November 2023

I am excited to welcome to my group at the Digitalized Electrical grid Innovations, Developments & Applications Laboratory (DEgIDAL) up to three PhD students starting in Fall ’24. DEgIDAL operates within the Dept. of Electrical Engineering at the City College of the University of New York. Each student is guaranteed three years of funding at the highest research assistant rate; there are also available Scholarships for 5 years of tuition.

Successful applicants must demonstrate (via transcripts and/or peer-reviewed publications) :
– either strong power systems and electrical machines background (incl. steady & transient states, Park transformation & control, power electronics) with some coding experience (preferably in any type of artificial intelligence applications/projects)
– or strong machine learning and optimization background (incl. Bayesian statistics, cone programming, neural networks, decision trees, SVMs, clustering, genetic algorithms, stochastic programming) with some experience in linear circuits and electricity markets.

The aspiring group members will be involved in research in one or more of the following 3 areas:

• Novel pricing paradigms for hybrid Renewable Energy Sources (hRES): In electricity markets, conventional generators bid their energy and their contribution to grid reliability via prices determined (mostly) by their fuel costs. Renewable energy sources (RES), having no fuel costs, cannot make such bids, while, due to  their volatility, they are typically excluded from active contribution to grid reliability (ancillary services). This situation distorts electricity market operation and limits the operators’ ability to safely manage electrical grid stability and security. However, integrating RES with other resources (thermal & hydro units, batteries, etc.) within hybrid RES (hRES), allows cost aggregation for integrated pricing and internal balancing of the overall volatility. Background in optimization, machine learning and electricity markets is necessary for this subject.

• Digital twins and artificial intelligence control for grid stability with high penetrations of renewables: Conventional generation comprises large rotating inertias that delay  by seconds any instability effects caused by grid disturbances. This allows operators to act in the meantime and avoid collapse or service disruptions. As renewables with small inertias displace conventional generation, instability will propagate faster, thus, requiring identification of the risks and response times that only artificial intelligence and machine learning may enable. Background in automatic control, power electronics and machine learning will be necessary for this subject.

• Equitable and fair quality of service at distribution systems: Most utilities comprise distribution systems of outdated equipment, especially in communities of minorities and low income. Also, states with lower median household income suffer longer yearly average distribution grid disruptions. The failure to roll blackouts across Texas, during storm Uri in 2021, indicates, too, lack of equitable upgrades at the distribution level. With the volatility of renewables scrutinized for additional effects to grid disruptions, it is imperative to rethink distribution grid planning and operation. Background on transient modeling of electric machines/grids and signal processing is necessary for this subject.

For expression of interest and additional information, please, email me at my CCNY address found in the second to last line here, and set the subject line as “interest to join DEgIDAL group – (your last name)”. I apologize in advance for not replying to applicants who meet neither of the 2 background conditions described above.