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The fluctuating nature of many renewable energy sources (RES) introduces new challenges in power systems. Flywheel Energy Storage Systems (FESS) in general have a longer life span than normal batteries, very fast response time, and they can provide high power for a short period of time. These characteristics make FESS an excellent option for many applications in future power Microgrids (MGs), in particular with integrated RES. The purpose of this paper is twofold. First, a new topology of FESS in MGs is introduced, where the FESS is connected at the same DC-bus of the fuel cells and the Photovoltaic (PV) inverter instead of connecting it with a separate on-grid inverter. Fuel cells are connected at the bus to help the FESS to operate as Uninterruptible Power Supply (UPS) system for longer periods by using the same power electronics components. Not only this topology is cost-effective, but also it allows higher PV penetration levels due to regulating the power flow by the flywheel and it is also more efficient than the traditional topology due to the shortest path of power flow. Second, a detailed simulation model of MGs with FESS is developed. This simulation model makes it possible to explore different scenarios including connected and isolated status of MGs with high levels of PV penetration. The simulation results demonstrate the ability of FESS to withstand changes in the load, PVs and wind, and the ability to provide electricity even when an interruption from the utility grid occurs. Additionally, the common on-grid inverter has been exploited to compensate the reactive power and hence improve the power factor inside the MG.

We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.

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