2019 Community College Internship (CCI) Program Participants & Projects

Student: Nitya Goyal

Mentor: Alyona Ivanoa

Project Title: Data Driven Grid Modernization

Project Description: With economic growth and electricity demand on the rise, existing grid technologies are challenging the conventional model of reliable clean and efficient electricity for every customer. To address this problem, the Grid Integration Systems and Mobility (GISMo) team is developing approaches to modernizing the grid using data science and machine learning.  These technologies allow for a deep analysis that can uncover insights about the vulnerabilities and opportunities in the current grid system that may not be immediately apparent to utilities.  The California Energy Commission has funded an effort to upgrade a modern grid simulator, called GridLAB-D. The objectives of the project include conversion to high-performance and scalable computing, development of the graphical user-interface for distributed energy resources analysis and addition of a data-exchange platform for power systems analysis.  The intern will analyze and process utility data, assist in the development of the GridLAB-D platform, and develop post-processing and visualization tools for these projects. Skills desired: (1) Proficiency in any of the modern programming languages. C/C++ preferred, Python optional; (2) Prior experience in, or desire to learn, database management, WebHooks, web APIs; (3) Academic coursework or prior experience in related to web based technologies like HTML, CSS, JavaScript; (4) Prior experience in / or desire to learn tools data analysis tools and libraries like Jupyter, Numpy, Pandas, NetworkX and Matplotlib.

 

Student: Kaung Sett Hein

Mentor: Bennett Meyers

Project Title: Image-Based Solar Forecasting System

Project Description:  Photovoltaic (PV) solar power generation is growing exponentially.  PV energy boasts at least a 10-fold reduction in carbon footprint compared to other energy sources, near-zero cost to operate, and an energy payback period of less than 2 years, making it an important component of the 21st century energy mix. Unlike traditional power plants, however, PV power depends on available irradiance, which can be highly variable.  The ability to forecast future irradiance (and therefore PV power output), is crucial for planning and load-balancing operations in a modern grid.  Direct assessment of cloud movement through the processing of 360° fisheye images is a very accurate way to generate high-frequency, short time horizon forecasts of irradiance.  Currently, the GISMo lab does not have access to our own fisheye image capture system for experimenting with short-term solar forecasting. The goal of this project would be to install an end-to-end image capture system and implement methodologies from existing literature to process the images for forecasting.  The Intern will be responsible for installing a fisheye camera at the GISMo lab, including mounting, data connection, and power supply. The data will then be piped into the GISMo team’s AWS cloud storage platform and implement image processing algorithms from established literature. The student will also implement some basic image processing algorithms on the new image data stream. Skills desired: (1) Prior experience with, or desire to learn about, data acquisition system installation and real-world sensing; (2) Proficiency in any of the modern programming languages, Python preferred ; (3) Prior experience in, or desire to learn, database management and AWS; (4) Prior experience in, or desire to learn, data analysis tools and libraries like Jupyter, Numpy, Pandas, and Matplotlib.

 

Student: Nathan Zhou

Mentor: Ray Sierra

Project Title: Structural studies of C-cycling enzymes

Project Description: This project involves purification, crystallization and structure determination of novel and more efficient C-cycling enzymes.  The principal objective of this project is to design and engineer the natural C-cycling pathway to increase the efficiency of Carbon-fixing cycle.