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Program

Below find the schedule and the overview of projects for the Research Experience for Teachers (RET) Clean Energy program.

Schedule

Period

Task

Jan. 13, 2025 - Feb. 28, 2025

Site advertisement and outreach activities

Jan. 13, 2025 - March 31, 2025

Applications and selection window (application deadline is Feb. 28

May 15, 2025 - May 31, 2025

Sharing reading materials and program importance and expectations

June 1, 2025 - June 16, 2025

Administrative tasks

June 16, 2025

Research orientation, lectures, seminars and forming teams

June 17, 2025 - July 24, 2025

Focused research activities, curriculum development and industry interactions

July 25, 2025

Workshops and poster session

Fall 2025

Academic year follow-up, curriculum implementation and feedback

Overview of projects

Below see the projects in the RET: Clean Energy program.

Degradation of different energy storage materials and release of engineered nanomaterials to the environment

This project studies the degradation of novel energy storage materials (graphene) and their fate and transport in the natural environment. Professor Yang’s group recently developed methods to detect and quantify carbon nanotubes in the natural environment and investigated the plant uptake and environmental transformation of carbonaceous nanomaterials. Based on these recent studies, the environmental fate and plant uptake of graphene will be studied to understand its fate in the soil-plant system.

Solar energy conversion — basics and applications

Solar power is omnipresent, free, sustainable, reliable and the cleanest possible source of everlasting energy. If there were a mechanism to store the sun’s energy, it would open the tantalizing possibility of 24/7 clean energy. Professor Subramanian's group focuses on designing and developing multifunctional materials, characterizing them and applying them to energy conversion and storage. The group studies materials that are earth-abundant, non-toxic and ecofriendly. These materials can be used for solar energy utilization including photovoltaics, solar fuels and environmental remediation.

Seamless integration of RESs and ESDs into the power grid

The deployment of renewable energy sources (RES)s alleviates several concerns related to energy, natural resources and climate change. But the lack of rotational kinetic energy as well as the variability and intermittency of the power generated by RESs are key challenges to the stability, reliability and resilience of power grids.

Energy storage devices (ESD)s hold the potential to smooth the output power of RESs and to compensate for the lack of rotational kinetic energy through virtual inertia. However, due to the irregular output of RESs and the high frequency of charging and discharging during contingencies, conventional stand-alone ESDs have a short lifespan and usually are oversized to reduce the stress level and to meet the intermittent peak power demand.

Hybrid energy storage systems (HESSs) have been introduced to overcome limitations that may exist in individual ESDs and can increase the durability and performance of the system response. The project team has been working to determine optimal sizes and sites and identify appropriate types of HESSs and associated control systems in order to maintain the stability, reliability, and resilience of future power systems.

Stochastic optimal operation of grids with RESs and ESDs

The emergence of energy storage devices (ESD)s in electricity networks enhances the compatibility of renewable energy sources (RES)s, leading to a more reliable and resilient grid. Yet optimal scheduling and operation of grids with ESDs and RESs is still a challenge because of operational and technical constraints and uncertainties that are inherent to RESs’ outputs.

To cope with these paradigm shifts, prominent optimal scheduling with advanced forecasting methods have been proposed. However, a key challenge is that future energy management systems will require uncertainty and risk components to develop stochastic optimal operational frameworks. Additionally, wind and solar generation from large wind and solar farms tend to be strongly correlated in many geographical regions, and forecasting their generation and calculating uncertainty hinges upon accurately characterizing their correlation structure. This has been largely overlooked by existing studies.