV battery size and rated charging power, then, especially, the active power drawn by the feeder does not change linearly. Ultimately, the study aims to add to the state-of-the-art by solely and systematically focusing on the CF and its sensitivity to a number of key factors. For planning and design, distribution system operators may use this study as a part of their planning for the integration of electric vehicles in the electrical grid.

Moving towards regional Supergrids, an increasing number of interconnections are formed by High Voltage Direct Current (HVDC) lines. Currently, in most regions, HVDC losses are not considered in market operations, resulting in additional costs for Transmission System Operators (TSOs). Nordic TSOs have proposed the introduction of HVDC loss factors in the market clearing algorithm, to account for the cost of losses and avoid HVDC flows between zones with zero price difference. In this paper, we introduce a rigorous framework to assess the introduction of loss factors, in particular HVDC loss factors, in nodal and zonal pricing markets. First, we focus on the identification of an appropriate loss factor. We propose and compare three different models: constant, linear, and piecewise linear. Second, we introduce formulations to include losses in market clearing algorithms. Carrying numerical tests for a whole year, we find that accounting only for HVDC or AC losses may lead to lower social welfare for a non-negligible amount of time. To counter this, this paper introduces a framework for including both AC and HVDC losses in a zonal or nodal pricing environment. We show both theoretically and through simulations that such a framework is guaranteed to increase social welfare.

In this paper, it is investigated how detailed the model of a synchronous machine needs to be in order to assess transient stability using a Single Machine Equivalent (SIME). The results will show how the stability mechanism and the stability assessment are affected by the model detail. In order to identify the transient stability mechanism, a simulation with a high-order model was used as reference. The Western System Coordinating Council System (WSCC) and the New England & New York system are considered and simulations of an unstable and a stable scenario are carried out, where the detail of the machine models is varied. Analyses of the results suggest that a 4 ^th -order model may be sufficient to represent synchronous machines in transient stability studies.