Zeljko Pantic

North Carolina State University (NCSU), in collaboration with Danfoss, New York Power Authority (NYPA), Commonwealth Edicon (ComEd), GoTriangle, and the North Carolina Clean Tech Cluster will research, develop, and demonstrate ultra-low cost, all-SiC modular power converters for DC fast charging equipment connected directly to a Medium Voltage (MV) distribution system. The system will deliver a 1 MW front end, while enabling scalable charging and generation integration, with changing ports serving 100-500kW per stall at voltage of 200-900V. Therefore, the proposed station will be capable of supporting today's electric vehicles (EVs) as well as the next generation of vehicles operating at higher voltages and faster charging rates. The system will feature reconfigurable firmware, allowing the same design to be used as a front end for feeding a DC microgrid or for direct power delivery to the vehicle. The system medium-voltage front end will feature high bandwidth control to actively improve the local power quality, thus providing ancillary services to the grid. The project entails two phases: a cost analysis and system development phase (Phase 1) and a system demonstration phase (Phase 2). The cost analysis task will help set measurable system-level cost targets to provide the lowest cost of ownership system on the market. We will achieve this aim by quantifying the target converter costs and benefits of direct connection to MV (i.e., higher efficiency, provision of ancillary services, lower installation costs, and smaller footprint). In parallel, by implementing the design innovations outlined in this proposal, we will develop the ultra-low cost fast charging equipment, meeting the set target converter costs, and will collect reliability and performance data from the baseline system which will be in operation at a NYPA site in Marcy, New York (NY). In Phase 2, we will deploy the newly-developed system at demonstration sites run by our industry partners. Possible demonstration sites include a proving ground at ComEd in Chicago, IL; a location that is made available by NYPA such as a NY turnpike rest stop in NY, and a GoTriangle bus depot in NC.

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.

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.