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UC Berkeley, Fall 2020

Evaluating Alameda County CO2 Emissions and Optimizing Customer Programs Using Marginal Emissions Data

Partner:Kevin Li, East Bay Community Energy, Industry & Government

Overview

Project Description

East Bay Community Energy’s goal is to provide cleaner and cheaper energy to East Bay Cities and customers. The goal of this project is to answer the following questions:

  1. Compare CO2 accounting methods between a locational/market-based approach versus a marginal approach for EBCE electricity load
  2. Optimize electric vehicle (EV) and demand response (DR) schedules to minimize emissions and energy costs
  3. Evaluate whether more accurate energy demand forecasts lower CO2 emissions.

Expected Deliverable

The outputs of this project should answer the above stated questions in the description of the project, with end deliverables consisting of:

  • A presentation outlying the methodology and results to EBCE teams
  • Notebooks of code used to perform analysis and create visualizations.

What would a successful semester look like to you?

A successful semester would consist of identifying the best CO2 accounting methodology, an optimization algorithm model based on the CO2 calculation method that we can reuse to update optimal EV and DR schedules over time, and a method to determine how a more accurate electricity demand forecast affects CO2 emissions. The project will also expose students to statistical and optimization techniques and provide experience working with real-word data using leading-edge technologies. Hopefully, the work developed during this project is good enough to submit for publication in an academic journal.

Additional Skills from ideal candidates

Willingness to read academic papers, able to think critically about problems, experience with power point or similar programs to present results to other teams in the organization.

Data

Models

Conclusion