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To analyze the grid integration effect of a virtual synchronous generator (VSG), this paper proposes a simple power system modeling method. It is shown that for a specific disturbance, by simplifying the original network to a radical n generators single-load model, its state-space small-signal model can be obtained using a unified formula, as long as the single generator infinite bus model of each generator is given. Case studies based on the IEEE 9-bus test system as well as the New York New England test system show high efficiency of the proposed modeling to represent slow power and frequency deviations. Modal analyses give insight into how system poles are synthesized by individual generator poles, and how different designs of VSGs affect the system oscillation modes. Potential errors due to the topology simplification and the measures to avoid them are discussed. The time-domain results and modal analyses highlight some important features of VSG, which are overlooked in previous studies. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to damp interarea oscillations by designing a robust multi-input multioutput supplementary controller for multiterminal dc (MTDC) systems embedded in ac grids. The key idea to achieve robustness lies in explicitly modeling the MTDC current injection as disturbances using an \mathcal H\infty mixed-sensitivity formulation in the linear matrix inequality framework. Control directions are established by selecting wide-area feedback signals and the relative gain array computation. Robustness is assessed through dynamic simulations for scenarios including: first, disturbances on the ac side, second, disturbances on the dc-side such as loss of a converter pole including actuator, third, partial loss of feedback signal, and fourth, communication latencies. The performance of the proposed controller is compared against the conventional \mathcal H\infty based design, using a four-terminal dc grid embedded within the New England–New York test system. The results suggest that the proposed approach demonstrates superior performance following dc-side disturbances, actuator outages, and latency. [ABSTRACT FROM PUBLISHER]

The provocative cover of Jeremy Weissman’s debut monograph captures well the new visibility we are all subject to. Reminiscent of the times we live in, is the idea of the “photoborg”. [ABSTRACT FROM AUTHOR]

There are plenty of parking spaces in big cities, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, weekdays, and events. PewLSTM includes a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events. PewLSTM is extremely useful for drivers and parking lot owners to improve customer experience. For example, the probability of parking space that will be available soon can be provided even if the parking lot is full. Based on 910,477 real parking records in 904 days from 13 parking lots, PewLSTM yields 93.84% parking prediction accuracy, which is about 30% higher than the state-of-the-art parking behavior prediction method. Additionally, we have analyzed parking behaviors in events like holidays and COVID-19. PewLSTM can handle parking behavior prediction in events and reaches 90.68 percent accuracy. [ABSTRACT FROM AUTHOR]