Zeljko Pantic

The proposed project will apply risk segmentation, adaptive credit scoring and network-based portfolio analysis techniques from financial engineering and risk management for risk analytics of power systems at both asset and system levels. At the asset level (Thrust 1), the project will introduce risk segmentation of an asset’s throughput by applying tranching similar to collateralized debt obligations. The risk-free to most risky tranches will be assessed for their risk profile in terms of risk scores taking into account the variability of the renewable resource (wind or solar), presence of storage units or services that they may be equipped/associated with, and the asset’s locational specification. This risk scoring will be designed to be adaptive based on system level (Thrust 2) feedback at different contractual time-scales, starting from sub-seconds to tens of minutes, to determine asset suitability as an energy, regulation, spin, non-spin, or replacement reserve. Novel copula-based probabilistic risk models will be developed for the joint correlation structures between different contract tranches of assets for asset and system level risk assessment.

1.7 million Americans rely on Power Mobility Devices (PMDs) – power wheelchairs and electric scooters - to improve their mobility. However, they still travel less than users of manual wheelchairs and much less than people without disability, where sometimes only 2% of that distance occurs outdoors. Users and caregivers consistently report the energy constraints of PMD’s batteries as one of the top reasons for limited away-from-home mobility. A collaborative research team from NCSU (Raleigh) and UNC (Chapel Hill) are partnering with a group of stakeholders to pilot a public charging infrastructure and cyber-information system to support outdoor use of power mobility devices, to improve the mobility and inclusion of their owners. The project objectives are to 1) design, develop, and test a pilot public physical charging network accessible for PMD charging; 2) make the charging stations real-time IoT-connected through Google Maps services; 3) build smart energy monitoring hardware to track the PMD energy consumption and driving parameters, 4) develop a cloud-based, data-driven energy consumption prediction algorithm to enable route planning, 5) write a Best Practice Protocol to alleviate scaling up the charging network, and 6) increase the awareness of the general population regarding the needs of people with disabilities and aging adults. The anticipated project outcomes are: (1) the PMD users will be able to successfully use public charging stations and charging apps; (2) the overall distance traveled by PMD will increase for 10%; (3) the average participation of outdoor miles in totals PMD miles traveled will increase; (4) the life-time of PMD batteries will increase. The project will generate the following products: (1) an operational pilot charging infrastructure installed in Downtown Raleigh, (2) a fully functional charging app for managing the charging process, (3) cloud-located AI-based software capable of estimating PMD energy consumption for a specified route, and (4) Best Practice Protocol instructions for further expansion of charging network.