Electric Grid Modling

The focus of my PhD is energy sciences. For this major I done projects on energy systems modeling. One of my projects focused on modeling automotive emissions with a focus on identifying what technologies can be used to meet 2054 Emissions Goals set by Stephen Pacala and Robert H. Socolow. The second project was on identifying what technologies could be used in conjunction with demand response in order to better integrate renewables into the electric grid.

The Carbon Impact of Different Automotive Propulsion System’s

Week2

The goal of this project was to determine what combination of automotive technologies could be used in order to reduce emissions in order to make a Pacala and Socolow wedge. For this project we created a model that used global fleet predictions and lifetime emissions for all the dominant and predicted vehicle technologies. In addition to this we analyzed emissions from electricity production and future predictions for the makeup of the global electric grid. Using this model we were able to test seven different scenarios and their impact on global emissions. These scenarios ranged from fleet improvements to car sharing. This was a group term project for a class in Advanced Energy Conversion. Click on the plot for the final paper.


Inverse Demand Response for Energy Intensive Industrial Processes

Week2

Demand Response (DR), in its traditional form, is an initiative by the grid suppliers offering compensation to the end-user in exchange for load-reduction at critical times during periods of high electricity prices. Inverse demand response aims to capitalize on periods of low electricity prices in order to run energy intensive processes that may otherwise be too expensive. The project explored the viability of this approach by focusing on a couple of industrial processes – namely air separation and desalination – with a lens over the New England electrical grid. Load and electricity price forecasting is attempted for the near future, using historical prices and various inputs as a precedent in a neural network model. A diverse set of assumptions in the energy and economic fields are created in order to test the model in different scenarios. The cases demonstrated that the viability of such a business would be successful in certain scenarios, but not in all. Furthermore, the benefits to the electrical grid by such an approach are also explored. This was a group term project for a class in Fundamentals of Smart and Resilient Grids. Click on the diagram for the final paper.